Affiliations
Division of Infectious Diseases, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Division of General Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
Email
shahs@email.chop.edu
Given name(s)
Samir S.
Family name
Shah
Degrees
MD, MSCE

1.06 Common Clinical Diagnoses and Conditions: Bone and Joint Infections

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Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Knowledge

Pediatric hospitalists should be able to:

  • Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
  • Explain the pathophysiology of bone and joint infections.
  • Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
  • Explain risk factors for bone and joint infections, including sickle cell disease.
  • Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
  • Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
  • State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
  • Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
  • Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
  • Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
  • Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
  • Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
  • Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
  • Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Skills

Pediatric hospitalists should be able to:

  • Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
  • Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
  • Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
  • Manage pain effectively for children with bone and joint infections.
  • Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
  • Access and arrange for pediatric home care services as appropriate.
  • Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Attitudes

Pediatric hospitalists should be able to:

  • Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
  • Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

Systems Organization and Improvement

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

  • Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
  • Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
  • Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
  • Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.
References

1. Keren R, Shah SS, Srivastava R, et al. Comparative effectiveness of intravenous vs. oral antibiotics for postdischarge treatment of acute osteomyelitis in children. JAMA Pediatr. 2015;169(2):120-128. https://doi.org/10.1001/jamapediatrics.2014.2822.

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Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Knowledge

Pediatric hospitalists should be able to:

  • Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
  • Explain the pathophysiology of bone and joint infections.
  • Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
  • Explain risk factors for bone and joint infections, including sickle cell disease.
  • Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
  • Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
  • State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
  • Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
  • Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
  • Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
  • Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
  • Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
  • Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
  • Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Skills

Pediatric hospitalists should be able to:

  • Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
  • Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
  • Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
  • Manage pain effectively for children with bone and joint infections.
  • Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
  • Access and arrange for pediatric home care services as appropriate.
  • Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Attitudes

Pediatric hospitalists should be able to:

  • Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
  • Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

Systems Organization and Improvement

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

  • Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
  • Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
  • Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
  • Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.

Introduction

Osteomyelitis is a pyogenic infection of the bone or periosteum, whereas septic arthritis is an infection of the joint space. In children, these most commonly result from hematogenous spread. Osteomyelitis may also result from extension of contiguous skin or muscle infection. Septic arthritis may also result from either contiguous bone infection or direct inoculation of bacteria into the joint from penetrating trauma, intra-articular injection, or other causes. Either site of infection may represent a medical emergency in children. Bone and joint infections are commonly caused by Staphylococcus aureus, Streptococcus species, Kingella kingae, and Salmonella species. They most commonly occur in children <5 years of age. Males are nearly twice as likely to be affected as females. Prompt recognition and appropriate treatment are essential to reduce the risk of significant complications, including permanent bone or cartilage destruction with life-long disability. Pediatric hospitalists should render evidence-based care that minimizes harm, improves outcomes, and avoids use of unnecessary procedures and treatments.

Knowledge

Pediatric hospitalists should be able to:

  • Discuss the differential diagnosis of common presenting signs and symptoms of bone and joint infections, including swollen joint, limp, and pain or limited movement of the affected bone or joint.
  • Explain the pathophysiology of bone and joint infections.
  • Compare and contrast the different clinical presentations of bone and joint infections between children of varying chronological ages.
  • Explain risk factors for bone and joint infections, including sickle cell disease.
  • Identify indications for admission to the hospital and goals for therapy while hospitalized for children with suspected osteomyelitis and septic arthritis.
  • Classify the most likely pathogens based on age, underlying risk factors, and exposures and list appropriate antimicrobial agents for each.
  • State relative local antimicrobial resistance rates for the most common organisms and explain the importance of these in prescribing therapy.
  • Describe the relative advantages, disadvantages, and local availability of commonly used laboratory tests (such as C-reactive protein, blood cultures, bone aspirate, and others) and radiologic modalities (such as plain film, computed tomography, bone scan, magnetic resonance imaging, and others) in the evaluation of bone and joint infections.
  • Identify risk factors for poor outcomes, including leg length discrepancy or chronic infection.
  • Describe a comprehensive approach to pain management in children with bone and joint infections, including the roles of child life, occupational therapy, the pain service, and others according to local context.
  • Discuss the relative advantages and disadvantages of intravenous versus oral antibiotic administration at discharge and identify the rare circumstances in which intravenous antibiotic therapy may be preferred.
  • Define the role of the orthopedic surgeon and infectious diseases subspecialists in consultation, co-management, and follow-up care.
  • Discuss criteria for patient transfer to a referral center in cases requiring pediatric-specific services not available at the local facility.
  • Describe criteria, including specific measures of clinical improvement, antimicrobial treatment plan, and post-discharge management plan, which must be met before discharging patients with bone and joint infections.

Skills

Pediatric hospitalists should be able to:

  • Diagnose osteomyelitis or septic arthritis by efficiently performing an accurate history and physical examination.
  • Develop a cost-effective approach to diagnostic evaluation for children suspected of having a bone or joint infection, including laboratory and radiographic testing.
  • Interpret laboratory and radiographic studies commonly ordered to assess for bone and joint infections.
  • Manage pain effectively for children with bone and joint infections.
  • Engage consultants (such as orthopedic surgeons, infectious disease specialists, physical therapists, and others) in a timely and effective manner when indicated.
  • Access and arrange for pediatric home care services as appropriate.
  • Coordinate care with subspecialists and the primary care provider and arrange an appropriate transition plan for hospital discharge.

Attitudes

Pediatric hospitalists should be able to:

  • Acknowledge the need for effective communication with patients, the family/caregivers, and healthcare providers regarding findings and care plans.
  • Collaborate with subspecialists and the primary care provider to ensure coordinated, longitudinal care for children with bone and joint infections.

Systems Organization and Improvement

In order to improve efficiency and quality within their organizations, pediatric hospitalists should:

  • Lead, coordinate, or participate in the development and implementation of cost-effective, safe, evidence-based care pathways to standardize the evaluation and management for hospitalized children with bone and joint infections.
  • Work with hospital administration to build a multidisciplinary team that can provide high value care to children with bone and joint infections, including nursing, social work, physical therapy, pharmacy, and care coordinators.
  • Assist in creating systems to evaluate and improve pain management for children hospitalized with bone and joint infections.
  • Lead, coordinate, or participate in efforts to increase pediatric-specific community health care resources that allow for an efficient transition to outpatient therapy and management after inpatient goals are achieved.
References

1. Keren R, Shah SS, Srivastava R, et al. Comparative effectiveness of intravenous vs. oral antibiotics for postdischarge treatment of acute osteomyelitis in children. JAMA Pediatr. 2015;169(2):120-128. https://doi.org/10.1001/jamapediatrics.2014.2822.

References

1. Keren R, Shah SS, Srivastava R, et al. Comparative effectiveness of intravenous vs. oral antibiotics for postdischarge treatment of acute osteomyelitis in children. JAMA Pediatr. 2015;169(2):120-128. https://doi.org/10.1001/jamapediatrics.2014.2822.

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How JHM is improving the author experience

 

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

Dr. Samir S. Shah of Cincinnati Children's Hospital
Dr. Samir S. Shah

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

  • 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
  • 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

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How JHM is improving the author experience

How JHM is improving the author experience

 

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

Dr. Samir S. Shah of Cincinnati Children's Hospital
Dr. Samir S. Shah

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

  • 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
  • 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

 

“No hassle” new manuscript submission process

Many authors have experienced the frustration of formatting a manuscript for submission to a medical journal. The process is time consuming and each journal has different requirements. This means that if you decide to submit your manuscript to one journal and later decide that another journal is a better fit, you may spend an hour (or several hours) reformatting to meet the new journal’s unique requirements.

Dr. Samir S. Shah of Cincinnati Children's Hospital
Dr. Samir S. Shah

To allow authors to spend more time on what matters to them, we’re pleased to introduce our “No Hassle” process for initial original research and brief report manuscript submissions to the Journal of Hospital Medicine. Our goal is to eliminate unnecessary and burdensome steps in the manuscript submission process. Thus, we have relaxed formatting requirements for initial manuscript submissions. Any conventional and readable manuscript format and reference style is acceptable.

Tables and figures can be embedded in the main document file or uploaded individually, depending on your preference. Funding and disclosures should be included on the title page but there is no need to submit completed disclosure or copyright forms unless we request a manuscript revision.
 

Timely decisions

We have all experienced the agony of waiting months on end for a journal to make a decision about our manuscript. The review process itself can take many months (or even longer). Furthermore, a manuscript may not be published for many more months (or even longer) following acceptance. At the Journal of Hospital Medicine, we commit to making timely decisions and publishing your accepted manuscript as fast as we can.

We currently reject approximately half of all original research and brief report manuscript submissions without formal peer review. We do this for two reasons. First, we want to ensure that we’re not overburdening our peer reviewers so we only ask them to review manuscripts that we are seriously considering for publication. Second, we want to ensure that we’re being respectful of our authors’ time. If we are unlikely to publish a manuscript based on lower priority scores assigned by me, as editor-in-chief, or other journal editors, we don’t want to subject your manuscript to a lengthy peer review, but would rather return the manuscript to you quickly for timely submission elsewhere.

Here are data that support our timely decision making:

  • 1.3 days = our average time from manuscript submission to rejection without formal peer review (median, less than one day).
  • 23 days = our average time from manuscript submission to first decision for manuscripts sent for peer review.

We also are working to improve our time to publication. Our goal is to publish accepted manuscripts within 120 days from initial submission to publication, and within 60 days from acceptance to publication.
 

Dissemination

Finally, little public knowledge is gleaned from medical research unless the study is published and widely read. The Journal of Hospital Medicine is at the leading edge of helping authors disseminate their work to a broader audience. Of course, we produce press releases and distribute those to many media outlets in partnership with the Society of Hospital Medicine. We also leverage social media to promote your article through tweets, visual abstracts, and, more recently, comics or graphic medicine abstracts. Some articles are even discussed on #JHMChat, our twitter-based journal club. This work is led by our exceptional Digital Media Editors, Dr. Vineet Arora (@FutureDocs), Dr. Charlie Wray (@WrayCharles), and Dr. Grace Farris (@gracefarris).

In summary, we are committed to making the Journal of Hospital Medicine even more author friendly. To that end, we’re making it easy for authors to submit their work, making timely disposition decisions, and facilitating dissemination of the work we publish.
 

Dr. Shah is chief metrics officer and director of the division of hospital medicine at Cincinnati Children’s Hospital Medical Center. He is the current editor-in-chief of the Journal of Hospital Medicine.

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Association of Weekend Admission and Weekend Discharge with Length of Stay and 30-Day Readmission in Children’s Hospitals

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Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.1-8 However, the relatively low rate of pediatric readmissions,9 coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,32 the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

METHODS

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.9 With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

 

 

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.33 Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),34 technology dependence,34 number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

RESULTS

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

 

 

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

 

 

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

DISCUSSION

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.22 This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.22 The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,22 our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.32 As with prior studies22, we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

 

 

CONCLUSION

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

 

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References

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9. Berry JG, Toomey SL, Zaslavsky AM, et al. Pediatric readmission prevalence and variability across hospitals. JAMA. 2013;309(4):372-380. doi:10.1001/jama.2012.188351 PubMed
10. Bardach NS, Vittinghoff E, Asteria-Peñaloza R, et al. Measuring hospital quality using pediatric readmission and revisit rates. Pediatrics. 2013;132(3):429-436. doi:10.1542/peds.2012-3527 PubMed
11. Berry JG, Blaine K, Rogers J, et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955-962; quiz 965-966. doi:10.1001/jamapediatrics.2014.891 PubMed
12. Auger KA, Simon TD, Cooperberg D, et al. Summary of STARNet: seamless transitions and (Re)admissions network. Pediatrics. 2015;135(1):164. doi:10.1542/peds.2014-1887 PubMed
13. Freemantle N, Ray D, McNulty D, et al. Increased mortality associated with weekend hospital admission: a case for expanded seven day services? BMJ. 2015;351:h4596. doi:10.1136/bmj.h4596 PubMed
14. Schilling PL, Campbell DA, Englesbe MJ, Davis MM. A comparison of in-hospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza. Med Care. 2010;48(3):224-232. doi:10.1097/MLR.0b013e3181c162c0 PubMed
15. Cram P, Hillis SL, Barnett M, Rosenthal GE. Effects of weekend admission and hospital teaching status on in-hospital mortality. Am J Med. 2004;117(3):151-157. doi:10.1016/j.amjmed.2004.02.035 PubMed
16. Zapf MAC, Kothari AN, Markossian T, et al. The “weekend effect” in urgent general operative procedures. Surgery. 2015;158(2):508-514. doi:10.1016/j.surg.2015.02.024 PubMed
17. Freemantle N, Richardson M, Wood J, et al. Weekend hospitalization and additional risk of death: an analysis of inpatient data. J R Soc Med. 2012;105(2):74-84. doi:10.1258/jrsm.2012.120009 PubMed
18. Bell CM, Redelmeier DA. Mortality among patients admitted to hospitals on weekends as compared with weekdays. N Engl J Med. 2001;345(9):663-668. doi:10.1056/NEJMsa003376 PubMed
19. Coiera E, Wang Y, Magrabi F, Concha OP, Gallego B, Runciman W. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks. BMC Health Serv Res. 2014;14:226. doi:10.1186/1472-6963-14-226 PubMed
20. Powell ES, Khare RK, Courtney DM, Feinglass J. The weekend effect for patients with sepsis presenting to the emergency department. J Emerg Med. 2013;45(5):641-648. doi:10.1016/j.jemermed.2013.04.042 PubMed
21. Ananthakrishnan AN, McGinley EL, Saeian K. Outcomes of weekend admissions for upper gastrointestinal hemorrhage: a nationwide analysis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2009;7(3):296-302e1. doi:10.1016/j.cgh.2008.08.013 PubMed
22. Auger KA, Davis MM. Pediatric weekend admission and increased unplanned readmission rates. J Hosp Med. 2015;10(11):743-745. doi:10.1002/jhm.2426 PubMed
23. Goldstein SD, Papandria DJ, Aboagye J, et al. The “weekend effect” in pediatric surgery - increased mortality for children undergoing urgent surgery during the weekend. J Pediatr Surg. 2014;49(7):1087-1091. doi:10.1016/j.jpedsurg.2014.01.001 PubMed
24. Adil MM, Vidal G, Beslow LA. Weekend effect in children with stroke in the nationwide inpatient sample. Stroke. 2016;47(6):1436-1443. doi:10.1161/STROKEAHA.116.013453 PubMed
25. McCrory MC, Spaeder MC, Gower EW, et al. Time of admission to the PICU and mortality. Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2017;18(10):915-923. doi:10.1097/PCC.0000000000001268 PubMed
26. Mangold WD. Neonatal mortality by the day of the week in the 1974-75 Arkansas live birth cohort. Am J Public Health. 1981;71(6):601-605. PubMed
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28. McShane P, Draper ES, McKinney PA, McFadzean J, Parslow RC, Paediatric intensive care audit network (PICANet). Effects of out-of-hours and winter admissions and number of patients per unit on mortality in pediatric intensive care. J Pediatr. 2013;163(4):1039-1044.e5. doi:10.1016/j.jpeds.2013.03.061 PubMed
29. Hixson ED, Davis S, Morris S, Harrison AM. Do weekends or evenings matter in a pediatric intensive care unit? Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2005;6(5):523-530. PubMed
30. Gonzalez KW, Dalton BGA, Weaver KL, Sherman AK, St Peter SD, Snyder CL. Effect of timing of cannulation on outcome for pediatric extracorporeal life support. Pediatr Surg Int. 2016;32(7):665-669. doi:10.1007/s00383-016-3901-6 PubMed
31. Desai V, Gonda D, Ryan SL, et al. The effect of weekend and after-hours surgery on morbidity and mortality rates in pediatric neurosurgery patients. J Neurosurg Pediatr. 2015;16(6):726-731. doi:10.3171/2015.6.PEDS15184 PubMed
32. Berry JG, Hall DE, Kuo DZ, et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. JAMA. 2011;305(7):682-690. doi:10.1001/jama.2011.122 PubMed
33. Hoshijima H, Takeuchi R, Mihara T, et al. Weekend versus weekday admission and short-term mortality: A meta-analysis of 88 cohort studies including 56,934,649 participants. Medicine (Baltimore). 2017;96(17):e6685. doi:10.1097/MD.0000000000006685 PubMed
34. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199. doi:10.1186/1471-2431-14-199 PubMed
35. Li L, Rothwell PM, Oxford Vascular Study. Biases in detection of apparent “weekend effect” on outcome with administrative coding data: population based study of stroke. BMJ. 2016;353:i2648. doi: https://doi.org/10.1136/bmj.i2648 PubMed
36. Bray BD, Cloud GC, James MA, et al. Weekly variation in health-care quality by day and time of admission: a nationwide, registry-based, prospective cohort study of acute stroke care. The Lancet. 2016;388(10040):170-177. doi:10.1016/S0140-6736(16)30443-3 PubMed
37. Ko SQ, Strom JB, Shen C, Yeh RW. Mortality, Length of Stay, and Cost of Weekend Admissions. J Hosp Med. 2018. doi:10.12788/jhm.2906 PubMed
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Related Articles

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.1-8 However, the relatively low rate of pediatric readmissions,9 coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,32 the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

METHODS

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.9 With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

 

 

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.33 Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),34 technology dependence,34 number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

RESULTS

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

 

 

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

 

 

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

DISCUSSION

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.22 This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.22 The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,22 our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.32 As with prior studies22, we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

 

 

CONCLUSION

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

 

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.1-8 However, the relatively low rate of pediatric readmissions,9 coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,32 the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

METHODS

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.9 With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

 

 

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.33 Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),34 technology dependence,34 number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

RESULTS

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

 

 

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

 

 

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

DISCUSSION

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.22 This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.22 The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,22 our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.32 As with prior studies22, we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

 

 

CONCLUSION

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

 

References

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10. Bardach NS, Vittinghoff E, Asteria-Peñaloza R, et al. Measuring hospital quality using pediatric readmission and revisit rates. Pediatrics. 2013;132(3):429-436. doi:10.1542/peds.2012-3527 PubMed
11. Berry JG, Blaine K, Rogers J, et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955-962; quiz 965-966. doi:10.1001/jamapediatrics.2014.891 PubMed
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14. Schilling PL, Campbell DA, Englesbe MJ, Davis MM. A comparison of in-hospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza. Med Care. 2010;48(3):224-232. doi:10.1097/MLR.0b013e3181c162c0 PubMed
15. Cram P, Hillis SL, Barnett M, Rosenthal GE. Effects of weekend admission and hospital teaching status on in-hospital mortality. Am J Med. 2004;117(3):151-157. doi:10.1016/j.amjmed.2004.02.035 PubMed
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References

