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Hospitalizations frequently last longer than warranted by medical necessity alone, due to inefficiencies within the US healthcare system.[1, 2] Discharge delays place patients at risk for hospital‐acquired complications and increase costs. With the growing emphasis on high‐value care, hospital length of stay (LOS) has emerged as a key metric for inpatient care and will remain a central focus of hospital‐based improvement initiatives for the foreseeable future.
Hospitals may find it difficult to identify the primary drivers of inpatient LOS in a dynamic and increasingly complex healthcare system. Multiple recent policy changes have affected inpatient care. The Health Information Technology for Economic and Clinical Health Act of 2009 has led to widespread adoption of electronic health records (EHRs) that have markedly impacted provider workflows.[3] In October 2013, the Centers for Medicare & Medicaid Services implemented the 2‐midnight rule, which reclassified lower acuity inpatients with an expected stay <48 hours to observation status.[4] In January 2014, expansion of insurance coverage under the Affordable Care Act (ACA) altered payer mix for hospitals nationwide.[5] At a local level, hospitals that are rapidly adjusting resource allocation, capital investments, and marketing efforts and making complex operational decisions (eg, to open new units or change admission or referral algorithms) may simultaneously experience shifts in patient volumes, case‐mix index, and staffing ratios with downstream effects on LOS.
Given the myriad factors influencing inpatient LOS, hospital leaders may encounter real challenges in designing effective LOS reduction strategies. For example, they may expend significant resources on real‐time demand‐capacity management systems to improve hospital‐wide patient flow, but the resultant emphasis on bed placement and early discharges may shave only hours off average LOS.[6, 7] An alternative approach may be to target the small percentage of patients with prolonged hospitalizations who contribute disproportionately to the average LOS, as other initiatives focused on high utilizers have done.[8, 9, 10]
Our institution noted an increase in the average inpatient LOS for general medicine patients from 2012 to 2014, prompting a call to action by hospital leaders. We sought to characterize the predictors of prolonged hospitalizations among medicine patients to guide future efforts aimed at mitigating the contribution of prolonged LOS to overall LOS.
METHODS
Study Design
We performed a retrospective analysis of medicine patients discharged between January 1, 2012 and December 31, 2014, from the University of Colorado Hospital, a 551‐bed urban, quaternary‐care academic medical center in Aurora, Colorado. Patients were included if they were admitted under inpatient status, 18 years of age, and discharged from 1 of our 10 medicine services: 7 services with residents, staffed by hospitalists, general internists, or subspecialists; and 3 services with advanced practice providers, staffed by hospitalists.
Data Collection
We obtained LOS, calendar year of discharge, demographic data, insurance type, discharge disposition, number of medications, consults, intensive care unit (ICU) stays, surgeries (ie, procedures requiring anesthesia), and primary diagnosis by International Classification of Diseases, Ninth Revision codes from an administrative database that had been developed, validated, and maintained by our hospital medicine group. This database was populated with variables from our EHR, which was implemented in September 2011; to minimize variability in data input during the EHR rollout, we excluded data from September 2011 through December 2011. The Colorado Multiple Institutional Review Board reviewed and exempted this database (protocol 13‐2953) as a program evaluation.
Outcomes
We defined a prolonged hospitalization or LOS as >21 days in duration. This represented approximately 2 standard deviations above the mean LOS in our cohort. This cutoff also helped to remove provider‐level variability, as each medicine service was staffed by 2 attendings per month, each working approximately 7 days on and 7 days off. We examined LOS >14 and >30 days in sensitivity analyses to ensure that the selection of >21 days did not impose an arbitrary and invalid limitation on our statistical analysis.
Statistical Analysis
Demographic and clinical data were compared in the group with LOS 21 days versus the group with LOS >21 days with a 2 test for dichotomous variables and Student t test for continuous variables. We then built a multivariable logistic regression model to predict LOS >21 days using the variables that were significantly different between groups in bivariate analyses. A two‐sided P value of <0.05 was considered statistically significant. All data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 18,363 inpatient discharges among 12,511 medicine patients between January 1, 2012 and December 31, 2014. Of these discharges, 416 (2.3%) demonstrated prolonged LOS. Prolonged hospitalizations accounted for 18.6% of total inpatient days. The average LOS during the study period was 4.8 days including patients with prolonged LOS and 4.0 days excluding patients with prolonged LOS, a contribution of 0.8 days.
Table 1 compares the characteristics of patients with and without prolonged LOS. Age, insurance, discharge disposition, palliative care consults, ICU stays, and surgeries were among the variables that differed significantly between the 2 groups. Among patients undergoing surgery, those with prolonged LOS were more likely to have surgery >24 hours after admission than those without prolonged LOS (85.7% vs 51.4%, P<0.001).
Variable | LOS 21 Days, N=17,947 | LOS >21 Days, N=416 | P Value |
---|---|---|---|
| |||
Age, y, mean (SD) | 56.4 (18.7) | 54.4 (17.1) | 0.030 |
Female | 9,256 (52%) | 199 (48%) | 0.132 |
Year of discharge | <0.001 | ||
2012 | 5,486 (31%) | 69 (17%) | |
2013 | 6,193 (35%) | 