1. Crossing the Quality Chasm: The IOM Health Care Quality Initiative : Health and Medicine Division. http://www.nationalacademies.org/hmd/Global/News%20Announcements/Crossing-the-Quality-Chasm-The-IOM-Health-Care-Quality-Initiative.aspx. Accessed November 20, 2017.
2. Institute for Healthcare Improvement: IHI Home Page. http://www.ihi.org:80/Pages/default.aspx. Accessed November 20, 2017.
3. Berry JG, Zaslavsky AM, Toomey SL, et al. Recognizing differences in hospital quality performance for pediatric inpatient care. Pediatrics. 2015;136(2):251-262. doi:10.1542/peds.2014-3131
4. NQF: All-Cause Admissions and Readmissions Measures - Final Report. http://www.qualityforum.org/Publications/2015/04/All-Cause_Admissions_and_Readmissions_Measures_-_Final_Report.aspx. Accessed March 24, 2018.
5. Hospital Inpatient Potentially Preventable Readmissions Information and Reports. https://www.illinois.gov/hfs/MedicalProviders/hospitals/PPRReports/Pages/default.aspx. Accessed November 6, 2016.
6. Potentially Preventable Readmissions in Texas Medicaid and CHIP Programs - Fiscal Year 2013 | Texas Health and Human Services. https://hhs.texas.gov/reports/2016/08/potentially-preventable-readmissions-texas-medicaid-and-chip-programs-fiscal-year. Accessed November 6, 2016.
7. Statewide Planning and Research Cooperative System. http://www.health.ny.gov/statistics/sparcs/sb/. Accessed November 6, 2016.
8. HCA Implements Potentially Preventable Readmission (PPR) Adjustments. Wash State Hosp Assoc. http://www.wsha.org/articles/hca-implements-potentially-preventable-readmission-ppr-adjustments/. Accessed November 8, 2016.
9. Berry JG, Toomey SL, Zaslavsky AM, et al. Pediatric readmission prevalence and variability across hospitals. JAMA. 2013;309(4):372-380. doi:10.1001/jama.2012.188351 PubMed
10. Bardach NS, Vittinghoff E, Asteria-Peñaloza R, et al. Measuring hospital quality using pediatric readmission and revisit rates. Pediatrics. 2013;132(3):429-436. doi:10.1542/peds.2012-3527 PubMed
11. Berry JG, Blaine K, Rogers J, et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955-962; quiz 965-966. doi:10.1001/jamapediatrics.2014.891 PubMed
12. Auger KA, Simon TD, Cooperberg D, et al. Summary of STARNet: seamless transitions and (Re)admissions network. Pediatrics. 2015;135(1):164. doi:10.1542/peds.2014-1887 PubMed
13. Freemantle N, Ray D, McNulty D, et al. Increased mortality associated with weekend hospital admission: a case for expanded seven day services? BMJ. 2015;351:h4596. doi:10.1136/bmj.h4596 PubMed
14. Schilling PL, Campbell DA, Englesbe MJ, Davis MM. A comparison of in-hospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza. Med Care. 2010;48(3):224-232. doi:10.1097/MLR.0b013e3181c162c0 PubMed
15. Cram P, Hillis SL, Barnett M, Rosenthal GE. Effects of weekend admission and hospital teaching status on in-hospital mortality. Am J Med. 2004;117(3):151-157. doi:10.1016/j.amjmed.2004.02.035 PubMed
16. Zapf MAC, Kothari AN, Markossian T, et al. The “weekend effect” in urgent general operative procedures. Surgery. 2015;158(2):508-514. doi:10.1016/j.surg.2015.02.024 PubMed
17. Freemantle N, Richardson M, Wood J, et al. Weekend hospitalization and additional risk of death: an analysis of inpatient data. J R Soc Med. 2012;105(2):74-84. doi:10.1258/jrsm.2012.120009 PubMed
18. Bell CM, Redelmeier DA. Mortality among patients admitted to hospitals on weekends as compared with weekdays. N Engl J Med. 2001;345(9):663-668. doi:10.1056/NEJMsa003376 PubMed
19. Coiera E, Wang Y, Magrabi F, Concha OP, Gallego B, Runciman W. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks. BMC Health Serv Res. 2014;14:226. doi:10.1186/1472-6963-14-226 PubMed
20. Powell ES, Khare RK, Courtney DM, Feinglass J. The weekend effect for patients with sepsis presenting to the emergency department. J Emerg Med. 2013;45(5):641-648. doi:10.1016/j.jemermed.2013.04.042 PubMed
21. Ananthakrishnan AN, McGinley EL, Saeian K. Outcomes of weekend admissions for upper gastrointestinal hemorrhage: a nationwide analysis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2009;7(3):296-302e1. doi:10.1016/j.cgh.2008.08.013 PubMed
22. Auger KA, Davis MM. Pediatric weekend admission and increased unplanned readmission rates. J Hosp Med. 2015;10(11):743-745. doi:10.1002/jhm.2426 PubMed
23. Goldstein SD, Papandria DJ, Aboagye J, et al. The “weekend effect” in pediatric surgery - increased mortality for children undergoing urgent surgery during the weekend. J Pediatr Surg. 2014;49(7):1087-1091. doi:10.1016/j.jpedsurg.2014.01.001 PubMed
24. Adil MM, Vidal G, Beslow LA. Weekend effect in children with stroke in the nationwide inpatient sample. Stroke. 2016;47(6):1436-1443. doi:10.1161/STROKEAHA.116.013453 PubMed
25. McCrory MC, Spaeder MC, Gower EW, et al. Time of admission to the PICU and mortality. Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2017;18(10):915-923. doi:10.1097/PCC.0000000000001268 PubMed
26. Mangold WD. Neonatal mortality by the day of the week in the 1974-75 Arkansas live birth cohort. Am J Public Health. 1981;71(6):601-605. PubMed
27. MacFarlane A. Variations in number of births and perinatal mortality by day of week in England and Wales. Br Med J. 1978;2(6153):1670-1673. PubMed
28. McShane P, Draper ES, McKinney PA, McFadzean J, Parslow RC, Paediatric intensive care audit network (PICANet). Effects of out-of-hours and winter admissions and number of patients per unit on mortality in pediatric intensive care. J Pediatr. 2013;163(4):1039-1044.e5. doi:10.1016/j.jpeds.2013.03.061 PubMed
29. Hixson ED, Davis S, Morris S, Harrison AM. Do weekends or evenings matter in a pediatric intensive care unit? Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2005;6(5):523-530. PubMed
30. Gonzalez KW, Dalton BGA, Weaver KL, Sherman AK, St Peter SD, Snyder CL. Effect of timing of cannulation on outcome for pediatric extracorporeal life support. Pediatr Surg Int. 2016;32(7):665-669. doi:10.1007/s00383-016-3901-6 PubMed
31. Desai V, Gonda D, Ryan SL, et al. The effect of weekend and after-hours surgery on morbidity and mortality rates in pediatric neurosurgery patients. J Neurosurg Pediatr. 2015;16(6):726-731. doi:10.3171/2015.6.PEDS15184 PubMed
32. Berry JG, Hall DE, Kuo DZ, et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. JAMA. 2011;305(7):682-690. doi:10.1001/jama.2011.122 PubMed
33. Hoshijima H, Takeuchi R, Mihara T, et al. Weekend versus weekday admission and short-term mortality: A meta-analysis of 88 cohort studies including 56,934,649 participants. Medicine (Baltimore). 2017;96(17):e6685. doi:10.1097/MD.0000000000006685 PubMed
34. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199. doi:10.1186/1471-2431-14-199 PubMed
35. Li L, Rothwell PM, Oxford Vascular Study. Biases in detection of apparent “weekend effect” on outcome with administrative coding data: population based study of stroke. BMJ. 2016;353:i2648. doi: https://doi.org/10.1136/bmj.i2648 PubMed
36. Bray BD, Cloud GC, James MA, et al. Weekly variation in health-care quality by day and time of admission: a nationwide, registry-based, prospective cohort study of acute stroke care. The Lancet. 2016;388(10040):170-177. doi:10.1016/S0140-6736(16)30443-3 PubMed
37. Ko SQ, Strom JB, Shen C, Yeh RW. Mortality, Length of Stay, and Cost of Weekend Admissions. J Hosp Med. 2018. doi:10.12788/jhm.2906 PubMed
38. Tubbs-Cooley HL, Cimiotti JP, Silber JH, Sloane DM, Aiken LH. An observational study of nurse staffing ratios and hospital readmission among children admitted for common conditions. BMJ Qual Saf. 2013;22(9):735-742. doi:10.1136/bmjqs-2012-001610 PubMed
39. Ong M, Bostrom A, Vidyarthi A, McCulloch C, Auerbach A. House staff team workload and organization effects on patient outcomes in an academic general internal medicine inpatient service. Arch Intern Med. 2007;167(1):47-52. doi:10.1001/archinte.167.1.47 PubMed

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Journal of Hospital Medicine 14(2)
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Jessica L. Markham, MD, MSc, Division of Pediatric Hospital Medicine, Children’s Mercy Kansas City, 2401 Gillham Road, Kansas City, MO 64108; Telephone: 816-302-1493, Fax: 816-302-9729; E-mail: jlmarkham@cmh.edu
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The Journal of Hospital Medicine in 2019 and Beyond

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With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.1 We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.2 A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students3 and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.4 This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

 

 

Disclosures

The author has no financial conflicts of interest to disclose

 

References

1. Wray CM, Auerbach AD, Arora VM. The adoption of an online journal club to improve research dissemination and social media engagement among hospitalists. J Hosp Med 2018;13:764-769. PubMed
2. Wray CM, Olson A, Shah SS, Auerbach AD. Announcing the Journal of Hospital Medicine editorial fellowship. J Hosp Med 2019;14: 8.PubMed
3. American Association of Medical Colleges. Applicants and matriculants data. 2018. https://www.aamc.org/data/facts/applicantmatriculant/. Accessed December 15, 2018.
4. American Association of Medical Colleges. U.S. medical school faculty, 2017. https://www.aamc.org/data/facultyroster/reports/486050/usmsf17.html. Accessed December 15, 2018.

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Journal of Hospital Medicine 14(1)
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7
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Article PDF
Related Articles

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.1 We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.2 A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students3 and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.4 This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

 

 

Disclosures

The author has no financial conflicts of interest to disclose

 

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.1 We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.2 A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students3 and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.4 This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

 

 

Disclosures

The author has no financial conflicts of interest to disclose

 

References

1. Wray CM, Auerbach AD, Arora VM. The adoption of an online journal club to improve research dissemination and social media engagement among hospitalists. J Hosp Med 2018;13:764-769. PubMed
2. Wray CM, Olson A, Shah SS, Auerbach AD. Announcing the Journal of Hospital Medicine editorial fellowship. J Hosp Med 2019;14: 8.PubMed
3. American Association of Medical Colleges. Applicants and matriculants data. 2018. https://www.aamc.org/data/facts/applicantmatriculant/. Accessed December 15, 2018.
4. American Association of Medical Colleges. U.S. medical school faculty, 2017. https://www.aamc.org/data/facultyroster/reports/486050/usmsf17.html. Accessed December 15, 2018.

References

1. Wray CM, Auerbach AD, Arora VM. The adoption of an online journal club to improve research dissemination and social media engagement among hospitalists. J Hosp Med 2018;13:764-769. PubMed
2. Wray CM, Olson A, Shah SS, Auerbach AD. Announcing the Journal of Hospital Medicine editorial fellowship. J Hosp Med 2019;14: 8.PubMed
3. American Association of Medical Colleges. Applicants and matriculants data. 2018. https://www.aamc.org/data/facts/applicantmatriculant/. Accessed December 15, 2018.
4. American Association of Medical Colleges. U.S. medical school faculty, 2017. https://www.aamc.org/data/facultyroster/reports/486050/usmsf17.html. Accessed December 15, 2018.

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Journal of Hospital Medicine 14(1)
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© 2019 Society of Hospital Medicine

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Dr. Samir S Shah; E-mail: Samir.Shah@cchmc.org; Telephone: 513-636-6222; Twitter: @SamirShahMD
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Announcing the Journal of Hospital Medicine Editorial Fellowship

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The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,1 the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.2 Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

 

 

Disclosures

The authors have nothing to disclose.

 

Files
References

1. Lovejoy TI, Revenson TA, France CR. Reviewing manuscripts for peer-review journals: a primer for novice and seasoned reviewers. Ann Behav Med Publ Soc Behav Med. 2011;42(1):1-13. doi:10.1007/s12160-011-9269-x PubMed
2. Wray CM, Arora VM, Auerbach AD. The Adoption of an Online Journal Club to Improve Research Dissemination and Social Media Engagement Among Hospitalists. J Hosp Med. 2018;13(11). doi:10.12788/jhm.2987 PubMed

Article PDF
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Journal of Hospital Medicine 14(1)
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8
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The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,1 the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.2 Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

 

 

Disclosures

The authors have nothing to disclose.

 

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,1 the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.2 Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

 

 

Disclosures

The authors have nothing to disclose.

 

References

1. Lovejoy TI, Revenson TA, France CR. Reviewing manuscripts for peer-review journals: a primer for novice and seasoned reviewers. Ann Behav Med Publ Soc Behav Med. 2011;42(1):1-13. doi:10.1007/s12160-011-9269-x PubMed
2. Wray CM, Arora VM, Auerbach AD. The Adoption of an Online Journal Club to Improve Research Dissemination and Social Media Engagement Among Hospitalists. J Hosp Med. 2018;13(11). doi:10.12788/jhm.2987 PubMed

References

1. Lovejoy TI, Revenson TA, France CR. Reviewing manuscripts for peer-review journals: a primer for novice and seasoned reviewers. Ann Behav Med Publ Soc Behav Med. 2011;42(1):1-13. doi:10.1007/s12160-011-9269-x PubMed
2. Wray CM, Arora VM, Auerbach AD. The Adoption of an Online Journal Club to Improve Research Dissemination and Social Media Engagement Among Hospitalists. J Hosp Med. 2018;13(11). doi:10.12788/jhm.2987 PubMed

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Caregiver Perspectives on Communication During Hospitalization at an Academic Pediatric Institution: A Qualitative Study

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Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.1 The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.2 Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,3,15,16 there remains much work to be done.1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study17 by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

METHODS

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.17 This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.18

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.19 On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

 

 

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).26 This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,17 developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation27 was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.27 Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.28 As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.27,28 Coded data were maintained in an electronic database accessible only to study personnel.

RESULTS

Participants

Sixty-one caregivers of children discharged from CCHMC participated. Participants were 87% female and 46% non-white; 42.5% had a 2-year college level of education or greater, and 56% resided in census tracts with ≥15% of residents living in poverty (Table 1). Participant characteristics aligned closely with the demographics of families of children hospitalized at CCHMC.

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

 

 

Experiences during the hospitalization contributed to caregivers’ perceptions of their communication with their child’s inpatient medical team. There were 5 related subthemes identified. The following 2 subthemes were characterized as positive experiences: (1) feeling like part of the team and (2) nurses as interpreters and navigators. The following 3 subthemes were characterized as negative: (1) feeling left out of the loop, (2) insufficient face time with physicians, and (3) the use of medical jargon (Table 2). More specifically, participants described feeling more satisfied with their care and the inpatient experience when they felt included and when their input and expertise as a caregiver was valued. They also appreciated how nurses often took the time after FCR or interactions with the medical team to explain and clarify information that was discussed with the patient and their caregiver. For example, 1 participant stated, “Whenever I ask about anything, I just ask the nurse. And if she didn’t know, she would find out for me…”

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

At a large teaching institution with various trainees and multiple subspecialties, communication challenges were particularly prominent. Three subthemes were related to this theme: (1) confusing messages with a large multidisciplinary team, (2) perceptions of FCR, and (3) role confusion, or who’s in charge of the team? (Table 3). Participants described confusing and inconsistent messages arising from the involvement of many medical providers. One stated, “When [the providers] all talk it seems like it don’t make sense because [what] one [is] saying is slightly different [from] the other one…and then you’d be like, ‘Wait, what?’ So it kind of confuses you…” Similarly, the use of FCR was overwhelming for the majority of participants who cited difficulty tracking conversations, feeling “lost” in the crowd of team members, or feeling excluded from the conversation about their child. One participant stated, “But because so many people came in, it can get overwhelming. They come in big groups, like 10 at once.” In contrast, some participants had a more favorable view of FCR: “What really blew me away was I came out of the restroom and there is 10 doctors standing around and they very well observed my child. And not only one doctor, but every one of them knew was going on with my kid. It kind of blew me away.” Participants felt it was not always clear who was in charge of the medical team. Trying to remember the various roles of all of the team members contributed to this confusion and made asking questions difficult. One participant shared, “I just want the main people…the boss to come in, check the baby out. I don’t need all the extra people running around me, keep asking me the same thing on that topic. Send in the main group, the bosses, they know what the problem is and how to fix it.”

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Caregivers have a unique vantage point as they witness many interactions between medical providers during their child’s hospitalization. Still, they do not generally witness all the interactions between inpatient providers or between inpatient and outpatient providers. This led to variable perceptions of this communication. Specifically, the 2 subthemes described here were (1) communication between inpatient medical providers and (2) communication between inpatient and outpatient providers (Table 4). Caregivers assessed how well (or how poorly) medical providers communicated with each other based upon the consistency of messages they received or interactions they personally experienced or observed. One participant described how the medical team did not appear to be in consensus about when to discharge her child, highlighting the perception that team members did not have a shared understanding of the child’s needs: “One of the doctors was…nervous about sending him home. It was just one doctor…the other doctors on her team and everything and the nurses, they were like ‘He’s fine.’” Others shared concerns related to inadequate handoff and messages not getting passed along shift-to-shift.

 

 

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

DISCUSSION

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.2 During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.35 Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.36 found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.29 Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,8-10,12 but few have looked at it from the perspective of the caregiver.13 After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

 

 

CONCLUSION

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

ACKNOWLEDGMENTS

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

References

1. Riesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and Attending Physicians’ Handoffs: A Systematic Review of the Literature. Acad Med. 2009;84(12):1775-1787. PubMed
2. The Joint Commission releases improving America’s hospitals: a report on quality and safety. JT Comm Perspect. 2007;27(5):1, 3. PubMed
3. Nobile C, Drotar D. Research on the quality of parent-provider communication in pediatric care: Implications and recommendations. J Dev Behav Pediatr. 2003;24(4):279-290. PubMed
4. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. PubMed
5. Giambra BK, Stiffler D, Broome ME. An integrative review of communication between parents and nurses of hospitalized technology-dependent children. Worldviews Evid Based Nurs. 2014;11(6):369-375. PubMed

6. Comp D. Improving parent satisfaction by sharing the inpatient daily plan of care: an evidence review with implications for practice and research. Pediatr Nurs. 2011;37(5):237-242. PubMed

7. Khan A, Rogers JE, Melvin P, et al. Physician and Nurse Nighttime Communication and Parents’ Hospital Experience. Pediatrics. 2015;136(5):e1249-e1258. PubMed
8. Coghlin DT, Leyenaar JK, Shen M, et al. Pediatric discharge content: a multisite assessment of physician preferences and experiences. Hosp Pediatr. 2014;4(1):9-15. PubMed
9. Harlan G, Srivastava R, Harrison L, McBride G, Maloney C. Pediatric hospitalists and primary care providers: A communication needs assessment. J Hosp Med. 2009;4(3):187-193. PubMed
10. Leyenaar JK, Bergert L, Mallory LA, et al. Pediatric primary care providers’ perspectives regarding hospital discharge communication: a mixed methods analysis. Acad Pediatr. 2015;15(1):61-68. PubMed
11. Ruth JL, Geskey JM, Shaffer ML, Bramley HP, Paul IM. Evaluating communication between pediatric primary care physicians and hospitalists. Clin Pediatr. 2011;50(10):923-928. PubMed
12. Solan LG, Sherman SN, DeBlasio D, Simmons JM. Communication Challenges: A Qualitative Look at the Relationship Between Pediatric Hospitalists and Primary Care Providers. Acad Pediatr. 2016;16(5):453-459. PubMed
13. Adams DR, Flores A, Coltri A, Meltzer DO, Arora VM. A Missed Opportunity to Improve Patient Satisfaction? Patient Perceptions of Inpatient Communication With Their Primary Care Physician. Am J Med Qual. 2016;31(6)568-576. PubMed
14. Hruby M, Pantilat SZ, Lo B. How do patients view the role of the primary care physician in inpatient care? Dis Mon. 2002;48(4):230-238. PubMed
15. Rao JK, Anderson LA, Inui TS, Frankel RM. Communication interventions make a difference in conversations between physicians and patients - A systematic review of the evidence. Med Care. 2007;45(4):340-349. PubMed
16. Banka G, Edgington S, Kyulo N, et al. Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10(8):497-502. PubMed
17. Solan LG, Beck AF, Brunswick SA, et al. The Family Perspective on Hospital to Home Transitions: A Qualitative Study. Pediatrics. 2015;136(6):e1539-e1549. PubMed
18. Tubbs-Cooley HL, Pickler RH, Simmons JM, et al. Testing a post-discharge nurse-led transitional home visit in acute care pediatrics: the Hospital-To-Home Outcomes (H2O) study protocol. J Adv Nurs. 2016;72(4)915-925. PubMed
19. Muething SE, Kotagal UR, Schoettker PJ, Gonzalez del Rey J, DeWitt TG. Family-centered bedside rounds: a new approach to patient care and teaching. Pediatrics. 2007;119(4):829-832. PubMed
20. Beck AF, Solan LG, Brunswick SA, et al. Socioeconomic status influences the toll paediatric hospitalisations take on families: a qualitative study. BMJ Qual Saf. 2017;26(4)304-311. PubMed
21. Crabtree BF, Miller WL. Doing Qualitative Research. 2nd ed. Thousand Oaks: Sage Publications; 1999. 
22. Stewart D, Shamdasani P, Rook D. Focus Groups: Theory and Practice. 2nd ed. Thousand Oaks: Sage Publications; 2007. 
23. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: the Public Health Disparities Geocoding Project. Am J Public Health. 2005;95(2):312-323. PubMed
24. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. American J Epidemiol. 2002;156(5):471-482. PubMed
25. Krieger N, Waterman P, Chen JT, Soobader MJ, Subramanian SV, Carson R. Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project. Am J Public Health. 2002;92(7):1100-1102. PubMed
26. Shonkoff JP, Garner AS; Committee on Psychosocial Aspects of Child and Family Health; Committee on Early Childhood, Adoption, and Dependent Care; Section on Developmental and Behavioral Pediatrics. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232-e246. PubMed
27. Patton MQ. Qualitative Research and Evaluation Methods. 3rd ed. Thousand Oaks: Sage Publications; 2002. 
28. Miles MB, Huberman AM, Saldaña J. Qualitative Data Analysis: A Methods Sourcebook. 3rd ed. Thousand Oaks: Sage Publications; 2014. 
29. Kuo DZ, Houtrow AJ, Arango P, Kuhlthau KA, Simmons JM, Neff JM. Family-centered care: current applications and future directions in pediatric health care. Matern Child Health J. 2012;16(2):297-305. PubMed

30. Latta LC, Dick R, Parry C, Tamura GS. Parental responses to involvement in rounds on a pediatric inpatient unit at a teaching hospital: a qualitative study. Acad Med. 2008;83(3):292-297. PubMed

31. Bent KN, Keeling A, Routson J. Home from the PICU: are parents ready? MCN Am J Matern Child Nurs. 1996;21(2):80-84. PubMed
32. Heuer L. Parental stressors in a pediatric intensive care unit. Pediatr Nurs. 1993;19(2):128-131. PubMed
33. Lapillonne A, Regnault A, Gournay V, et al. Impact on parents of bronchiolitis hospitalization of full-term, preterm and congenital heart disease infants. BMC Pediatr. 2012;12:171-181. PubMed
34. Leidy NK, Margolis MK, Marcin JP, et al. The impact of severe respiratory syncytial virus on the child, caregiver, and family during hospitalization and recovery. Pediatrics. 2005;115(6):1536-1546. PubMed
35. Bringing I-PASS to the Bedside: A Communication Bundle to Improve Patient Safety and Experience. http://www.pcori.org/research-results/2013/bringing-i-pass-bedside-communication-bundle-improve-patient-safety-and. Accessed on December 1, 2016.
36. Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186-188. PubMed
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Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.1 The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.2 Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,3,15,16 there remains much work to be done.1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study17 by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

METHODS

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.17 This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.18

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.19 On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

 

 

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).26 This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,17 developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation27 was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.27 Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.28 As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.27,28 Coded data were maintained in an electronic database accessible only to study personnel.

RESULTS

Participants

Sixty-one caregivers of children discharged from CCHMC participated. Participants were 87% female and 46% non-white; 42.5% had a 2-year college level of education or greater, and 56% resided in census tracts with ≥15% of residents living in poverty (Table 1). Participant characteristics aligned closely with the demographics of families of children hospitalized at CCHMC.

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

 

 

Experiences during the hospitalization contributed to caregivers’ perceptions of their communication with their child’s inpatient medical team. There were 5 related subthemes identified. The following 2 subthemes were characterized as positive experiences: (1) feeling like part of the team and (2) nurses as interpreters and navigators. The following 3 subthemes were characterized as negative: (1) feeling left out of the loop, (2) insufficient face time with physicians, and (3) the use of medical jargon (Table 2). More specifically, participants described feeling more satisfied with their care and the inpatient experience when they felt included and when their input and expertise as a caregiver was valued. They also appreciated how nurses often took the time after FCR or interactions with the medical team to explain and clarify information that was discussed with the patient and their caregiver. For example, 1 participant stated, “Whenever I ask about anything, I just ask the nurse. And if she didn’t know, she would find out for me…”

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

At a large teaching institution with various trainees and multiple subspecialties, communication challenges were particularly prominent. Three subthemes were related to this theme: (1) confusing messages with a large multidisciplinary team, (2) perceptions of FCR, and (3) role confusion, or who’s in charge of the team? (Table 3). Participants described confusing and inconsistent messages arising from the involvement of many medical providers. One stated, “When [the providers] all talk it seems like it don’t make sense because [what] one [is] saying is slightly different [from] the other one…and then you’d be like, ‘Wait, what?’ So it kind of confuses you…” Similarly, the use of FCR was overwhelming for the majority of participants who cited difficulty tracking conversations, feeling “lost” in the crowd of team members, or feeling excluded from the conversation about their child. One participant stated, “But because so many people came in, it can get overwhelming. They come in big groups, like 10 at once.” In contrast, some participants had a more favorable view of FCR: “What really blew me away was I came out of the restroom and there is 10 doctors standing around and they very well observed my child. And not only one doctor, but every one of them knew was going on with my kid. It kind of blew me away.” Participants felt it was not always clear who was in charge of the medical team. Trying to remember the various roles of all of the team members contributed to this confusion and made asking questions difficult. One participant shared, “I just want the main people…the boss to come in, check the baby out. I don’t need all the extra people running around me, keep asking me the same thing on that topic. Send in the main group, the bosses, they know what the problem is and how to fix it.”

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Caregivers have a unique vantage point as they witness many interactions between medical providers during their child’s hospitalization. Still, they do not generally witness all the interactions between inpatient providers or between inpatient and outpatient providers. This led to variable perceptions of this communication. Specifically, the 2 subthemes described here were (1) communication between inpatient medical providers and (2) communication between inpatient and outpatient providers (Table 4). Caregivers assessed how well (or how poorly) medical providers communicated with each other based upon the consistency of messages they received or interactions they personally experienced or observed. One participant described how the medical team did not appear to be in consensus about when to discharge her child, highlighting the perception that team members did not have a shared understanding of the child’s needs: “One of the doctors was…nervous about sending him home. It was just one doctor…the other doctors on her team and everything and the nurses, they were like ‘He’s fine.’” Others shared concerns related to inadequate handoff and messages not getting passed along shift-to-shift.

 

 

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

DISCUSSION

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.2 During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.35 Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.36 found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.29 Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,8-10,12 but few have looked at it from the perspective of the caregiver.13 After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

 

 

CONCLUSION

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

ACKNOWLEDGMENTS

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

Provision of high-quality, high-value medical care hinges upon effective communication. During a hospitalization, critical information is communicated between patients, caregivers, and providers multiple times each day. This can cause inconsistent and misinterpreted messages, leaving ample room for error.1 The Joint Commission notes that communication failures occurring between medical providers account for ~60% of all sentinel or serious adverse events that result in death or harm to a patient.2 Communication that occurs between patients and/or their caregivers and medical providers is also critically important. The content and consistency of this communication is highly valued by patients and providers and can affect patient outcomes during hospitalizations and during transitions to home.3,4 Still, the multifactorial, complex nature of communication in the pediatric inpatient setting is not well understood.5,6

During hospitalization, communication happens continuously during both daytime and nighttime hours. It also precedes the particularly fragile period of transition from hospital to home. Studies have shown that nighttime communication between caregivers and medical providers (ie, nurses and physicians), as well as caregivers’ perceptions of interactions that occur between nurses and physicians, may be closely linked to that caregiver’s satisfaction and perceived quality of care.6,7 Communication that occurs between inpatient and outpatient providers is also subject to barriers (eg, limited availability for direct communication)8-12; studies have shown that patient and/or caregiver satisfaction has also been tied to perceptions of this communication.13,14 Moreover, a caregiver’s ability to understand diagnoses and adhere to postdischarge care plans is intimately tied to communication during the hospitalization and at discharge. Although many improvement efforts have aimed to enhance communication during these vulnerable time periods,3,15,16 there remains much work to be done.1,10,12

The many facets and routes of communication, and the multiple stakeholders involved, make improvement efforts challenging. We believe that more effective communication strategies could result from a deeper understanding of how caregivers view communication successes and challenges during a hospitalization. We see this as key to developing meaningful interventions that are directed towards improving communication and, by extension, patient satisfaction and safety. Here, we sought to extend findings from a broader qualitative study17 by developing an in-depth understanding of communication issues experienced by families during their child’s hospitalization and during the transition to home.

METHODS

Setting

The analyses presented here emerged from the Hospital to Home Outcomes Study (H2O). The first objective of H2O was to explore the caregiver perspective on hospital-to-home transitions. Here, we present the results related to caregiver perspectives of communication, while broader results of our qualitative investigation have been published elsewhere.17 This objective informed the latter 2 aims of the H2O study, which were to modify an existing nurse-led transitional home visit (THV) program and to study the effectiveness of the modified THV on reutilization and patient-specific outcomes via a randomized control trial. The specifics of the H2O protocol and design have been presented elsewhere.18

H2O was approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC), a free-standing, academic children’s hospital with ~600 inpatient beds. This teaching hospital has >800 total medical students, residents, and fellows. Approximately 8000 children are hospitalized annually at CCHMC for general pediatric conditions, with ~85% of such admissions staffed by hospitalists from the Division of Hospital Medicine. The division is composed of >40 providers who devote the majority of their clinical time to the hospital medicine service; 15 additional providers work on the hospital medicine service but have primary clinical responsibilities in another division.

Family-centered rounds (FCR) are the standard of care at CCHMC, involving family members at the bedside to discuss patient care plans and diagnoses with the medical team.19 On a typical day, a team conducting FCR is composed of 1 attending, 1 fellow, 2 to 3 pediatric residents, 2 to 3 medical students, a charge nurse or bedside nurse, and a pharmacist. Other ancillary staff, such as social workers, care coordinators, nurse practitioners, or dieticians, may also participate on rounds, particularly for children with greater medical complexity.

 

 

Population

Caregivers of children discharged with acute medical conditions were eligible for recruitment if they were English-speaking (we did not have access to interpreter services during focus groups/interviews), had a child admitted to 1 of 3 services (hospital medicine, neurology, or neurosurgery), and could attend a focus group within 30 days of the child’s discharge. The majority of participants had a child admitted to hospital medicine; however, caregivers with a generally healthy child admitted to either neurology or neurosurgery were eligible to participate in the study.

Study Design

As presented elsewhere,17,20 we used focus groups and individual in-depth interviews to generate consensus themes about patient and caregiver experiences during the transition from hospital to home. Because there is evidence suggesting that focus group participants are more willing to talk openly when among others of similar backgrounds, we stratified the sample by the family’s estimated socioeconomic status.21,22 Socioeconomic status was estimated by identifying the poverty rate in the census tract in which each participant lived. Census tracts, relatively homogeneous areas of ~4000 individuals, have been previously shown to effectively detect socioeconomic gradients.23-26 Here, we separated participants into 2 socioeconomically distinct groupings (those in census tracts where <15% or ≥15% of the population lived below the federal poverty level).26 This cut point ensured an equivalent number of eligible participants within each stratum and diversity within our sample.

Data Collection

Caregivers were recruited on the inpatient unit during their child’s hospitalization. Participants then returned to CCHMC facilities for the focus group within 30 days of discharge. Though efforts were made to enhance participation by scheduling sessions at multiple sites and during various days and times of the week, 4 sessions yielded just 1 participant; thus, the format for those became an individual interview. Childcare was provided, and participants received a gift card for their participation.

An open-ended, semistructured question guide,17 developed de novo by the research team, directed the discussion for focus groups and interviews. As data collection progressed, the question guide was adapted to incorporate new issues raised by participants. Questions broadly focused on aspects of the inpatient experience, discharge processes, and healthcare system and family factors thought to be most relevant to patient- and family-centered outcomes. Communication-related questions addressed information shared with families from the medical team about discharge, diagnoses, instructions, and care plans. An experienced moderator and qualitative research methodologist (SNS) used probes to further elucidate responses and expand discussion by participants. Sessions were held in private conference rooms, lasted ~90 minutes, were audiotaped, and were transcribed verbatim. Identifiers were stripped and transcripts were reviewed for accuracy. After conducting 11 focus groups (generally composed of 5-10 participants) and 4 individual interviews, the research team determined that theoretical saturation27 was achieved, and recruitment was suspended.

Data Analysis

An inductive, thematic approach was used for analysis.27 Transcripts were independently reviewed by a multidisciplinary team of 4 researchers, including 2 pediatricians (LGS and AFB), a clinical research coordinator (SAS), and a qualitative research methodologist (SNS). The study team identified emerging concepts and themes related to the transition from hospital to home; themes related to communication during hospitalization are presented here.

During the first phase of analysis, investigators independently read transcripts and later convened to identify and define initial concepts and themes. A preliminary codebook was then designed. Investigators continued to review and code transcripts independently, meeting regularly to discuss coding decisions collaboratively, resolving differences through consensus.28 As patterns in the data became apparent, the codebook was modified iteratively, adding, subtracting, and refining codes as needed and grouping related codes. Results were reviewed with key stakeholders, including parents, inpatient and outpatient pediatricians, and home health nurses, throughout the analytic process.27,28 Coded data were maintained in an electronic database accessible only to study personnel.

RESULTS

Participants

Sixty-one caregivers of children discharged from CCHMC participated. Participants were 87% female and 46% non-white; 42.5% had a 2-year college level of education or greater, and 56% resided in census tracts with ≥15% of residents living in poverty (Table 1). Participant characteristics aligned closely with the demographics of families of children hospitalized at CCHMC.

Resulting Themes

Analyses revealed the following 3 major communication-related themes with associated subthemes: (1) experiences that affect caregiver perceptions of communication between the inpatient medical team and families, (2) communication challenges for caregivers related to a teaching hospital environment, and (3) caregiver perceptions of communication between medical providers. Each theme (and subtheme) is explored below with accompanying verbatim quotes in the narrative and the tables.

Major Theme 1: Experiences that Affect Caregiver Perceptions of Communication Between the Inpatient Medical Team and Families

 

 

Experiences during the hospitalization contributed to caregivers’ perceptions of their communication with their child’s inpatient medical team. There were 5 related subthemes identified. The following 2 subthemes were characterized as positive experiences: (1) feeling like part of the team and (2) nurses as interpreters and navigators. The following 3 subthemes were characterized as negative: (1) feeling left out of the loop, (2) insufficient face time with physicians, and (3) the use of medical jargon (Table 2). More specifically, participants described feeling more satisfied with their care and the inpatient experience when they felt included and when their input and expertise as a caregiver was valued. They also appreciated how nurses often took the time after FCR or interactions with the medical team to explain and clarify information that was discussed with the patient and their caregiver. For example, 1 participant stated, “Whenever I ask about anything, I just ask the nurse. And if she didn’t know, she would find out for me…”

In contrast, some of the negative experiences shared by participants related to feeling excluded from discussions about their child’s care. One participant said, “They tell you…as much as they want to tell you. They don’t fully inform you on things.” Additionally, concerns were voiced about insufficient time for face-to-face discussions with physicians: “I forget what I have to say and it’s something really, really important…But now, my doctor is going, you can’t get the doctor back.” Finally, participants discussed how the use of medical jargon often made it more difficult to understand things, especially for those not in the medical field.

Major Theme 2: Communication Challenges for Caregivers Related to a Teaching Hospital Environment

At a large teaching institution with various trainees and multiple subspecialties, communication challenges were particularly prominent. Three subthemes were related to this theme: (1) confusing messages with a large multidisciplinary team, (2) perceptions of FCR, and (3) role confusion, or who’s in charge of the team? (Table 3). Participants described confusing and inconsistent messages arising from the involvement of many medical providers. One stated, “When [the providers] all talk it seems like it don’t make sense because [what] one [is] saying is slightly different [from] the other one…and then you’d be like, ‘Wait, what?’ So it kind of confuses you…” Similarly, the use of FCR was overwhelming for the majority of participants who cited difficulty tracking conversations, feeling “lost” in the crowd of team members, or feeling excluded from the conversation about their child. One participant stated, “But because so many people came in, it can get overwhelming. They come in big groups, like 10 at once.” In contrast, some participants had a more favorable view of FCR: “What really blew me away was I came out of the restroom and there is 10 doctors standing around and they very well observed my child. And not only one doctor, but every one of them knew was going on with my kid. It kind of blew me away.” Participants felt it was not always clear who was in charge of the medical team. Trying to remember the various roles of all of the team members contributed to this confusion and made asking questions difficult. One participant shared, “I just want the main people…the boss to come in, check the baby out. I don’t need all the extra people running around me, keep asking me the same thing on that topic. Send in the main group, the bosses, they know what the problem is and how to fix it.”

Major Theme 3: Caregiver Perceptions of Communication Between Medical Providers

Caregivers have a unique vantage point as they witness many interactions between medical providers during their child’s hospitalization. Still, they do not generally witness all the interactions between inpatient providers or between inpatient and outpatient providers. This led to variable perceptions of this communication. Specifically, the 2 subthemes described here were (1) communication between inpatient medical providers and (2) communication between inpatient and outpatient providers (Table 4). Caregivers assessed how well (or how poorly) medical providers communicated with each other based upon the consistency of messages they received or interactions they personally experienced or observed. One participant described how the medical team did not appear to be in consensus about when to discharge her child, highlighting the perception that team members did not have a shared understanding of the child’s needs: “One of the doctors was…nervous about sending him home. It was just one doctor…the other doctors on her team and everything and the nurses, they were like ‘He’s fine.’” Others shared concerns related to inadequate handoff and messages not getting passed along shift-to-shift.

 

 

Perceptions were not isolated to the inpatient setting. Based on their experiences, caregivers similarly described their sense of how inpatient and outpatient providers were communicating with each other. In some cases, it was clear that good communication, as perceived by the participant, had occurred in situations in which the primary care physician knew “everything” about the hospitalization when they saw the patient in follow-up. One participant described, “We didn’t even realize at the time, [the medical team] had actually called our doctor and filled them in on our situation, and we got [to the follow up visit]…He already knew the entire situation.” There were others, however, who shared their uncertainty about whether the information exchange about their child’s hospitalization had actually occurred. They, therefore, voiced apprehension around who to call for advice after discharge; would their outpatient provider have their child’s hospitalization history and be able to properly advise them?

DISCUSSION

Communication during a hospitalization and at transition from hospital to home happens in both formal and informal ways; it is a vital component of appropriate, effective patient care. When done poorly, it has the potential to negatively affect a patient’s safety, care, and key outcomes.2 During a hospitalization, the multifaceted nature of communication and multidisciplinary approach to care provision can create communication challenges and make fixing challenges difficult. In order to more comprehensively move toward mitigation, it is important to gather perspectives of key stakeholders, such as caregivers. Caregivers are an integral part of their child’s care during the hospitalization and particularly at home during their child’s recovery. They are also a valued member of the team, particularly in this era of family-centered care.19,29 The perspectives of the caregivers presented here identified both successes and challenges of their communication experiences with the medical team during their child’s hospitalization. These perspectives included experiences affecting perceptions of communication between the inpatient medical team and families; communication related to the teaching hospital environment, including confusing messages associated with large multidisciplinary teams, aspects of FCR, and confusion about medical team member roles; and caregivers’ perceptions of communication between providers in and out of the hospital, including types of communication caregivers observed or believed occurred between medical providers. We believe that these qualitative results are crucial to developing better, more targeted interventions to improve communication.

Maintaining a healthy and productive relationship with patients and their caregivers is critical to providing comprehensive and safe patient care. As supported in the literature, we found that when caregivers were included in conversations, they felt appreciated and valued; in addition, when answers were not directly shared by providers or there were lingering questions, nurses often served as “interpreters.”29,30 Indeed, nurses were seen as a critical touchpoint for many participants, individuals that could not only answer questions but also be a trusted source of information. Supporting such a relationship, and helping enhance the relationship between the family and other team members, may be particularly important considering the degree to which a hospitalization can stress a patient, caregiver, and family.31-34 Developing rapport with families and facilitating relationships with the inclusion of nursing during FCR can be particularly helpful. Though this can be challenging with the many competing priorities of medical providers and the fast-paced, acute nature of inpatient care, making an effort to include nursing staff on rounds can cut down on confusion and assist the family in understanding care plans. This, in turn, can minimize the stress associated with hospitalization and improve the patient and family experience.

While academic institutions’ resources and access to subspecialties are often thought to be advantageous, there are other challenges inherent to providing care in such complex environments. Some caregivers cited confusion related to large teams of providers with, to them, indistinguishable roles asking redundant questions. These experiences affected their perceptions of FCR, generally leading to a fixation on its overwhelming aspects. Certain caregivers highlighted that FCR caused them, and their child, to feel overwhelmed and more confused about the plan for the day. It is important to find ways to mitigate these feelings while simultaneously continuing to support the inclusion of caregivers during their child’s hospitalization and understanding of care plans. Some initiatives (in addition to including nursing on FCR as discussed above) focus on improving the ways in which providers communicate with families during rounds and throughout the day, seeking to decrease miscommunications and medical errors while also striving for better quality of care and patient/family satisfaction.35 Other initiatives seek to clarify identities and roles of the often large and confusing medical team. One such example of this is the development of a face sheet tool, which provides families with medical team members’ photos and role descriptions. Unaka et al.36 found that the use of the face sheet tool improved the ability of caregivers to correctly identify providers and their roles. Thinking beyond interventions at the bedside, it is also important to include caregivers on higher level committees within the institution, such as on family advisory boards and/or peer support groups, to inform systems-wide interventions that support the tenants of family-centered care.29 Efforts such as these are worth trialing in order to improve the patient and family experience and quality of communication.

Multiple studies have evaluated the challenges with ensuring consistent and useful handoffs across the inpatient-to-outpatient transition,8-10,12 but few have looked at it from the perspective of the caregiver.13 After leaving the hospital to care for their recovering child, caregivers often feel overwhelmed; they may want, or need, to rely on the support of others in the outpatient environment. This support can be enhanced when outpatient providers are intimately aware of what occurred during the hospitalization; trust erodes if this is not the case. Given the value caregivers place on this communication occurring and occurring well, interventions supporting this communication are critical. Furthermore, as providers, we should also inform families that communication with outpatient providers is happening. Examples of efforts that have worked to improve the quality and consistency of communication with outpatient providers include improving discharge summary documentation, ensuring timely faxing of documentation to outpatient providers, and reliably making phone calls to outpatient providers.37-39 These types of interventions seek to bridge the gap between inpatient and outpatient care and facilitate a smooth transfer of information in order to provide optimal quality of care and avoid undesired outcomes (eg, emergency department revisits, readmissions, medication errors, etc) and can be adopted by institutions to address the issue of communication between inpatient and outpatient providers.

We acknowledge limitations to our study. This was done at a single academic institution with only English-speaking participants. Thus, our results may not be reflective of caregivers of children cared for in different, more ethnically or linguistically diverse settings. The patient population at CCHMC, however, is diverse both demographically and clinically, which was reflected in the composition of our focus groups and interviews. Additionally, the inclusion of participants who received a nurse home visit after discharge may limit generalizability. However, only 4 participants had a nurse home visit; thus, the overwhelming majority of participants did not receive such an intervention. We also acknowledge that those willing to participate may have differed from nonparticipants, specifically sharing more positive experiences. We believe that our sampling strategy and use of an unbiased, nonhospital affiliated moderator minimized this possibility. Recall bias is possible, as participants were asked to reflect back on a discharge experience occurring in their past. We attempted to minimize this by holding sessions no more than 30 days from the day of discharge. Finally, we present data on caregivers’ perception of communication and not directly observed communication occurrences. Still, we expect that perception is powerful in and of itself, relevant to both outcomes and to interventions.

 

 

CONCLUSION

Communication during hospitalization influences how caregivers understand diagnoses and care plans. Communication perceived as effective fosters mutual understandings and positive relationships with the potential to result in better care and improved outcomes. Communication perceived as ineffective negatively affects experiences of patients and their caregivers and can adversely affect patient outcomes. Learning from caregivers’ experiences with communication during their child’s hospitalization can help identify modifiable factors and inform strategies to improve communication, support families through hospitalization, and facilitate a smooth reentry home.

ACKNOWLEDGMENTS

This manuscript is submitted on behalf of the H2O study group: Katherine A. Auger, MD, MSc, JoAnne Bachus, BSN, Monica L. Borell, BSN, Lenisa V. Chang, MA, PhD, Jennifer M. Gold, BSN, Judy A. Heilman, RN, Joseph A. Jabour, BS, Jane C. Khoury, PhD, Margo J. Moore, BSN, CCRP, Rita H. Pickler, PNP, PhD, Anita N. Shah, DO, Angela M. Statile, MD, MEd, Heidi J. Sucharew, PhD, Karen P. Sullivan, BSN, Heather L. Tubbs-Cooley, RN, PhD, Susan Wade-Murphy, MSN, and Christine M. White, MD, MAT.

Disclaimer

All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Disclosure

 This work was (partially) supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (HIS-1306-0081). The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest to disclose.

References

1. Riesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and Attending Physicians’ Handoffs: A Systematic Review of the Literature. Acad Med. 2009;84(12):1775-1787. PubMed
2. The Joint Commission releases improving America’s hospitals: a report on quality and safety. JT Comm Perspect. 2007;27(5):1, 3. PubMed
3. Nobile C, Drotar D. Research on the quality of parent-provider communication in pediatric care: Implications and recommendations. J Dev Behav Pediatr. 2003;24(4):279-290. PubMed
4. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. PubMed
5. Giambra BK, Stiffler D, Broome ME. An integrative review of communication between parents and nurses of hospitalized technology-dependent children. Worldviews Evid Based Nurs. 2014;11(6):369-375. PubMed

6. Comp D. Improving parent satisfaction by sharing the inpatient daily plan of care: an evidence review with implications for practice and research. Pediatr Nurs. 2011;37(5):237-242. PubMed

7. Khan A, Rogers JE, Melvin P, et al. Physician and Nurse Nighttime Communication and Parents’ Hospital Experience. Pediatrics. 2015;136(5):e1249-e1258. PubMed
8. Coghlin DT, Leyenaar JK, Shen M, et al. Pediatric discharge content: a multisite assessment of physician preferences and experiences. Hosp Pediatr. 2014;4(1):9-15. PubMed
9. Harlan G, Srivastava R, Harrison L, McBride G, Maloney C. Pediatric hospitalists and primary care providers: A communication needs assessment. J Hosp Med. 2009;4(3):187-193. PubMed
10. Leyenaar JK, Bergert L, Mallory LA, et al. Pediatric primary care providers’ perspectives regarding hospital discharge communication: a mixed methods analysis. Acad Pediatr. 2015;15(1):61-68. PubMed
11. Ruth JL, Geskey JM, Shaffer ML, Bramley HP, Paul IM. Evaluating communication between pediatric primary care physicians and hospitalists. Clin Pediatr. 2011;50(10):923-928. PubMed
12. Solan LG, Sherman SN, DeBlasio D, Simmons JM. Communication Challenges: A Qualitative Look at the Relationship Between Pediatric Hospitalists and Primary Care Providers. Acad Pediatr. 2016;16(5):453-459. PubMed
13. Adams DR, Flores A, Coltri A, Meltzer DO, Arora VM. A Missed Opportunity to Improve Patient Satisfaction? Patient Perceptions of Inpatient Communication With Their Primary Care Physician. Am J Med Qual. 2016;31(6)568-576. PubMed
14. Hruby M, Pantilat SZ, Lo B. How do patients view the role of the primary care physician in inpatient care? Dis Mon. 2002;48(4):230-238. PubMed
15. Rao JK, Anderson LA, Inui TS, Frankel RM. Communication interventions make a difference in conversations between physicians and patients - A systematic review of the evidence. Med Care. 2007;45(4):340-349. PubMed
16. Banka G, Edgington S, Kyulo N, et al. Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10(8):497-502. PubMed
17. Solan LG, Beck AF, Brunswick SA, et al. The Family Perspective on Hospital to Home Transitions: A Qualitative Study. Pediatrics. 2015;136(6):e1539-e1549. PubMed
18. Tubbs-Cooley HL, Pickler RH, Simmons JM, et al. Testing a post-discharge nurse-led transitional home visit in acute care pediatrics: the Hospital-To-Home Outcomes (H2O) study protocol. J Adv Nurs. 2016;72(4)915-925. PubMed
19. Muething SE, Kotagal UR, Schoettker PJ, Gonzalez del Rey J, DeWitt TG. Family-centered bedside rounds: a new approach to patient care and teaching. Pediatrics. 2007;119(4):829-832. PubMed
20. Beck AF, Solan LG, Brunswick SA, et al. Socioeconomic status influences the toll paediatric hospitalisations take on families: a qualitative study. BMJ Qual Saf. 2017;26(4)304-311. PubMed
21. Crabtree BF, Miller WL. Doing Qualitative Research. 2nd ed. Thousand Oaks: Sage Publications; 1999. 
22. Stewart D, Shamdasani P, Rook D. Focus Groups: Theory and Practice. 2nd ed. Thousand Oaks: Sage Publications; 2007. 
23. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: the Public Health Disparities Geocoding Project. Am J Public Health. 2005;95(2):312-323. PubMed
24. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. American J Epidemiol. 2002;156(5):471-482. PubMed
25. Krieger N, Waterman P, Chen JT, Soobader MJ, Subramanian SV, Carson R. Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project. Am J Public Health. 2002;92(7):1100-1102. PubMed
26. Shonkoff JP, Garner AS; Committee on Psychosocial Aspects of Child and Family Health; Committee on Early Childhood, Adoption, and Dependent Care; Section on Developmental and Behavioral Pediatrics. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232-e246. PubMed
27. Patton MQ. Qualitative Research and Evaluation Methods. 3rd ed. Thousand Oaks: Sage Publications; 2002. 
28. Miles MB, Huberman AM, Saldaña J. Qualitative Data Analysis: A Methods Sourcebook. 3rd ed. Thousand Oaks: Sage Publications; 2014. 
29. Kuo DZ, Houtrow AJ, Arango P, Kuhlthau KA, Simmons JM, Neff JM. Family-centered care: current applications and future directions in pediatric health care. Matern Child Health J. 2012;16(2):297-305. PubMed

30. Latta LC, Dick R, Parry C, Tamura GS. Parental responses to involvement in rounds on a pediatric inpatient unit at a teaching hospital: a qualitative study. Acad Med. 2008;83(3):292-297. PubMed

31. Bent KN, Keeling A, Routson J. Home from the PICU: are parents ready? MCN Am J Matern Child Nurs. 1996;21(2):80-84. PubMed
32. Heuer L. Parental stressors in a pediatric intensive care unit. Pediatr Nurs. 1993;19(2):128-131. PubMed
33. Lapillonne A, Regnault A, Gournay V, et al. Impact on parents of bronchiolitis hospitalization of full-term, preterm and congenital heart disease infants. BMC Pediatr. 2012;12:171-181. PubMed
34. Leidy NK, Margolis MK, Marcin JP, et al. The impact of severe respiratory syncytial virus on the child, caregiver, and family during hospitalization and recovery. Pediatrics. 2005;115(6):1536-1546. PubMed
35. Bringing I-PASS to the Bedside: A Communication Bundle to Improve Patient Safety and Experience. http://www.pcori.org/research-results/2013/bringing-i-pass-bedside-communication-bundle-improve-patient-safety-and. Accessed on December 1, 2016.
36. Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186-188. PubMed
37. Mussman GM, Vossmeyer MT, Brady PW, Warrick DM, Simmons JM, White CM. Improving the reliability of verbal communication between primary care physicians and pediatric hospitalists at hospital discharge. J Hosp Med. 2015;10(9):574-580. PubMed
38. Key-Solle M, Paulk E, Bradford K, Skinner AC, Lewis MC, Shomaker K. Improving the quality of discharge communication with an educational intervention. Pediatrics. 2010;126(4):734-739. PubMed
39. Harlan GA, Nkoy FL, Srivastava R, et al. Improving transitions of care at hospital discharge--implications for pediatric hospitalists and primary care providers. J Healthc Qual. 2010;32(5):51-60. PubMed

References

1. Riesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and Attending Physicians’ Handoffs: A Systematic Review of the Literature. Acad Med. 2009;84(12):1775-1787. PubMed
2. The Joint Commission releases improving America’s hospitals: a report on quality and safety. JT Comm Perspect. 2007;27(5):1, 3. PubMed
3. Nobile C, Drotar D. Research on the quality of parent-provider communication in pediatric care: Implications and recommendations. J Dev Behav Pediatr. 2003;24(4):279-290. PubMed
4. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. PubMed
5. Giambra BK, Stiffler D, Broome ME. An integrative review of communication between parents and nurses of hospitalized technology-dependent children. Worldviews Evid Based Nurs. 2014;11(6):369-375. PubMed

6. Comp D. Improving parent satisfaction by sharing the inpatient daily plan of care: an evidence review with implications for practice and research. Pediatr Nurs. 2011;37(5):237-242. PubMed

7. Khan A, Rogers JE, Melvin P, et al. Physician and Nurse Nighttime Communication and Parents’ Hospital Experience. Pediatrics. 2015;136(5):e1249-e1258. PubMed
8. Coghlin DT, Leyenaar JK, Shen M, et al. Pediatric discharge content: a multisite assessment of physician preferences and experiences. Hosp Pediatr. 2014;4(1):9-15. PubMed
9. Harlan G, Srivastava R, Harrison L, McBride G, Maloney C. Pediatric hospitalists and primary care providers: A communication needs assessment. J Hosp Med. 2009;4(3):187-193. PubMed
10. Leyenaar JK, Bergert L, Mallory LA, et al. Pediatric primary care providers’ perspectives regarding hospital discharge communication: a mixed methods analysis. Acad Pediatr. 2015;15(1):61-68. PubMed
11. Ruth JL, Geskey JM, Shaffer ML, Bramley HP, Paul IM. Evaluating communication between pediatric primary care physicians and hospitalists. Clin Pediatr. 2011;50(10):923-928. PubMed
12. Solan LG, Sherman SN, DeBlasio D, Simmons JM. Communication Challenges: A Qualitative Look at the Relationship Between Pediatric Hospitalists and Primary Care Providers. Acad Pediatr. 2016;16(5):453-459. PubMed
13. Adams DR, Flores A, Coltri A, Meltzer DO, Arora VM. A Missed Opportunity to Improve Patient Satisfaction? Patient Perceptions of Inpatient Communication With Their Primary Care Physician. Am J Med Qual. 2016;31(6)568-576. PubMed
14. Hruby M, Pantilat SZ, Lo B. How do patients view the role of the primary care physician in inpatient care? Dis Mon. 2002;48(4):230-238. PubMed
15. Rao JK, Anderson LA, Inui TS, Frankel RM. Communication interventions make a difference in conversations between physicians and patients - A systematic review of the evidence. Med Care. 2007;45(4):340-349. PubMed
16. Banka G, Edgington S, Kyulo N, et al. Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10(8):497-502. PubMed
17. Solan LG, Beck AF, Brunswick SA, et al. The Family Perspective on Hospital to Home Transitions: A Qualitative Study. Pediatrics. 2015;136(6):e1539-e1549. PubMed
18. Tubbs-Cooley HL, Pickler RH, Simmons JM, et al. Testing a post-discharge nurse-led transitional home visit in acute care pediatrics: the Hospital-To-Home Outcomes (H2O) study protocol. J Adv Nurs. 2016;72(4)915-925. PubMed
19. Muething SE, Kotagal UR, Schoettker PJ, Gonzalez del Rey J, DeWitt TG. Family-centered bedside rounds: a new approach to patient care and teaching. Pediatrics. 2007;119(4):829-832. PubMed
20. Beck AF, Solan LG, Brunswick SA, et al. Socioeconomic status influences the toll paediatric hospitalisations take on families: a qualitative study. BMJ Qual Saf. 2017;26(4)304-311. PubMed
21. Crabtree BF, Miller WL. Doing Qualitative Research. 2nd ed. Thousand Oaks: Sage Publications; 1999. 
22. Stewart D, Shamdasani P, Rook D. Focus Groups: Theory and Practice. 2nd ed. Thousand Oaks: Sage Publications; 2007. 
23. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: the Public Health Disparities Geocoding Project. Am J Public Health. 2005;95(2):312-323. PubMed
24. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. American J Epidemiol. 2002;156(5):471-482. PubMed
25. Krieger N, Waterman P, Chen JT, Soobader MJ, Subramanian SV, Carson R. Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project. Am J Public Health. 2002;92(7):1100-1102. PubMed
26. Shonkoff JP, Garner AS; Committee on Psychosocial Aspects of Child and Family Health; Committee on Early Childhood, Adoption, and Dependent Care; Section on Developmental and Behavioral Pediatrics. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232-e246. PubMed
27. Patton MQ. Qualitative Research and Evaluation Methods. 3rd ed. Thousand Oaks: Sage Publications; 2002. 
28. Miles MB, Huberman AM, Saldaña J. Qualitative Data Analysis: A Methods Sourcebook. 3rd ed. Thousand Oaks: Sage Publications; 2014. 
29. Kuo DZ, Houtrow AJ, Arango P, Kuhlthau KA, Simmons JM, Neff JM. Family-centered care: current applications and future directions in pediatric health care. Matern Child Health J. 2012;16(2):297-305. PubMed

30. Latta LC, Dick R, Parry C, Tamura GS. Parental responses to involvement in rounds on a pediatric inpatient unit at a teaching hospital: a qualitative study. Acad Med. 2008;83(3):292-297. PubMed

31. Bent KN, Keeling A, Routson J. Home from the PICU: are parents ready? MCN Am J Matern Child Nurs. 1996;21(2):80-84. PubMed
32. Heuer L. Parental stressors in a pediatric intensive care unit. Pediatr Nurs. 1993;19(2):128-131. PubMed
33. Lapillonne A, Regnault A, Gournay V, et al. Impact on parents of bronchiolitis hospitalization of full-term, preterm and congenital heart disease infants. BMC Pediatr. 2012;12:171-181. PubMed
34. Leidy NK, Margolis MK, Marcin JP, et al. The impact of severe respiratory syncytial virus on the child, caregiver, and family during hospitalization and recovery. Pediatrics. 2005;115(6):1536-1546. PubMed
35. Bringing I-PASS to the Bedside: A Communication Bundle to Improve Patient Safety and Experience. http://www.pcori.org/research-results/2013/bringing-i-pass-bedside-communication-bundle-improve-patient-safety-and. Accessed on December 1, 2016.
36. Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9(3):186-188. PubMed
37. Mussman GM, Vossmeyer MT, Brady PW, Warrick DM, Simmons JM, White CM. Improving the reliability of verbal communication between primary care physicians and pediatric hospitalists at hospital discharge. J Hosp Med. 2015;10(9):574-580. PubMed
38. Key-Solle M, Paulk E, Bradford K, Skinner AC, Lewis MC, Shomaker K. Improving the quality of discharge communication with an educational intervention. Pediatrics. 2010;126(4):734-739. PubMed
39. Harlan GA, Nkoy FL, Srivastava R, et al. Improving transitions of care at hospital discharge--implications for pediatric hospitalists and primary care providers. J Healthc Qual. 2010;32(5):51-60. PubMed

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The Pipeline From Abstract Presentation to Publication in Pediatric Hospital Medicine

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Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

METHODS

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

 

 

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

RESULTS

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

Of the abstracts submitted to PHM, 118 (52.2%) were also presented at the 2014 PAS meeting. Of these, 19 (16.1%) were rejected from PHM, 79 (66.9%) were accepted for poster presentation, and 20 (16.9%) were accepted for oral presentation. A trainee was the primary author for 40.3% (n = 91) of the abstracts submitted to PHM; abstracts submitted by trainees were more likely to be rejected from conference presentation (P = 0.002). Of the abstracts submitted by a trainee, 7 (24.1%) were accepted for oral presentation, 57 (37.0%) were accepted for poster presentation, and 27 (63%) were rejected from presentation. Adjusting for presentation at PAS and trainee status did not substantively change the odds of subsequent publication for abstracts accepted for poster presentation, but it increased the odds of publication for abstracts accepted for oral presentation (Table 1).

Of the abstracts subsequently published in journals, the median time to publication was 17 months (interquartile range [IQR], 10-21; Table 2, Figure). Abstracts accepted for oral presentation had an almost 4-fold greater likelihood of publication at each month than rejected abstracts (Table 2). Among abstracts that were subsequently published, the median journal impact factor was significantly higher for abstracts accepted for oral presentation than for either rejected abstracts or those accepted for poster presentation (Table 2). The median impact factor by content area was as follows: clinical research 1.0, educational research 2.1, HSR and epidemiology 1.5, practice management research 0, and quality improvement 1.4 (P = 0.023). The most common journals were Hospital Pediatrics (31.7%, n = 26), Pediatrics (15.9%, n = 13), and the Journal of Hospital Medicine (4.9%, n = 4). Oral presentation abstracts were most commonly published in Pediatrics, Hospital Pediatrics, and JAMA Pediatrics. Hospital Pediatrics was the most common journal for abstracts accepted for poster presentation, representing 44.9% of the published abstracts. Rejected abstracts were subsequently published in a range of journals, including Clinical Pediatrics, Advances in Preventative Medicine, and Ethnicity & Disease (Table 3).

 

 

 

DISCUSSION

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

 

 

CONCLUSIONS

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

References

1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed

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Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

METHODS

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

 

 

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

RESULTS

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

Of the abstracts submitted to PHM, 118 (52.2%) were also presented at the 2014 PAS meeting. Of these, 19 (16.1%) were rejected from PHM, 79 (66.9%) were accepted for poster presentation, and 20 (16.9%) were accepted for oral presentation. A trainee was the primary author for 40.3% (n = 91) of the abstracts submitted to PHM; abstracts submitted by trainees were more likely to be rejected from conference presentation (P = 0.002). Of the abstracts submitted by a trainee, 7 (24.1%) were accepted for oral presentation, 57 (37.0%) were accepted for poster presentation, and 27 (63%) were rejected from presentation. Adjusting for presentation at PAS and trainee status did not substantively change the odds of subsequent publication for abstracts accepted for poster presentation, but it increased the odds of publication for abstracts accepted for oral presentation (Table 1).

Of the abstracts subsequently published in journals, the median time to publication was 17 months (interquartile range [IQR], 10-21; Table 2, Figure). Abstracts accepted for oral presentation had an almost 4-fold greater likelihood of publication at each month than rejected abstracts (Table 2). Among abstracts that were subsequently published, the median journal impact factor was significantly higher for abstracts accepted for oral presentation than for either rejected abstracts or those accepted for poster presentation (Table 2). The median impact factor by content area was as follows: clinical research 1.0, educational research 2.1, HSR and epidemiology 1.5, practice management research 0, and quality improvement 1.4 (P = 0.023). The most common journals were Hospital Pediatrics (31.7%, n = 26), Pediatrics (15.9%, n = 13), and the Journal of Hospital Medicine (4.9%, n = 4). Oral presentation abstracts were most commonly published in Pediatrics, Hospital Pediatrics, and JAMA Pediatrics. Hospital Pediatrics was the most common journal for abstracts accepted for poster presentation, representing 44.9% of the published abstracts. Rejected abstracts were subsequently published in a range of journals, including Clinical Pediatrics, Advances in Preventative Medicine, and Ethnicity & Disease (Table 3).

 

 

 

DISCUSSION

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

 

 

CONCLUSIONS

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9

Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.

We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.

METHODS

Study Design

Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.

Main Exposures

The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.

 

 

Main Outcome Measures

All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.

Data Analysis

All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.

RESULTS

For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).

Of the abstracts submitted to PHM, 118 (52.2%) were also presented at the 2014 PAS meeting. Of these, 19 (16.1%) were rejected from PHM, 79 (66.9%) were accepted for poster presentation, and 20 (16.9%) were accepted for oral presentation. A trainee was the primary author for 40.3% (n = 91) of the abstracts submitted to PHM; abstracts submitted by trainees were more likely to be rejected from conference presentation (P = 0.002). Of the abstracts submitted by a trainee, 7 (24.1%) were accepted for oral presentation, 57 (37.0%) were accepted for poster presentation, and 27 (63%) were rejected from presentation. Adjusting for presentation at PAS and trainee status did not substantively change the odds of subsequent publication for abstracts accepted for poster presentation, but it increased the odds of publication for abstracts accepted for oral presentation (Table 1).

Of the abstracts subsequently published in journals, the median time to publication was 17 months (interquartile range [IQR], 10-21; Table 2, Figure). Abstracts accepted for oral presentation had an almost 4-fold greater likelihood of publication at each month than rejected abstracts (Table 2). Among abstracts that were subsequently published, the median journal impact factor was significantly higher for abstracts accepted for oral presentation than for either rejected abstracts or those accepted for poster presentation (Table 2). The median impact factor by content area was as follows: clinical research 1.0, educational research 2.1, HSR and epidemiology 1.5, practice management research 0, and quality improvement 1.4 (P = 0.023). The most common journals were Hospital Pediatrics (31.7%, n = 26), Pediatrics (15.9%, n = 13), and the Journal of Hospital Medicine (4.9%, n = 4). Oral presentation abstracts were most commonly published in Pediatrics, Hospital Pediatrics, and JAMA Pediatrics. Hospital Pediatrics was the most common journal for abstracts accepted for poster presentation, representing 44.9% of the published abstracts. Rejected abstracts were subsequently published in a range of journals, including Clinical Pediatrics, Advances in Preventative Medicine, and Ethnicity & Disease (Table 3).

 

 

 

DISCUSSION

About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.

Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.

Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.

Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.

The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.

This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.

 

 

CONCLUSIONS

Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.

Disclosure

Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.

References

1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed

References

1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed

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Lisa E. Herrmann, MD, MEd, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 9016, Cincinnati, OH 45229; Telephone: 513-803-4257; Fax: 513-803-9244; E-mail: lisa.herrmann@cchmc.org
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Regional Variation in Standardized Costs of Care at Children’s Hospitals

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With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

METHODS

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

 

 

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

RESULTS

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

 

 

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

DISCUSSION

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

 

 

CONCLUSION

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

References

1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
2. Rau J. IOM Finds Differences In Regional Health Spending Are Linked To Post-Hospital Care And Provider Prices. Washington, DC: Kaiser Health News; 2013. http://www.kaiserhealthnews.org/stories/2013/july/24/iom-report-on-geographic-variations-in-health-care-spending.aspx. Accessed on April 11, 2014.
3. Radnofsky L. Health-Care Costs: A State-by-State Comparison. The Wall Street Journal. April 8, 2013.
4. Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices. New Engl J Med. 2010;363(1):45-53. PubMed
5. Reschovsky JD, Hadley J, O’Malley AJ, Landon BE. Geographic Variations in the Cost of Treating Condition-Specific Episodes of Care among Medicare Patients. Health Serv Res. 2014;49:32-51. PubMed
6. Ashton CM, Petersen NJ, Souchek J, et al. Geographic variations in utilization rates in Veterans Affairs hospitals and clinics. New Engl J Med. 1999;340(1):32-39. PubMed
7. Newhouse JP, Garber AM. Geographic variation in health care spending in the United States: insights from an Institute of Medicine report. JAMA. 2013;310(12):1227-1228. PubMed
8. Wennberg JE. Practice variation: implications for our health care system. Manag Care. 2004;13(9 Suppl):3-7. PubMed
9. Wennberg J. Wrestling with variation: an interview with Jack Wennberg [interviewed by Fitzhugh Mullan]. Health Aff. 2004;Suppl Variation:VAR73-80. PubMed
10. Sirovich B, Gallagher PM, Wennberg DE, Fisher ES. Discretionary decision making by primary care physicians and the cost of U.S. health care. Health Aff. 2008;27(3):813-823. PubMed
11. Wennberg J, Gittelsohn. Small area variations in health care delivery. Science. 1973;182(4117):1102-1108. PubMed
12. Cooper RA. Geographic variation in health care and the affluence-poverty nexus. Adv Surg. 2011;45:63-82. PubMed
13. Cooper RA, Cooper MA, McGinley EL, Fan X, Rosenthal JT. Poverty, wealth, and health care utilization: a geographic assessment. J Urban Health. 2012;89(5):828-847. PubMed
14. L Sheiner. Why the Geographic Variation in Health Care Spending Can’t Tell Us Much about the Efficiency or Quality of our Health Care System. Finance and Economics Discussion Series: Division of Research & Statistics and Monetary Affairs. Washington, DC: United States Federal Reserve; 2013.
15. Keren R, Luan X, Localio R, et al. Prioritization of comparative effectiveness research topics in hospital pediatrics. Arch Pediatr Adolesc Med. 2012;166(12):1155-1164. PubMed
16. Lagu T, Krumholz HM, Dharmarajan K, et al. Spending more, doing more, or both? An alternative method for quantifying utilization during hospitalizations. J Hosp Med. 2013;8(7):373-379. PubMed
17. Silber JH, Rosenbaum PR, Wang W, et al. Auditing practice style variation in pediatric inpatient asthma care. JAMA Pediatr. 2016;170(9):878-886. PubMed
18. 3M Health Information Systems. All Patient Refined Diagnosis Related Groups (APR DRGs), Version 24.0 - Methodology Overview. 2007; https://www.hcup-us.ahrq.gov/db/nation/nis/v24_aprdrg_meth_ovrview.pdf. Accessed on March 19, 2017.
19. Tieder JS, McLeod L, Keren R, et al. Variation in resource use and readmission for diabetic ketoacidosis in children’s hospitals. Pediatrics. 2013;132(2):229-236. PubMed
20. Larson K, Halfon N. Family income gradients in the health and health care access of US children. Matern Child Health J. 2010;14(3):332-342. PubMed
21. Simpson L, Owens PL, Zodet MW, et al. Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by income. Ambul Pediatr. 2005;5(1):6-44. PubMed
22. US Department of Health and Human Services. 2015 Poverty Guidelines. https://aspe.hhs.gov/2015-poverty-guidelines Accessed on April 19, 2016.
23. Morrill R, Cromartie J, Hart LG. Metropolitan, urban, and rural commuting areas: toward a better depiction of the US settlement system. Urban Geogr. 1999;20:727-748. 
24. Welch HG, Larson EB, Welch WP. Could distance be a proxy for severity-of-illness? A comparison of hospital costs in distant and local patients. Health Serv Res. 1993;28(4):441-458. PubMed
25. HCUP Chronic Condition Indicator (CCI) for ICD-9-CM. Healthcare Cost and Utilization Project (HCUP). https://www.hcup-us.ahrq.gov/toolssoftware/chronic/chronic.jsp Accessed on May 2016.
26. United States Census Bureau. Geographic Terms and Concepts - Census Divisions and Census Regions. https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html Accessed on May 2016.
27. Marazzi A, Ruffieux C. The truncated mean of an asymmetric distribution. Comput Stat Data Anal. 1999;32(1):70-100. 
28. Tsugawa Y, Jha AK, Newhouse JP, Zaslavsky AM, Jena AB. Variation in Physician Spending and Association With Patient Outcomes. JAMA Intern Med. 2017;177:675-682. PubMed
29. Parikh K, Hall M, Mittal V, et al. Establishing benchmarks for the hospitalized care of children with asthma, bronchiolitis, and pneumonia. Pediatrics. 2014;134(3):555-562. PubMed
30. James BC, Savitz LA. How Intermountain trimmed health care costs through robust quality improvement efforts. Health Aff. 2011;30(6):1185-1191. PubMed
31. Lind CH, Hall M, Arnold DH, et al. Variation in Diagnostic Testing and Hospitalization Rates in Children With Acute Gastroenteritis. Hosp Pediatr. 2016;6(12):714-721. PubMed
32. Kenyon CC, Fieldston ES, Luan X, Keren R, Zorc JJ. Safety and effectiveness of continuous aerosolized albuterol in the non-intensive care setting. Pediatrics. 2014;134(4):e976-e982. PubMed

33. Morgan-Trimmer S, Channon S, Gregory JW, Townson J, Lowes L. Family preferences for home or hospital care at diagnosis for children with diabetes in the DECIDE study. Diabet Med. 2016;33(1):119-124. PubMed
34. Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393. PubMed
35. Seifer SD, Vranizan K, Grumbach K. Graduate medical education and physician practice location. Implications for physician workforce policy. JAMA. 1995;274(9):685-691. PubMed
36. Association of American Medical Colleges (AAMC). Table C4. Physician Retention in State of Residency Training, by Last Completed GME Specialty. 2015; https://www.aamc.org/data/448492/c4table.html. Accessed on August 2016.
37. Fieldston ES, Zaniletti I, Hall M, et al. Community household income and resource utilization for common inpatient pediatric conditions. Pediatrics. 2013;132(6):e1592-e1601. PubMed
38. Agency for Healthcare Research and Quality HCUPnet. National estimates on use of hospitals by children from the HCUP Kids’ Inpatient Database (KID). 2012; http://hcupnet.ahrq.gov/HCUPnet.jsp?Id=02768E67C1CB77A2&Form=DispTab&JS=Y&Action=Accept. Accessed on August 2016.
39. Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879-2888. PubMed

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With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

METHODS

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

 

 

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

RESULTS

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

 

 

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

DISCUSSION

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

 

 

CONCLUSION

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

METHODS

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

 

 

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

RESULTS

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

 

 

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

DISCUSSION

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

 

 

CONCLUSION

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

References

1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
2. Rau J. IOM Finds Differences In Regional Health Spending Are Linked To Post-Hospital Care And Provider Prices. Washington, DC: Kaiser Health News; 2013. http://www.kaiserhealthnews.org/stories/2013/july/24/iom-report-on-geographic-variations-in-health-care-spending.aspx. Accessed on April 11, 2014.
3. Radnofsky L. Health-Care Costs: A State-by-State Comparison. The Wall Street Journal. April 8, 2013.
4. Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices. New Engl J Med. 2010;363(1):45-53. PubMed
5. Reschovsky JD, Hadley J, O’Malley AJ, Landon BE. Geographic Variations in the Cost of Treating Condition-Specific Episodes of Care among Medicare Patients. Health Serv Res. 2014;49:32-51. PubMed
6. Ashton CM, Petersen NJ, Souchek J, et al. Geographic variations in utilization rates in Veterans Affairs hospitals and clinics. New Engl J Med. 1999;340(1):32-39. PubMed
7. Newhouse JP, Garber AM. Geographic variation in health care spending in the United States: insights from an Institute of Medicine report. JAMA. 2013;310(12):1227-1228. PubMed
8. Wennberg JE. Practice variation: implications for our health care system. Manag Care. 2004;13(9 Suppl):3-7. PubMed
9. Wennberg J. Wrestling with variation: an interview with Jack Wennberg [interviewed by Fitzhugh Mullan]. Health Aff. 2004;Suppl Variation:VAR73-80. PubMed
10. Sirovich B, Gallagher PM, Wennberg DE, Fisher ES. Discretionary decision making by primary care physicians and the cost of U.S. health care. Health Aff. 2008;27(3):813-823. PubMed
11. Wennberg J, Gittelsohn. Small area variations in health care delivery. Science. 1973;182(4117):1102-1108. PubMed
12. Cooper RA. Geographic variation in health care and the affluence-poverty nexus. Adv Surg. 2011;45:63-82. PubMed
13. Cooper RA, Cooper MA, McGinley EL, Fan X, Rosenthal JT. Poverty, wealth, and health care utilization: a geographic assessment. J Urban Health. 2012;89(5):828-847. PubMed
14. L Sheiner. Why the Geographic Variation in Health Care Spending Can’t Tell Us Much about the Efficiency or Quality of our Health Care System. Finance and Economics Discussion Series: Division of Research & Statistics and Monetary Affairs. Washington, DC: United States Federal Reserve; 2013.
15. Keren R, Luan X, Localio R, et al. Prioritization of comparative effectiveness research topics in hospital pediatrics. Arch Pediatr Adolesc Med. 2012;166(12):1155-1164. PubMed
16. Lagu T, Krumholz HM, Dharmarajan K, et al. Spending more, doing more, or both? An alternative method for quantifying utilization during hospitalizations. J Hosp Med. 2013;8(7):373-379. PubMed
17. Silber JH, Rosenbaum PR, Wang W, et al. Auditing practice style variation in pediatric inpatient asthma care. JAMA Pediatr. 2016;170(9):878-886. PubMed
18. 3M Health Information Systems. All Patient Refined Diagnosis Related Groups (APR DRGs), Version 24.0 - Methodology Overview. 2007; https://www.hcup-us.ahrq.gov/db/nation/nis/v24_aprdrg_meth_ovrview.pdf. Accessed on March 19, 2017.
19. Tieder JS, McLeod L, Keren R, et al. Variation in resource use and readmission for diabetic ketoacidosis in children’s hospitals. Pediatrics. 2013;132(2):229-236. PubMed
20. Larson K, Halfon N. Family income gradients in the health and health care access of US children. Matern Child Health J. 2010;14(3):332-342. PubMed
21. Simpson L, Owens PL, Zodet MW, et al. Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by income. Ambul Pediatr. 2005;5(1):6-44. PubMed
22. US Department of Health and Human Services. 2015 Poverty Guidelines. https://aspe.hhs.gov/2015-poverty-guidelines Accessed on April 19, 2016.
23. Morrill R, Cromartie J, Hart LG. Metropolitan, urban, and rural commuting areas: toward a better depiction of the US settlement system. Urban Geogr. 1999;20:727-748. 
24. Welch HG, Larson EB, Welch WP. Could distance be a proxy for severity-of-illness? A comparison of hospital costs in distant and local patients. Health Serv Res. 1993;28(4):441-458. PubMed
25. HCUP Chronic Condition Indicator (CCI) for ICD-9-CM. Healthcare Cost and Utilization Project (HCUP). https://www.hcup-us.ahrq.gov/toolssoftware/chronic/chronic.jsp Accessed on May 2016.
26. United States Census Bureau. Geographic Terms and Concepts - Census Divisions and Census Regions. https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html Accessed on May 2016.
27. Marazzi A, Ruffieux C. The truncated mean of an asymmetric distribution. Comput Stat Data Anal. 1999;32(1):70-100. 
28. Tsugawa Y, Jha AK, Newhouse JP, Zaslavsky AM, Jena AB. Variation in Physician Spending and Association With Patient Outcomes. JAMA Intern Med. 2017;177:675-682. PubMed
29. Parikh K, Hall M, Mittal V, et al. Establishing benchmarks for the hospitalized care of children with asthma, bronchiolitis, and pneumonia. Pediatrics. 2014;134(3):555-562. PubMed
30. James BC, Savitz LA. How Intermountain trimmed health care costs through robust quality improvement efforts. Health Aff. 2011;30(6):1185-1191. PubMed
31. Lind CH, Hall M, Arnold DH, et al. Variation in Diagnostic Testing and Hospitalization Rates in Children With Acute Gastroenteritis. Hosp Pediatr. 2016;6(12):714-721. PubMed
32. Kenyon CC, Fieldston ES, Luan X, Keren R, Zorc JJ. Safety and effectiveness of continuous aerosolized albuterol in the non-intensive care setting. Pediatrics. 2014;134(4):e976-e982. PubMed

33. Morgan-Trimmer S, Channon S, Gregory JW, Townson J, Lowes L. Family preferences for home or hospital care at diagnosis for children with diabetes in the DECIDE study. Diabet Med. 2016;33(1):119-124. PubMed
34. Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393. PubMed
35. Seifer SD, Vranizan K, Grumbach K. Graduate medical education and physician practice location. Implications for physician workforce policy. JAMA. 1995;274(9):685-691. PubMed
36. Association of American Medical Colleges (AAMC). Table C4. Physician Retention in State of Residency Training, by Last Completed GME Specialty. 2015; https://www.aamc.org/data/448492/c4table.html. Accessed on August 2016.
37. Fieldston ES, Zaniletti I, Hall M, et al. Community household income and resource utilization for common inpatient pediatric conditions. Pediatrics. 2013;132(6):e1592-e1601. PubMed
38. Agency for Healthcare Research and Quality HCUPnet. National estimates on use of hospitals by children from the HCUP Kids’ Inpatient Database (KID). 2012; http://hcupnet.ahrq.gov/HCUPnet.jsp?Id=02768E67C1CB77A2&Form=DispTab&JS=Y&Action=Accept. Accessed on August 2016.
39. Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879-2888. PubMed

References

1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
2. Rau J. IOM Finds Differences In Regional Health Spending Are Linked To Post-Hospital Care And Provider Prices. Washington, DC: Kaiser Health News; 2013. http://www.kaiserhealthnews.org/stories/2013/july/24/iom-report-on-geographic-variations-in-health-care-spending.aspx. Accessed on April 11, 2014.
3. Radnofsky L. Health-Care Costs: A State-by-State Comparison. The Wall Street Journal. April 8, 2013.
4. Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices. New Engl J Med. 2010;363(1):45-53. PubMed
5. Reschovsky JD, Hadley J, O’Malley AJ, Landon BE. Geographic Variations in the Cost of Treating Condition-Specific Episodes of Care among Medicare Patients. Health Serv Res. 2014;49:32-51. PubMed
6. Ashton CM, Petersen NJ, Souchek J, et al. Geographic variations in utilization rates in Veterans Affairs hospitals and clinics. New Engl J Med. 1999;340(1):32-39. PubMed
7. Newhouse JP, Garber AM. Geographic variation in health care spending in the United States: insights from an Institute of Medicine report. JAMA. 2013;310(12):1227-1228. PubMed
8. Wennberg JE. Practice variation: implications for our health care system. Manag Care. 2004;13(9 Suppl):3-7. PubMed
9. Wennberg J. Wrestling with variation: an interview with Jack Wennberg [interviewed by Fitzhugh Mullan]. Health Aff. 2004;Suppl Variation:VAR73-80. PubMed
10. Sirovich B, Gallagher PM, Wennberg DE, Fisher ES. Discretionary decision making by primary care physicians and the cost of U.S. health care. Health Aff. 2008;27(3):813-823. PubMed
11. Wennberg J, Gittelsohn. Small area variations in health care delivery. Science. 1973;182(4117):1102-1108. PubMed
12. Cooper RA. Geographic variation in health care and the affluence-poverty nexus. Adv Surg. 2011;45:63-82. PubMed
13. Cooper RA, Cooper MA, McGinley EL, Fan X, Rosenthal JT. Poverty, wealth, and health care utilization: a geographic assessment. J Urban Health. 2012;89(5):828-847. PubMed
14. L Sheiner. Why the Geographic Variation in Health Care Spending Can’t Tell Us Much about the Efficiency or Quality of our Health Care System. Finance and Economics Discussion Series: Division of Research & Statistics and Monetary Affairs. Washington, DC: United States Federal Reserve; 2013.
15. Keren R, Luan X, Localio R, et al. Prioritization of comparative effectiveness research topics in hospital pediatrics. Arch Pediatr Adolesc Med. 2012;166(12):1155-1164. PubMed
16. Lagu T, Krumholz HM, Dharmarajan K, et al. Spending more, doing more, or both? An alternative method for quantifying utilization during hospitalizations. J Hosp Med. 2013;8(7):373-379. PubMed
17. Silber JH, Rosenbaum PR, Wang W, et al. Auditing practice style variation in pediatric inpatient asthma care. JAMA Pediatr. 2016;170(9):878-886. PubMed
18. 3M Health Information Systems. All Patient Refined Diagnosis Related Groups (APR DRGs), Version 24.0 - Methodology Overview. 2007; https://www.hcup-us.ahrq.gov/db/nation/nis/v24_aprdrg_meth_ovrview.pdf. Accessed on March 19, 2017.
19. Tieder JS, McLeod L, Keren R, et al. Variation in resource use and readmission for diabetic ketoacidosis in children’s hospitals. Pediatrics. 2013;132(2):229-236. PubMed
20. Larson K, Halfon N. Family income gradients in the health and health care access of US children. Matern Child Health J. 2010;14(3):332-342. PubMed
21. Simpson L, Owens PL, Zodet MW, et al. Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by income. Ambul Pediatr. 2005;5(1):6-44. PubMed
22. US Department of Health and Human Services. 2015 Poverty Guidelines. https://aspe.hhs.gov/2015-poverty-guidelines Accessed on April 19, 2016.
23. Morrill R, Cromartie J, Hart LG. Metropolitan, urban, and rural commuting areas: toward a better depiction of the US settlement system. Urban Geogr. 1999;20:727-748. 
24. Welch HG, Larson EB, Welch WP. Could distance be a proxy for severity-of-illness? A comparison of hospital costs in distant and local patients. Health Serv Res. 1993;28(4):441-458. PubMed
25. HCUP Chronic Condition Indicator (CCI) for ICD-9-CM. Healthcare Cost and Utilization Project (HCUP). https://www.hcup-us.ahrq.gov/toolssoftware/chronic/chronic.jsp Accessed on May 2016.
26. United States Census Bureau. Geographic Terms and Concepts - Census Divisions and Census Regions. https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html Accessed on May 2016.
27. Marazzi A, Ruffieux C. The truncated mean of an asymmetric distribution. Comput Stat Data Anal. 1999;32(1):70-100. 
28. Tsugawa Y, Jha AK, Newhouse JP, Zaslavsky AM, Jena AB. Variation in Physician Spending and Association With Patient Outcomes. JAMA Intern Med. 2017;177:675-682. PubMed
29. Parikh K, Hall M, Mittal V, et al. Establishing benchmarks for the hospitalized care of children with asthma, bronchiolitis, and pneumonia. Pediatrics. 2014;134(3):555-562. PubMed
30. James BC, Savitz LA. How Intermountain trimmed health care costs through robust quality improvement efforts. Health Aff. 2011;30(6):1185-1191. PubMed
31. Lind CH, Hall M, Arnold DH, et al. Variation in Diagnostic Testing and Hospitalization Rates in Children With Acute Gastroenteritis. Hosp Pediatr. 2016;6(12):714-721. PubMed
32. Kenyon CC, Fieldston ES, Luan X, Keren R, Zorc JJ. Safety and effectiveness of continuous aerosolized albuterol in the non-intensive care setting. Pediatrics. 2014;134(4):e976-e982. PubMed

33. Morgan-Trimmer S, Channon S, Gregory JW, Townson J, Lowes L. Family preferences for home or hospital care at diagnosis for children with diabetes in the DECIDE study. Diabet Med. 2016;33(1):119-124. PubMed
34. Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393. PubMed
35. Seifer SD, Vranizan K, Grumbach K. Graduate medical education and physician practice location. Implications for physician workforce policy. JAMA. 1995;274(9):685-691. PubMed
36. Association of American Medical Colleges (AAMC). Table C4. Physician Retention in State of Residency Training, by Last Completed GME Specialty. 2015; https://www.aamc.org/data/448492/c4table.html. Accessed on August 2016.
37. Fieldston ES, Zaniletti I, Hall M, et al. Community household income and resource utilization for common inpatient pediatric conditions. Pediatrics. 2013;132(6):e1592-e1601. PubMed
38. Agency for Healthcare Research and Quality HCUPnet. National estimates on use of hospitals by children from the HCUP Kids’ Inpatient Database (KID). 2012; http://hcupnet.ahrq.gov/HCUPnet.jsp?Id=02768E67C1CB77A2&Form=DispTab&JS=Y&Action=Accept. Accessed on August 2016.
39. Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879-2888. PubMed

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Evan S. Fieldston, MD, MBA, MSHP, Department of Pediatrics, The Children’s Hospital of Philadelphia, 34th & Civic Center Blvd, Philadelphia, PA 19104; Telephone: 267-426-2903; Fax: 267-426-6665; E-mail: fieldston@email.chop.edu
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LOS in Children With Medical Complexity

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Long length of hospital stay in children with medical complexity

Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

Study Design and Data Source

This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.

Study Population

Inclusion Criteria

Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.

As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.

Exclusion Criteria

We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).

Outcome Measures

The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.

Patient Demographics and Clinical Characteristics

We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]

Hospitalization Characteristics

Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.

Statistical Analysis

Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.

We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*
Characteristic Overall (n = 954,018) Length of Stay
<10 Days (n = 811,936) 10 Days (n = 142,082)
  • NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.

Age at admission, y, %
<1 14.6 12.7 25.7
14 27.1 27.9 22.4
59 20.1 21.0 14.9
1018 33.6 34.0 31.7
18+ 4.6 4.4 5.4
Gender, %
Female 47.0 46.9 47.5
Race/ethnicity, %
Non‐Hispanic white 49.4 49.4 49.4
Non‐Hispanic black 23.1 23.8 19.3
Hispanic 18.2 17.8 20.4
Asian 2.0 1.9 2.3
Other 7.4 7.1 8.6
Complex chronic condition, %
Any 79.5 77.3 91.8
Technology assistance 37.1 34.1 54.2
Gastrointestinal 30.0 27.2 45.9
Neuromuscular 28.2 27.7 30.9
Cardiovascular 16.8 14.5 29.9
Respiratory 14.1 11.5 29.4
Congenital/genetic defect 17.2 16.7 20.2
Metabolic 9.9 8.9 15.4
Renal 10.1 9.5 13.8
Hematology/emmmunodeficiency 11.7 12.0 10.0
Neonatal 3.8 3.1 7.7
Transplantation 4.5 4.2 6.7
Clinical risk group, %
Chronic condition in 2 systems 68.4 71.2 53.9
Catastrophic chronic condition 31.4 28.8 46.1
Distance from hospital to home residence in miles, median [IQR] 16.2 [7.440.4] 15.8 [7.338.7] 19.1 [8.552.6]
Transferred from outside hospital (%) 6.5 5.3 13.6
Admitted for surgery, % 23.4 20.7 38.7
Use of intensive care, % 19.6 14.9 46.5
Discharge disposition, %
Home 91.2 92.9 81.4
Home healthcare 4.5 3.5 9.9
Other 2.9 2.6 4.5
Postacute care facility 1.1 0.8 3.1
Died 0.4 0.3 1.1
Payor, %
Government 61.1 60.6 63.5
Private 33.2 33.6 30.9
Other 5.7 5.7 5.7
Hospital resource use
Median length of stay [IQR] 3 [16] 2 [14] 16 [1226]
Median hospital cost [IQR] $8,144 [$4,122$18,447] $6,689 [$3,685$12,395] $49,207 [$29,444$95,738]
Total hospital cost, $, billions $22.2 $8.5 $13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days
  • NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.

Most common reason for admission*
Cystic fibrosis 10.7%
Respiratory system diagnosis with ventilator support 96+ hours 5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure 2.8%
Craniotomy except for trauma 2.6%
Major small and large bowel procedures 2.3%
Most common complex chronic condition
Gastrointestinal devices 39.7%
Heart and great vessel malformations 12.8%
Cystic fibrosis 12.5%
Dysrhythmias 11.0%
Respiratory devices 10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days
Characteristic Odds Ratio (95% CI) of LOS 10 Days P Value
  • NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.

Use of intensive care 3.5 (3.4‐3.5) <0.001
Transfer from another acute‐care hospital 2.1 (2.0‐2.1) <0.001
Procedure/surgery 1.8 (1.8‐1.9) <0.001
Complex chronic condition
Respiratory 2.7 (2.6‐2.7) <0.001
Gastrointestinal 1.8 (1.8‐1.8) <0.001
Metabolic 1.7 (1.7‐1.7) <0.001
Cardiovascular 1.6 (1.5‐1.6) <0.001
Neonatal 1.5 (1.5‐1.5) <0.001
Renal 1.4 (1.4‐1.4) <0.001
Transplant 1.4 (1.4‐1.4) <0.001
Hematology and immunodeficiency 1.3 (1.3‐1.3) <0.001
Technology assistance 1.1 (1.1, 1.1) <0.001
Neuromuscular 0.9 (0.9‐0.9) <0.001
Congenital or genetic defect 0.8 (0.8‐0.8) <0.001
Age at admission, y
<1 1.2 (1.2‐1.3) <0.001
14 0.5 (0.5‐0.5) <0.001
59 0.6 (0.6‐0.6) <0.001
1018 0.9 (0.9‐0.9) <0.001
18+ Reference
Male 0.9 (0.9‐0.9) <0.001
Race/ethnicity
Non‐Hispanic black 0.9 (0.9‐0.9) <0.001
Hispanic 1.1 (1.0‐1.1) <0.001
Asian 1.0 (1.0‐1.1) 0.3
Other 1.1 (1.1‐1.1) <0.001
Non‐Hispanic white Reference
Payor
Private 0.9 (0.8 0.9) <0.001
Other 1.0 (1.0‐1.0) 0.4
Government Reference
Season
Spring 1.0 (1.0 1.0) <0.001
Summer 0.9 (0.9‐0.9) <0.001
Fall 1.0 (0.9‐1.0) <0.001
Winter Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1
Variation in the Prevalence and Cost of Long Length of Stay ≥10 days for Children with Medical Complexity Across Children's Hospitals. Presented from the left y‐axis are the adjusted percentages (with 95% confidence interval)—shown as circles and whiskers—of total admissions for complex chronic condition (CMC) with length of stay (LOS) ≥10 days across 44 freestanding children's hospitals. The percentages are adjusted for demographic, clinical, and hospitalization characteristics associated with the likelihood of CMC experiencing LOS ≥10 days. The dashed line indicates the mean percentage (15%) across all hospitals. Also presented on the right y‐axis are the percentages—shown as gray bars—of all hospital charges attributable to hospitalizations ≥10 days among CMC across children's hospitals.

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.

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  19. O'Mahony L, O'Mahony DS, Simon TD, Neff J, Klein EJ, Quan L. Medical complexity and pediatric emergency department and inpatient utilization. Pediatrics. 2013;131(2):e559e565.
  20. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
  21. Weissman C. Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med. 1997;25(9):15941600.
  22. Berry JG, Hall DE, Kuo DZ, et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals. JAMA. 2011;305(7):682690.
  23. Hudson SM. Hospital readmissions and repeat emergency department visits among children with medical complexity: an integrative review. J Pediatr Nurs. 2013;28(4):316339.
  24. Jurgens V, Spaeder MC, Pavuluri P, Waldman Z. Hospital readmission in children with complex chronic conditions discharged from subacute care. Hosp Pediatr. 2014;4(3):153158.
  25. Coller RJ, Nelson BB, Sklansky DJ, et al. Preventing hospitalizations in children with medical complexity: a systematic review. Pediatrics. 2014;134(6):e1628e1647.
  26. Kun SS, Edwards JD, Ward SLD, Keens TG. Hospital readmissions for newly discharged pediatric home mechanical ventilation patients. Pediatr Pulmonol. 2012;47(4):409414.
  27. Cram P, Lu X, Kaboli PJ, et al. Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA. 2011;305(15):15601567.
  28. Bueno H, Ross JS, Wang Y, et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303(21):21412147.
  29. U.S. Department of Health and Human Services. CMS Statistics 2013. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/CMS‐Statistics‐Reference‐Booklet/Downloads/CMS_Stats_2013_final.pdf. Published August 2013. Accessed October 6, 2015.
  30. Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
  31. Berry JG, Blaine K, Rogers J, et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955962; quiz 965–966.
  32. Brittan M, Albright K, Cifuentes M, Jimenez‐Zambrano A, Kempe A. Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559565.
  33. Berry JG, Gay JC. Preventing readmissions in children: how do we do that? Hosp Pediatr. 2015;5(11):602604.
  34. O'Brien JE, Berry J, Dumas H. Pediatric post‐acute hospital care: striving for identity and value. Hosp Pediatr. 2015;5(10):548551.
  35. Berry JG, Hall M, Dumas H, et al. Pediatric hospital discharges to home health and postacute facility care: a national study. JAMA Pediatr. 2016;170(4):326333.
  36. Corneli HM, Zorc JJ, Holubkov R, et al. Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99103.
  37. Myers J, Smith M, Woods C, Espinosa C, Lehna C. The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178183.
  38. Stroud MH, Sanders RC, Moss MM, et al. Goal‐directed resuscitative interventions during pediatric interfacility transport. Crit Care Med. 2015;43(8):16921698.
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Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

Study Design and Data Source

This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.

Study Population

Inclusion Criteria

Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.

As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.

Exclusion Criteria

We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).

Outcome Measures

The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.

Patient Demographics and Clinical Characteristics

We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]

Hospitalization Characteristics

Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.

Statistical Analysis

Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.

We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*
Characteristic Overall (n = 954,018) Length of Stay
<10 Days (n = 811,936) 10 Days (n = 142,082)
  • NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.

Age at admission, y, %
<1 14.6 12.7 25.7
14 27.1 27.9 22.4
59 20.1 21.0 14.9
1018 33.6 34.0 31.7
18+ 4.6 4.4 5.4
Gender, %
Female 47.0 46.9 47.5
Race/ethnicity, %
Non‐Hispanic white 49.4 49.4 49.4
Non‐Hispanic black 23.1 23.8 19.3
Hispanic 18.2 17.8 20.4
Asian 2.0 1.9 2.3
Other 7.4 7.1 8.6
Complex chronic condition, %
Any 79.5 77.3 91.8
Technology assistance 37.1 34.1 54.2
Gastrointestinal 30.0 27.2 45.9
Neuromuscular 28.2 27.7 30.9
Cardiovascular 16.8 14.5 29.9
Respiratory 14.1 11.5 29.4
Congenital/genetic defect 17.2 16.7 20.2
Metabolic 9.9 8.9 15.4
Renal 10.1 9.5 13.8
Hematology/emmmunodeficiency 11.7 12.0 10.0
Neonatal 3.8 3.1 7.7
Transplantation 4.5 4.2 6.7
Clinical risk group, %
Chronic condition in 2 systems 68.4 71.2 53.9
Catastrophic chronic condition 31.4 28.8 46.1
Distance from hospital to home residence in miles, median [IQR] 16.2 [7.440.4] 15.8 [7.338.7] 19.1 [8.552.6]
Transferred from outside hospital (%) 6.5 5.3 13.6
Admitted for surgery, % 23.4 20.7 38.7
Use of intensive care, % 19.6 14.9 46.5
Discharge disposition, %
Home 91.2 92.9 81.4
Home healthcare 4.5 3.5 9.9
Other 2.9 2.6 4.5
Postacute care facility 1.1 0.8 3.1
Died 0.4 0.3 1.1
Payor, %
Government 61.1 60.6 63.5
Private 33.2 33.6 30.9
Other 5.7 5.7 5.7
Hospital resource use
Median length of stay [IQR] 3 [16] 2 [14] 16 [1226]
Median hospital cost [IQR] $8,144 [$4,122$18,447] $6,689 [$3,685$12,395] $49,207 [$29,444$95,738]
Total hospital cost, $, billions $22.2 $8.5 $13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days
  • NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.

Most common reason for admission*
Cystic fibrosis 10.7%
Respiratory system diagnosis with ventilator support 96+ hours 5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure 2.8%
Craniotomy except for trauma 2.6%
Major small and large bowel procedures 2.3%
Most common complex chronic condition
Gastrointestinal devices 39.7%
Heart and great vessel malformations 12.8%
Cystic fibrosis 12.5%
Dysrhythmias 11.0%
Respiratory devices 10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days
Characteristic Odds Ratio (95% CI) of LOS 10 Days P Value
  • NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.

Use of intensive care 3.5 (3.4‐3.5) <0.001
Transfer from another acute‐care hospital 2.1 (2.0‐2.1) <0.001
Procedure/surgery 1.8 (1.8‐1.9) <0.001
Complex chronic condition
Respiratory 2.7 (2.6‐2.7) <0.001
Gastrointestinal 1.8 (1.8‐1.8) <0.001
Metabolic 1.7 (1.7‐1.7) <0.001
Cardiovascular 1.6 (1.5‐1.6) <0.001
Neonatal 1.5 (1.5‐1.5) <0.001
Renal 1.4 (1.4‐1.4) <0.001
Transplant 1.4 (1.4‐1.4) <0.001
Hematology and immunodeficiency 1.3 (1.3‐1.3) <0.001
Technology assistance 1.1 (1.1, 1.1) <0.001
Neuromuscular 0.9 (0.9‐0.9) <0.001
Congenital or genetic defect 0.8 (0.8‐0.8) <0.001
Age at admission, y
<1 1.2 (1.2‐1.3) <0.001
14 0.5 (0.5‐0.5) <0.001
59 0.6 (0.6‐0.6) <0.001
1018 0.9 (0.9‐0.9) <0.001
18+ Reference
Male 0.9 (0.9‐0.9) <0.001
Race/ethnicity
Non‐Hispanic black 0.9 (0.9‐0.9) <0.001
Hispanic 1.1 (1.0‐1.1) <0.001
Asian 1.0 (1.0‐1.1) 0.3
Other 1.1 (1.1‐1.1) <0.001
Non‐Hispanic white Reference
Payor
Private 0.9 (0.8 0.9) <0.001
Other 1.0 (1.0‐1.0) 0.4
Government Reference
Season
Spring 1.0 (1.0 1.0) <0.001
Summer 0.9 (0.9‐0.9) <0.001
Fall 1.0 (0.9‐1.0) <0.001
Winter Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1
Variation in the Prevalence and Cost of Long Length of Stay ≥10 days for Children with Medical Complexity Across Children's Hospitals. Presented from the left y‐axis are the adjusted percentages (with 95% confidence interval)—shown as circles and whiskers—of total admissions for complex chronic condition (CMC) with length of stay (LOS) ≥10 days across 44 freestanding children's hospitals. The percentages are adjusted for demographic, clinical, and hospitalization characteristics associated with the likelihood of CMC experiencing LOS ≥10 days. The dashed line indicates the mean percentage (15%) across all hospitals. Also presented on the right y‐axis are the percentages—shown as gray bars—of all hospital charges attributable to hospitalizations ≥10 days among CMC across children's hospitals.

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.

Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

Study Design and Data Source

This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.

Study Population

Inclusion Criteria

Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.

As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.

Exclusion Criteria

We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).

Outcome Measures

The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.

Patient Demographics and Clinical Characteristics

We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]

Hospitalization Characteristics

Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.

Statistical Analysis

Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.

We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*
Characteristic Overall (n = 954,018) Length of Stay
<10 Days (n = 811,936) 10 Days (n = 142,082)
  • NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.

Age at admission, y, %
<1 14.6 12.7 25.7
14 27.1 27.9 22.4
59 20.1 21.0 14.9
1018 33.6 34.0 31.7
18+ 4.6 4.4 5.4
Gender, %
Female 47.0 46.9 47.5
Race/ethnicity, %
Non‐Hispanic white 49.4 49.4 49.4
Non‐Hispanic black 23.1 23.8 19.3
Hispanic 18.2 17.8 20.4
Asian 2.0 1.9 2.3
Other 7.4 7.1 8.6
Complex chronic condition, %
Any 79.5 77.3 91.8
Technology assistance 37.1 34.1 54.2
Gastrointestinal 30.0 27.2 45.9
Neuromuscular 28.2 27.7 30.9
Cardiovascular 16.8 14.5 29.9
Respiratory 14.1 11.5 29.4
Congenital/genetic defect 17.2 16.7 20.2
Metabolic 9.9 8.9 15.4
Renal 10.1 9.5 13.8
Hematology/emmmunodeficiency 11.7 12.0 10.0
Neonatal 3.8 3.1 7.7
Transplantation 4.5 4.2 6.7
Clinical risk group, %
Chronic condition in 2 systems 68.4 71.2 53.9
Catastrophic chronic condition 31.4 28.8 46.1
Distance from hospital to home residence in miles, median [IQR] 16.2 [7.440.4] 15.8 [7.338.7] 19.1 [8.552.6]
Transferred from outside hospital (%) 6.5 5.3 13.6
Admitted for surgery, % 23.4 20.7 38.7
Use of intensive care, % 19.6 14.9 46.5
Discharge disposition, %
Home 91.2 92.9 81.4
Home healthcare 4.5 3.5 9.9
Other 2.9 2.6 4.5
Postacute care facility 1.1 0.8 3.1
Died 0.4 0.3 1.1
Payor, %
Government 61.1 60.6 63.5
Private 33.2 33.6 30.9
Other 5.7 5.7 5.7
Hospital resource use
Median length of stay [IQR] 3 [16] 2 [14] 16 [1226]
Median hospital cost [IQR] $8,144 [$4,122$18,447] $6,689 [$3,685$12,395] $49,207 [$29,444$95,738]
Total hospital cost, $, billions $22.2 $8.5 $13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days
  • NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.

Most common reason for admission*
Cystic fibrosis 10.7%
Respiratory system diagnosis with ventilator support 96+ hours 5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure 2.8%
Craniotomy except for trauma 2.6%
Major small and large bowel procedures 2.3%
Most common complex chronic condition
Gastrointestinal devices 39.7%
Heart and great vessel malformations 12.8%
Cystic fibrosis 12.5%
Dysrhythmias 11.0%
Respiratory devices 10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days
Characteristic Odds Ratio (95% CI) of LOS 10 Days P Value
  • NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.

Use of intensive care 3.5 (3.4‐3.5) <0.001
Transfer from another acute‐care hospital 2.1 (2.0‐2.1) <0.001
Procedure/surgery 1.8 (1.8‐1.9) <0.001
Complex chronic condition
Respiratory 2.7 (2.6‐2.7) <0.001
Gastrointestinal 1.8 (1.8‐1.8) <0.001
Metabolic 1.7 (1.7‐1.7) <0.001
Cardiovascular 1.6 (1.5‐1.6) <0.001
Neonatal 1.5 (1.5‐1.5) <0.001
Renal 1.4 (1.4‐1.4) <0.001
Transplant 1.4 (1.4‐1.4) <0.001
Hematology and immunodeficiency 1.3 (1.3‐1.3) <0.001
Technology assistance 1.1 (1.1, 1.1) <0.001
Neuromuscular 0.9 (0.9‐0.9) <0.001
Congenital or genetic defect 0.8 (0.8‐0.8) <0.001
Age at admission, y
<1 1.2 (1.2‐1.3) <0.001
14 0.5 (0.5‐0.5) <0.001
59 0.6 (0.6‐0.6) <0.001
1018 0.9 (0.9‐0.9) <0.001
18+ Reference
Male 0.9 (0.9‐0.9) <0.001
Race/ethnicity
Non‐Hispanic black 0.9 (0.9‐0.9) <0.001
Hispanic 1.1 (1.0‐1.1) <0.001
Asian 1.0 (1.0‐1.1) 0.3
Other 1.1 (1.1‐1.1) <0.001
Non‐Hispanic white Reference
Payor
Private 0.9 (0.8 0.9) <0.001
Other 1.0 (1.0‐1.0) 0.4
Government Reference
Season
Spring 1.0 (1.0 1.0) <0.001
Summer 0.9 (0.9‐0.9) <0.001
Fall 1.0 (0.9‐1.0) <0.001
Winter Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1
Variation in the Prevalence and Cost of Long Length of Stay ≥10 days for Children with Medical Complexity Across Children's Hospitals. Presented from the left y‐axis are the adjusted percentages (with 95% confidence interval)—shown as circles and whiskers—of total admissions for complex chronic condition (CMC) with length of stay (LOS) ≥10 days across 44 freestanding children's hospitals. The percentages are adjusted for demographic, clinical, and hospitalization characteristics associated with the likelihood of CMC experiencing LOS ≥10 days. The dashed line indicates the mean percentage (15%) across all hospitals. Also presented on the right y‐axis are the percentages—shown as gray bars—of all hospital charges attributable to hospitalizations ≥10 days among CMC across children's hospitals.

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.

References
  1. Berry JG, Hall M, Hall DE, et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi‐institutional study. JAMA Pediatr. 2013;167(2):170177.
  2. Simon TD, Berry J, Feudtner C, et al. Children with complex chronic conditions in inpatient hospital settings in the united states. Pediatrics. 2010;126(4):647655.
  3. Clancy CM, Andresen EM. Meeting the health care needs of persons with disabilities. Milbank Q. 2002;80(2):381391.
  4. Mosquera RA, Avritscher EBC, Samuels CL, et al. Effect of an enhanced medical home on serious illness and cost of care among high‐risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):26402648.
  5. Berry JG, Hall M, Neff J, et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff Proj Hope. 2014;33(12):21992206.
  6. Children's Hospital Association. CARE Award. Available at: https://www.childrenshospitals.org/Programs‐and‐Services/Quality‐Improvement‐and‐Measurement/CARE‐Award. Accessed December 18, 2015.
  7. Berry JG, Ziniel SI, Freeman L, et al. Hospital readmission and parent perceptions of their child's hospital discharge. Int J Qual Health Care. 2013;25(5):573581.
  8. Fendler W, Baranowska‐Jazwiecka A, Hogendorf A, et al. Weekend matters: Friday and Saturday admissions are associated with prolonged hospitalization of children. Clin Pediatr (Phila). 2013;52(9):875878.
  9. Goudie A, Dynan L, Brady PW, Rettiganti M. Attributable cost and length of stay for central line‐associated bloodstream infections. Pediatrics. 2014;133(6):e1525e1532.
  10. Graves N, Weinhold D, Tong E, et al. Effect of healthcare‐acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol. 2007;28(3):280292.
  11. Hassan F, Lewis TC, Davis MM, Gebremariam A, Dombkowski K. Hospital utilization and costs among children with influenza, 2003. Am J Prev Med. 2009;36(4):292296.
  12. Kronman MP, Hall M, Slonim AD, Shah SS. Charges and lengths of stay attributable to adverse patient‐care events using pediatric‐specific quality indicators: a multicenter study of freestanding children's hospitals. Pediatrics. 2008;121(6):e1653e1659.
  13. Leyenaar JK, Lagu T, Shieh M‐S, Pekow PS, Lindenauer PK. Variation in resource utilization for the management of uncomplicated community‐acquired pneumonia across community and children's hospitals. J Pediatr. 2014;165(3):585591.
  14. Leyenaar JK, Shieh M‐S, Lagu T, Pekow PS, Lindenauer PK. Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. JAMA Pediatr. 2014;168(9):829836.
  15. Hughes JS, Averill RF, Eisenhandler J, et al. Clinical Risk Groups (CRGs): a classification system for risk‐adjusted capitation‐based payment and health care management. Med Care. 2004;42(1):8190.
  16. Neff JM, Clifton H, Park KJ, et al. Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data. Acad Pediatr. 2010;10(6):417423.
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  30. Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
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  32. Brittan M, Albright K, Cifuentes M, Jimenez‐Zambrano A, Kempe A. Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559565.
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  34. O'Brien JE, Berry J, Dumas H. Pediatric post‐acute hospital care: striving for identity and value. Hosp Pediatr. 2015;5(10):548551.
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  37. Myers J, Smith M, Woods C, Espinosa C, Lehna C. The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178183.
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References
  1. Berry JG, Hall M, Hall DE, et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi‐institutional study. JAMA Pediatr. 2013;167(2):170177.
  2. Simon TD, Berry J, Feudtner C, et al. Children with complex chronic conditions in inpatient hospital settings in the united states. Pediatrics. 2010;126(4):647655.
  3. Clancy CM, Andresen EM. Meeting the health care needs of persons with disabilities. Milbank Q. 2002;80(2):381391.
  4. Mosquera RA, Avritscher EBC, Samuels CL, et al. Effect of an enhanced medical home on serious illness and cost of care among high‐risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):26402648.
  5. Berry JG, Hall M, Neff J, et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff Proj Hope. 2014;33(12):21992206.
  6. Children's Hospital Association. CARE Award. Available at: https://www.childrenshospitals.org/Programs‐and‐Services/Quality‐Improvement‐and‐Measurement/CARE‐Award. Accessed December 18, 2015.
  7. Berry JG, Ziniel SI, Freeman L, et al. Hospital readmission and parent perceptions of their child's hospital discharge. Int J Qual Health Care. 2013;25(5):573581.
  8. Fendler W, Baranowska‐Jazwiecka A, Hogendorf A, et al. Weekend matters: Friday and Saturday admissions are associated with prolonged hospitalization of children. Clin Pediatr (Phila). 2013;52(9):875878.
  9. Goudie A, Dynan L, Brady PW, Rettiganti M. Attributable cost and length of stay for central line‐associated bloodstream infections. Pediatrics. 2014;133(6):e1525e1532.
  10. Graves N, Weinhold D, Tong E, et al. Effect of healthcare‐acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol. 2007;28(3):280292.
  11. Hassan F, Lewis TC, Davis MM, Gebremariam A, Dombkowski K. Hospital utilization and costs among children with influenza, 2003. Am J Prev Med. 2009;36(4):292296.
  12. Kronman MP, Hall M, Slonim AD, Shah SS. Charges and lengths of stay attributable to adverse patient‐care events using pediatric‐specific quality indicators: a multicenter study of freestanding children's hospitals. Pediatrics. 2008;121(6):e1653e1659.
  13. Leyenaar JK, Lagu T, Shieh M‐S, Pekow PS, Lindenauer PK. Variation in resource utilization for the management of uncomplicated community‐acquired pneumonia across community and children's hospitals. J Pediatr. 2014;165(3):585591.
  14. Leyenaar JK, Shieh M‐S, Lagu T, Pekow PS, Lindenauer PK. Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. JAMA Pediatr. 2014;168(9):829836.
  15. Hughes JS, Averill RF, Eisenhandler J, et al. Clinical Risk Groups (CRGs): a classification system for risk‐adjusted capitation‐based payment and health care management. Med Care. 2004;42(1):8190.
  16. Neff JM, Clifton H, Park KJ, et al. Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data. Acad Pediatr. 2010;10(6):417423.
  17. Neff JM, Sharp VL, Muldoon J, Graham J, Myers K. Profile of medical charges for children by health status group and severity level in a Washington State Health Plan. Health Serv Res. 2004;39(1):7389.
  18. Neff JM, Sharp VL, Popalisky J, Fitzgibbon T. Using medical billing data to evaluate chronically ill children over time. J Ambulatory Care Manage. 2006;29(4):283290.
  19. O'Mahony L, O'Mahony DS, Simon TD, Neff J, Klein EJ, Quan L. Medical complexity and pediatric emergency department and inpatient utilization. Pediatrics. 2013;131(2):e559e565.
  20. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
  21. Weissman C. Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med. 1997;25(9):15941600.
  22. Berry JG, Hall DE, Kuo DZ, et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals. JAMA. 2011;305(7):682690.
  23. Hudson SM. Hospital readmissions and repeat emergency department visits among children with medical complexity: an integrative review. J Pediatr Nurs. 2013;28(4):316339.
  24. Jurgens V, Spaeder MC, Pavuluri P, Waldman Z. Hospital readmission in children with complex chronic conditions discharged from subacute care. Hosp Pediatr. 2014;4(3):153158.
  25. Coller RJ, Nelson BB, Sklansky DJ, et al. Preventing hospitalizations in children with medical complexity: a systematic review. Pediatrics. 2014;134(6):e1628e1647.
  26. Kun SS, Edwards JD, Ward SLD, Keens TG. Hospital readmissions for newly discharged pediatric home mechanical ventilation patients. Pediatr Pulmonol. 2012;47(4):409414.
  27. Cram P, Lu X, Kaboli PJ, et al. Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA. 2011;305(15):15601567.
  28. Bueno H, Ross JS, Wang Y, et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303(21):21412147.
  29. U.S. Department of Health and Human Services. CMS Statistics 2013. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/CMS‐Statistics‐Reference‐Booklet/Downloads/CMS_Stats_2013_final.pdf. Published August 2013. Accessed October 6, 2015.
  30. Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
  31. Berry JG, Blaine K, Rogers J, et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955962; quiz 965–966.
  32. Brittan M, Albright K, Cifuentes M, Jimenez‐Zambrano A, Kempe A. Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559565.
  33. Berry JG, Gay JC. Preventing readmissions in children: how do we do that? Hosp Pediatr. 2015;5(11):602604.
  34. O'Brien JE, Berry J, Dumas H. Pediatric post‐acute hospital care: striving for identity and value. Hosp Pediatr. 2015;5(10):548551.
  35. Berry JG, Hall M, Dumas H, et al. Pediatric hospital discharges to home health and postacute facility care: a national study. JAMA Pediatr. 2016;170(4):326333.
  36. Corneli HM, Zorc JJ, Holubkov R, et al. Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99103.
  37. Myers J, Smith M, Woods C, Espinosa C, Lehna C. The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178183.
  38. Stroud MH, Sanders RC, Moss MM, et al. Goal‐directed resuscitative interventions during pediatric interfacility transport. Crit Care Med. 2015;43(8):16921698.
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Aggregate and hospital‐level impact of national guidelines on diagnostic resource utilization for children with pneumonia at children's hospitals

Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

Patient Population

Data from 32 (of the 47) hospitals included in PHIS with complete inpatient and ED data were used to evaluate hospital‐level resource utilization for children 1 to 18 years of age discharged January 1, 2008 to June 30, 2014 with a diagnosis of pneumonia (International Classification of Diseases, 9th Revision [ICD‐9] codes 480.x‐486.x, 487.0).[13] Our goal was to identify previously healthy children with uncomplicated pneumonia, so we excluded patients with complex chronic conditions,[14] billing charges for intensive care management and/or pleural drainage procedure (IDC‐9 codes 510.0, 510.9, 511.0, 511.1, 511.8, 511.9, 513.x) on day of admission or the next day, or prior pneumonia admission in the last 30 days. We studied 2 mutually exclusive populations: children with pneumonia treated in the ED (ie, patients who were evaluated in the ED and discharged to home), and children hospitalized with pneumonia, including those admitted through the ED.

Guideline Publication and Study Periods

For an exploratory before and after comparison, patients were grouped into 2 cohorts based on a guideline online publication date of August 1, 2011: preguideline (January 1, 2008 to July 31, 2011) and postguideline (August 1, 2011 to June 30, 2014).

Study Outcomes

The measured outcomes were the monthly proportion of pneumonia patients for whom specific diagnostic tests were performed, as determined from billing data. The diagnostic tests evaluated were complete blood count (CBC), blood culture, C‐reactive protein (CRP), and chest radiograph (CXR). Standardized costs were also calculated from PHIS charges as previously described to standardize the cost of the individual tests and remove interhospital cost variation.[3]

Relationship of Repeat Testing and Severity of Illness

Because higher illness severity and clinical deterioration may warrant repeat testing, we also explored the association of repeat diagnostic testing for inpatients with severity of illness by using the following variables as measures of severity: length of stay (LOS), transfer to intensive care unit (ICU), or pleural drainage procedure after admission (>2 calendar days after admission). Repeat diagnostic testing was stratified by number of tests.

Statistical Analysis

The categorical demographic characteristics of the pre‐ and postguideline populations were summarized using frequencies and percentages, and compared using 2 tests. Continuous demographics were summarized with medians and interquartile ranges (IQRs) and compared with the Wilcoxon rank sum test. Segmented regression, clustered by hospital, was used to assess trends in monthly resource utilization as well as associated standardized costs before and after guidelines publication. To estimate the impact of the guidelines overall, we compared the observed diagnostic resource use at the end of the study period with expected use projected from trends in the preguidelines period (ie, if there were no new guidelines). Individual interrupted time series were also built for each hospital. From these models, we assessed which hospitals had a significant difference between the rate observed at the end of the study and that estimated from their preguideline trajectory. To assess the relationship between the number of positive improvements at a hospital and hospital characteristics, we used Spearman's correlation and Kruskal‐Wallis tests. All analyses were performed with SAS version 9.3 (SAS Institute, Inc., Cary, NC), and P values <0.05 were considered statistically significant. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this research, using a deidentified dataset, was not considered human subjects research.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Patient Demographics
 OverallPreguidelinePostguidelineP
  • NOTE: Abbreviations: ED, emergency department; HHS, home health services; IQR, interquartile range; LOS, length of stay; SNF, skilled nursing facility.

No. of discharges220,539128,85591,684 
Type of encounter    
ED only150,215 (68.1)88,790 (68.9)61,425 (67)<0.001
Inpatient70,324 (31.9)40,065 (31.1)30,259 (33) 
Age    
14 years129,360 (58.7)77,802 (60.4)51,558 (56.2)<0.001
59 years58,609 (26.6)32,708 (25.4)25,901 (28.3) 
1018 years32,570 (14.8)18,345 (14.2)14,225 (15.5) 
Median [IQR]4 [27]3 [27]4 [27]<0.001
Gender    
Male116,718 (52.9)68,319 (53)48,399 (52.8)00.285
Female103,813 (47.1)60,532 (47)43,281 (47.2) 
Race    
Non‐Hispanic white84,423 (38.3)47,327 (36.7)37,096 (40.5)<0.001
Non‐Hispanic black60,062 (27.2)35,870 (27.8)24,192 (26.4) 
Hispanic51,184 (23.2)31,167 (24.2)20,017 (21.8) 
Asian6,444 (2.9)3,691 (2.9)2,753 (3) 
Other18,426 (8.4)10,800 (8.4)7,626 (8.3) 
Payer    
Government128,047 (58.1)70,742 (54.9)57,305 (62.5)<0.001
Private73,338 (33.3)44,410 (34.5)28,928 (31.6) 
Other19,154 (8.7)13,703 (10.6)5,451 (5.9) 
Disposition    
HHS684 (0.3)411 (0.3)273 (0.3)<0.001
Home209,710 (95.1)123,236 (95.6)86,474 (94.3) 
Other9,749 (4.4)4,962 (3.9)4,787 (5.2) 
SNF396 (0.2)246 (0.2)150 (0.2) 
Season    
Spring60,171 (27.3)36,709 (28.5)23,462 (25.6)<0.001
Summer29,891 (13.6)17,748 (13.8)12,143 (13.2) 
Fall52,161 (23.7)28,332 (22)23,829 (26) 
Winter78,316 (35.5)46,066 (35.8)32,250 (35.2) 
LOS    
13 days204,812 (92.9)119,497 (92.7)85,315 (93.1)<0.001
46 days10,454 (4.7)6,148 (4.8)4,306 (4.7) 
7+ days5,273 (2.4)3,210 (2.5)2,063 (2.3) 
Median [IQR]1 [11]1 [11]1 [11]0.144
Admitted patients, median [IQR]2 [13]2 [13]2 [13]<0.001

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Utilization Rates Over Study Period
 

Baseline (%)

Preguideline Trend

Level Change at GuidelineChange in Trend After GuidelineEstimates at End of Study*

Without Guideline (%)

With Guideline (%)

P
  • NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.

ED‐only encounters
Blood culture14.60.10.80.15.58.6NS
CBC19.20.10.40.110.714.0NS
CRP5.40.00.60.12.94.8<0.05
Chest x‐ray85.40.10.10.080.981.1NS
Inpatient encounters
Blood culture50.60.01.70.249.241.4<0.05
Repeat blood culture6.50.01.00.18.95.8NS
CBC65.20.03.10.065.062.2NS
Repeat CBC23.40.04.20.020.816.0NS
CRP25.70.01.10.023.823.5NS
Repeat CRP12.50.12.20.17.17.3NS
Chest x‐ray89.40.10.70.085.483.9NS
Repeat chest x‐ray25.50.02.00.124.117.7<0.05
Figure 1
Utilization patterns for patients discharged from the emergency department. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2
Utilization patterns for patients admitted to the hospital. Upper trajectory represents initial utilization, and lower trajectory represents repeat utilization. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3
Utilization patterns by hospital. White boxes represent no significant change between what would be expected from the preguideline trend, dark gray is significant decrease in diagnostic testing, and light gray is significant increase in diagnostic testing. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography, ED, emergency department; ESR, erythrocyte sedimentation rate.

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

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References
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Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

Patient Population

Data from 32 (of the 47) hospitals included in PHIS with complete inpatient and ED data were used to evaluate hospital‐level resource utilization for children 1 to 18 years of age discharged January 1, 2008 to June 30, 2014 with a diagnosis of pneumonia (International Classification of Diseases, 9th Revision [ICD‐9] codes 480.x‐486.x, 487.0).[13] Our goal was to identify previously healthy children with uncomplicated pneumonia, so we excluded patients with complex chronic conditions,[14] billing charges for intensive care management and/or pleural drainage procedure (IDC‐9 codes 510.0, 510.9, 511.0, 511.1, 511.8, 511.9, 513.x) on day of admission or the next day, or prior pneumonia admission in the last 30 days. We studied 2 mutually exclusive populations: children with pneumonia treated in the ED (ie, patients who were evaluated in the ED and discharged to home), and children hospitalized with pneumonia, including those admitted through the ED.

Guideline Publication and Study Periods

For an exploratory before and after comparison, patients were grouped into 2 cohorts based on a guideline online publication date of August 1, 2011: preguideline (January 1, 2008 to July 31, 2011) and postguideline (August 1, 2011 to June 30, 2014).

Study Outcomes

The measured outcomes were the monthly proportion of pneumonia patients for whom specific diagnostic tests were performed, as determined from billing data. The diagnostic tests evaluated were complete blood count (CBC), blood culture, C‐reactive protein (CRP), and chest radiograph (CXR). Standardized costs were also calculated from PHIS charges as previously described to standardize the cost of the individual tests and remove interhospital cost variation.[3]

Relationship of Repeat Testing and Severity of Illness

Because higher illness severity and clinical deterioration may warrant repeat testing, we also explored the association of repeat diagnostic testing for inpatients with severity of illness by using the following variables as measures of severity: length of stay (LOS), transfer to intensive care unit (ICU), or pleural drainage procedure after admission (>2 calendar days after admission). Repeat diagnostic testing was stratified by number of tests.

Statistical Analysis

The categorical demographic characteristics of the pre‐ and postguideline populations were summarized using frequencies and percentages, and compared using 2 tests. Continuous demographics were summarized with medians and interquartile ranges (IQRs) and compared with the Wilcoxon rank sum test. Segmented regression, clustered by hospital, was used to assess trends in monthly resource utilization as well as associated standardized costs before and after guidelines publication. To estimate the impact of the guidelines overall, we compared the observed diagnostic resource use at the end of the study period with expected use projected from trends in the preguidelines period (ie, if there were no new guidelines). Individual interrupted time series were also built for each hospital. From these models, we assessed which hospitals had a significant difference between the rate observed at the end of the study and that estimated from their preguideline trajectory. To assess the relationship between the number of positive improvements at a hospital and hospital characteristics, we used Spearman's correlation and Kruskal‐Wallis tests. All analyses were performed with SAS version 9.3 (SAS Institute, Inc., Cary, NC), and P values <0.05 were considered statistically significant. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this research, using a deidentified dataset, was not considered human subjects research.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Patient Demographics
 OverallPreguidelinePostguidelineP
  • NOTE: Abbreviations: ED, emergency department; HHS, home health services; IQR, interquartile range; LOS, length of stay; SNF, skilled nursing facility.

No. of discharges220,539128,85591,684 
Type of encounter    
ED only150,215 (68.1)88,790 (68.9)61,425 (67)<0.001
Inpatient70,324 (31.9)40,065 (31.1)30,259 (33) 
Age    
14 years129,360 (58.7)77,802 (60.4)51,558 (56.2)<0.001
59 years58,609 (26.6)32,708 (25.4)25,901 (28.3) 
1018 years32,570 (14.8)18,345 (14.2)14,225 (15.5) 
Median [IQR]4 [27]3 [27]4 [27]<0.001
Gender    
Male116,718 (52.9)68,319 (53)48,399 (52.8)00.285
Female103,813 (47.1)60,532 (47)43,281 (47.2) 
Race    
Non‐Hispanic white84,423 (38.3)47,327 (36.7)37,096 (40.5)<0.001
Non‐Hispanic black60,062 (27.2)35,870 (27.8)24,192 (26.4) 
Hispanic51,184 (23.2)31,167 (24.2)20,017 (21.8) 
Asian6,444 (2.9)3,691 (2.9)2,753 (3) 
Other18,426 (8.4)10,800 (8.4)7,626 (8.3) 
Payer    
Government128,047 (58.1)70,742 (54.9)57,305 (62.5)<0.001
Private73,338 (33.3)44,410 (34.5)28,928 (31.6) 
Other19,154 (8.7)13,703 (10.6)5,451 (5.9) 
Disposition    
HHS684 (0.3)411 (0.3)273 (0.3)<0.001
Home209,710 (95.1)123,236 (95.6)86,474 (94.3) 
Other9,749 (4.4)4,962 (3.9)4,787 (5.2) 
SNF396 (0.2)246 (0.2)150 (0.2) 
Season    
Spring60,171 (27.3)36,709 (28.5)23,462 (25.6)<0.001
Summer29,891 (13.6)17,748 (13.8)12,143 (13.2) 
Fall52,161 (23.7)28,332 (22)23,829 (26) 
Winter78,316 (35.5)46,066 (35.8)32,250 (35.2) 
LOS    
13 days204,812 (92.9)119,497 (92.7)85,315 (93.1)<0.001
46 days10,454 (4.7)6,148 (4.8)4,306 (4.7) 
7+ days5,273 (2.4)3,210 (2.5)2,063 (2.3) 
Median [IQR]1 [11]1 [11]1 [11]0.144
Admitted patients, median [IQR]2 [13]2 [13]2 [13]<0.001

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Utilization Rates Over Study Period
 

Baseline (%)

Preguideline Trend

Level Change at GuidelineChange in Trend After GuidelineEstimates at End of Study*

Without Guideline (%)

With Guideline (%)

P
  • NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.

ED‐only encounters
Blood culture14.60.10.80.15.58.6NS
CBC19.20.10.40.110.714.0NS
CRP5.40.00.60.12.94.8<0.05
Chest x‐ray85.40.10.10.080.981.1NS
Inpatient encounters
Blood culture50.60.01.70.249.241.4<0.05
Repeat blood culture6.50.01.00.18.95.8NS
CBC65.20.03.10.065.062.2NS
Repeat CBC23.40.04.20.020.816.0NS
CRP25.70.01.10.023.823.5NS
Repeat CRP12.50.12.20.17.17.3NS
Chest x‐ray89.40.10.70.085.483.9NS
Repeat chest x‐ray25.50.02.00.124.117.7<0.05
Figure 1
Utilization patterns for patients discharged from the emergency department. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2
Utilization patterns for patients admitted to the hospital. Upper trajectory represents initial utilization, and lower trajectory represents repeat utilization. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3
Utilization patterns by hospital. White boxes represent no significant change between what would be expected from the preguideline trend, dark gray is significant decrease in diagnostic testing, and light gray is significant increase in diagnostic testing. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography, ED, emergency department; ESR, erythrocyte sedimentation rate.

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

Patient Population

Data from 32 (of the 47) hospitals included in PHIS with complete inpatient and ED data were used to evaluate hospital‐level resource utilization for children 1 to 18 years of age discharged January 1, 2008 to June 30, 2014 with a diagnosis of pneumonia (International Classification of Diseases, 9th Revision [ICD‐9] codes 480.x‐486.x, 487.0).[13] Our goal was to identify previously healthy children with uncomplicated pneumonia, so we excluded patients with complex chronic conditions,[14] billing charges for intensive care management and/or pleural drainage procedure (IDC‐9 codes 510.0, 510.9, 511.0, 511.1, 511.8, 511.9, 513.x) on day of admission or the next day, or prior pneumonia admission in the last 30 days. We studied 2 mutually exclusive populations: children with pneumonia treated in the ED (ie, patients who were evaluated in the ED and discharged to home), and children hospitalized with pneumonia, including those admitted through the ED.

Guideline Publication and Study Periods

For an exploratory before and after comparison, patients were grouped into 2 cohorts based on a guideline online publication date of August 1, 2011: preguideline (January 1, 2008 to July 31, 2011) and postguideline (August 1, 2011 to June 30, 2014).

Study Outcomes

The measured outcomes were the monthly proportion of pneumonia patients for whom specific diagnostic tests were performed, as determined from billing data. The diagnostic tests evaluated were complete blood count (CBC), blood culture, C‐reactive protein (CRP), and chest radiograph (CXR). Standardized costs were also calculated from PHIS charges as previously described to standardize the cost of the individual tests and remove interhospital cost variation.[3]

Relationship of Repeat Testing and Severity of Illness

Because higher illness severity and clinical deterioration may warrant repeat testing, we also explored the association of repeat diagnostic testing for inpatients with severity of illness by using the following variables as measures of severity: length of stay (LOS), transfer to intensive care unit (ICU), or pleural drainage procedure after admission (>2 calendar days after admission). Repeat diagnostic testing was stratified by number of tests.

Statistical Analysis

The categorical demographic characteristics of the pre‐ and postguideline populations were summarized using frequencies and percentages, and compared using 2 tests. Continuous demographics were summarized with medians and interquartile ranges (IQRs) and compared with the Wilcoxon rank sum test. Segmented regression, clustered by hospital, was used to assess trends in monthly resource utilization as well as associated standardized costs before and after guidelines publication. To estimate the impact of the guidelines overall, we compared the observed diagnostic resource use at the end of the study period with expected use projected from trends in the preguidelines period (ie, if there were no new guidelines). Individual interrupted time series were also built for each hospital. From these models, we assessed which hospitals had a significant difference between the rate observed at the end of the study and that estimated from their preguideline trajectory. To assess the relationship between the number of positive improvements at a hospital and hospital characteristics, we used Spearman's correlation and Kruskal‐Wallis tests. All analyses were performed with SAS version 9.3 (SAS Institute, Inc., Cary, NC), and P values <0.05 were considered statistically significant. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this research, using a deidentified dataset, was not considered human subjects research.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Patient Demographics
 OverallPreguidelinePostguidelineP
  • NOTE: Abbreviations: ED, emergency department; HHS, home health services; IQR, interquartile range; LOS, length of stay; SNF, skilled nursing facility.

No. of discharges220,539128,85591,684 
Type of encounter    
ED only150,215 (68.1)88,790 (68.9)61,425 (67)<0.001
Inpatient70,324 (31.9)40,065 (31.1)30,259 (33) 
Age    
14 years129,360 (58.7)77,802 (60.4)51,558 (56.2)<0.001
59 years58,609 (26.6)32,708 (25.4)25,901 (28.3) 
1018 years32,570 (14.8)18,345 (14.2)14,225 (15.5) 
Median [IQR]4 [27]3 [27]4 [27]<0.001
Gender    
Male116,718 (52.9)68,319 (53)48,399 (52.8)00.285
Female103,813 (47.1)60,532 (47)43,281 (47.2) 
Race    
Non‐Hispanic white84,423 (38.3)47,327 (36.7)37,096 (40.5)<0.001
Non‐Hispanic black60,062 (27.2)35,870 (27.8)24,192 (26.4) 
Hispanic51,184 (23.2)31,167 (24.2)20,017 (21.8) 
Asian6,444 (2.9)3,691 (2.9)2,753 (3) 
Other18,426 (8.4)10,800 (8.4)7,626 (8.3) 
Payer    
Government128,047 (58.1)70,742 (54.9)57,305 (62.5)<0.001
Private73,338 (33.3)44,410 (34.5)28,928 (31.6) 
Other19,154 (8.7)13,703 (10.6)5,451 (5.9) 
Disposition    
HHS684 (0.3)411 (0.3)273 (0.3)<0.001
Home209,710 (95.1)123,236 (95.6)86,474 (94.3) 
Other9,749 (4.4)4,962 (3.9)4,787 (5.2) 
SNF396 (0.2)246 (0.2)150 (0.2) 
Season    
Spring60,171 (27.3)36,709 (28.5)23,462 (25.6)<0.001
Summer29,891 (13.6)17,748 (13.8)12,143 (13.2) 
Fall52,161 (23.7)28,332 (22)23,829 (26) 
Winter78,316 (35.5)46,066 (35.8)32,250 (35.2) 
LOS    
13 days204,812 (92.9)119,497 (92.7)85,315 (93.1)<0.001
46 days10,454 (4.7)6,148 (4.8)4,306 (4.7) 
7+ days5,273 (2.4)3,210 (2.5)2,063 (2.3) 
Median [IQR]1 [11]1 [11]1 [11]0.144
Admitted patients, median [IQR]2 [13]2 [13]2 [13]<0.001

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Utilization Rates Over Study Period
 

Baseline (%)

Preguideline Trend

Level Change at GuidelineChange in Trend After GuidelineEstimates at End of Study*

Without Guideline (%)

With Guideline (%)

P
  • NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.

ED‐only encounters
Blood culture14.60.10.80.15.58.6NS
CBC19.20.10.40.110.714.0NS
CRP5.40.00.60.12.94.8<0.05
Chest x‐ray85.40.10.10.080.981.1NS
Inpatient encounters
Blood culture50.60.01.70.249.241.4<0.05
Repeat blood culture6.50.01.00.18.95.8NS
CBC65.20.03.10.065.062.2NS
Repeat CBC23.40.04.20.020.816.0NS
CRP25.70.01.10.023.823.5NS
Repeat CRP12.50.12.20.17.17.3NS
Chest x‐ray89.40.10.70.085.483.9NS
Repeat chest x‐ray25.50.02.00.124.117.7<0.05
Figure 1
Utilization patterns for patients discharged from the emergency department. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2
Utilization patterns for patients admitted to the hospital. Upper trajectory represents initial utilization, and lower trajectory represents repeat utilization. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography.

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3
Utilization patterns by hospital. White boxes represent no significant change between what would be expected from the preguideline trend, dark gray is significant decrease in diagnostic testing, and light gray is significant increase in diagnostic testing. Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; CXR, chest radiography, ED, emergency department; ESR, erythrocyte sedimentation rate.

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

References
  1. Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA. 2012;307(14):15131516.
  2. Quinonez RA, Garber MD, Schroeder AR, et al. Choosing wisely in pediatric hospital medicine: five opportunities for improved healthcare value. J Hosp Med. 2013;8(9):479485.
  3. Keren R, Luan X, Localio R, et al.; Pediatric Research in Inpatient Settings (PRIS) Network. Prioritization of comparative effectiveness research topics in hospital pediatrics. Arch Pediatr Adolesc Med. 2012;166(12):11551164.
  4. Brogan TV, Hall M, Williams DJ, et al. Variability in processes of care and outcomes among children hospitalized with community‐acquired pneumonia. Pediatr Infect Dis J. 2012;31(10):10361041.
  5. Florin TA, French B, Zorc JJ, Alpern ER, Shah SS. Variation in emergency department diagnostic testing and disposition outcomes in pneumonia. Pediatrics. 2013;132(2):237244.
  6. Bradley JS, Byington CL, Shah SS, et al.; Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. The management of community‐acquired pneumonia in infants and children older than 3 months of age: clinical practice guidelines by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. Clin Infect Dis. 2011;53(7):e25e76.
  7. Ross RK, Hersh AL, Kronman MP, et al., Impact of Infectious Diseases Society of America/Pediatric Infectious Diseases Society guidelines on treatment of community‐acquired pneumonia in hospitalized children. Clin Infect Dis. 2014;58(6):834838.
  8. Williams DJ, Edwards KM, Self WH, et al. Antibiotic choice for children hospitalized with pneumonia and adherence to national guidelines. Pediatrics. 2015;136(1):4452.
  9. Ambroggio L, Thomson J, Murtagh Kurowski E, et al. Quality improvement methods increase appropriate antibiotic prescribing for childhood pneumonia. Pediatrics. 2013;131(5):e1623e1631.
  10. Murtagh Kurowski E, Shah SS, Thomson J, et al. Improvement methodology increases guideline recommended blood cultures in children with pneumonia. Pediatrics. 2015;135(4):e1052e1059.
  11. Newman RE, Hedican EB, Herigon JC, Williams DD, Williams AR, Newland JG. Impact of a guideline on management of children hospitalized with community‐acquired pneumonia. Pediatrics. 2012;129(3):e597e604.
  12. Smith MJ, Kong M, Cambon A, Woods CR. Effectiveness of antimicrobial guidelines for community‐acquired pneumonia in children. Pediatrics. 2012;129(5):e1326e1333.
  13. Williams DJ, Shah SS, Myers A, et al. Identifying pediatric community‐acquired pneumonia hospitalizations: accuracy of administrative billing codes. JAMA Pediatr. 2013;167(9):851858.
  14. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
  15. Parikh K, Agrawal S. Establishing superior benchmarks of care in clinical practice: a proposal to drive achievable health care value. JAMA Pediatr. 2015;169(4):301302.
  16. Neuman MI, Shah SS, Shapiro DJ, Hersh AL. Emergency department management of childhood pneumonia in the United States prior to publication of national guidelines. Acad Emerg Med. 2013;20(3):240246.
  17. Myers AL, Hall M, Williams DJ, et al. Prevalence of bacteremia in hospitalized pediatric patients with community‐acquired pneumonia. Pediatr Infect Dis J. 2013;32(7):736740.
  18. Heine D, Cochran C, Moore M, Titus MO, Andrews AL. The prevalence of bacteremia in pediatric patients with community‐acquired pneumonia: guidelines to reduce the frequency of obtaining blood cultures. Hosp Pediatr. 2013;3(2):9296.
  19. Williams DJ. Do all children hospitalized with community‐acquired pneumonia require blood cultures? Hosp Pediatr. 2013;3(2):177179.
  20. Jain S, Williams DJ, Arnold SR, et al.; CDC EPIC Study Team. Community‐acquired pneumonia requiring hospitalization among U.S. children. N Engl J Med. 2015;372(9):835845.
  21. Neuman MI, Hall M, Hersh AL, et al. Influence of hospital guidelines on management of children hospitalized with pneumonia. Pediatrics. 2012;130(5):e823e830.
References
  1. Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA. 2012;307(14):15131516.
  2. Quinonez RA, Garber MD, Schroeder AR, et al. Choosing wisely in pediatric hospital medicine: five opportunities for improved healthcare value. J Hosp Med. 2013;8(9):479485.
  3. Keren R, Luan X, Localio R, et al.; Pediatric Research in Inpatient Settings (PRIS) Network. Prioritization of comparative effectiveness research topics in hospital pediatrics. Arch Pediatr Adolesc Med. 2012;166(12):11551164.
  4. Brogan TV, Hall M, Williams DJ, et al. Variability in processes of care and outcomes among children hospitalized with community‐acquired pneumonia. Pediatr Infect Dis J. 2012;31(10):10361041.
  5. Florin TA, French B, Zorc JJ, Alpern ER, Shah SS. Variation in emergency department diagnostic testing and disposition outcomes in pneumonia. Pediatrics. 2013;132(2):237244.
  6. Bradley JS, Byington CL, Shah SS, et al.; Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. The management of community‐acquired pneumonia in infants and children older than 3 months of age: clinical practice guidelines by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. Clin Infect Dis. 2011;53(7):e25e76.
  7. Ross RK, Hersh AL, Kronman MP, et al., Impact of Infectious Diseases Society of America/Pediatric Infectious Diseases Society guidelines on treatment of community‐acquired pneumonia in hospitalized children. Clin Infect Dis. 2014;58(6):834838.
  8. Williams DJ, Edwards KM, Self WH, et al. Antibiotic choice for children hospitalized with pneumonia and adherence to national guidelines. Pediatrics. 2015;136(1):4452.
  9. Ambroggio L, Thomson J, Murtagh Kurowski E, et al. Quality improvement methods increase appropriate antibiotic prescribing for childhood pneumonia. Pediatrics. 2013;131(5):e1623e1631.
  10. Murtagh Kurowski E, Shah SS, Thomson J, et al. Improvement methodology increases guideline recommended blood cultures in children with pneumonia. Pediatrics. 2015;135(4):e1052e1059.
  11. Newman RE, Hedican EB, Herigon JC, Williams DD, Williams AR, Newland JG. Impact of a guideline on management of children hospitalized with community‐acquired pneumonia. Pediatrics. 2012;129(3):e597e604.
  12. Smith MJ, Kong M, Cambon A, Woods CR. Effectiveness of antimicrobial guidelines for community‐acquired pneumonia in children. Pediatrics. 2012;129(5):e1326e1333.
  13. Williams DJ, Shah SS, Myers A, et al. Identifying pediatric community‐acquired pneumonia hospitalizations: accuracy of administrative billing codes. JAMA Pediatr. 2013;167(9):851858.
  14. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
  15. Parikh K, Agrawal S. Establishing superior benchmarks of care in clinical practice: a proposal to drive achievable health care value. JAMA Pediatr. 2015;169(4):301302.
  16. Neuman MI, Shah SS, Shapiro DJ, Hersh AL. Emergency department management of childhood pneumonia in the United States prior to publication of national guidelines. Acad Emerg Med. 2013;20(3):240246.
  17. Myers AL, Hall M, Williams DJ, et al. Prevalence of bacteremia in hospitalized pediatric patients with community‐acquired pneumonia. Pediatr Infect Dis J. 2013;32(7):736740.
  18. Heine D, Cochran C, Moore M, Titus MO, Andrews AL. The prevalence of bacteremia in pediatric patients with community‐acquired pneumonia: guidelines to reduce the frequency of obtaining blood cultures. Hosp Pediatr. 2013;3(2):9296.
  19. Williams DJ. Do all children hospitalized with community‐acquired pneumonia require blood cultures? Hosp Pediatr. 2013;3(2):177179.
  20. Jain S, Williams DJ, Arnold SR, et al.; CDC EPIC Study Team. Community‐acquired pneumonia requiring hospitalization among U.S. children. N Engl J Med. 2015;372(9):835845.
  21. Neuman MI, Hall M, Hersh AL, et al. Influence of hospital guidelines on management of children hospitalized with pneumonia. Pediatrics. 2012;130(5):e823e830.
Issue
Journal of Hospital Medicine - 11(5)
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Journal of Hospital Medicine - 11(5)
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317-323
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Aggregate and hospital‐level impact of national guidelines on diagnostic resource utilization for children with pneumonia at children's hospitals
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Aggregate and hospital‐level impact of national guidelines on diagnostic resource utilization for children with pneumonia at children's hospitals
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Address for correspondence and reprint requests: Kavita Parikh, MD, Division of Hospitalist Medicine, Department of Pediatrics, Children's National Medical Center and George Washington School of Medicine, 111 Michigan Ave. NW, Washington DC 20010; Telephone: 202‐476‐6366; Fax: 202‐476‐3732; E‐mail: kparikh@childrensnational.org
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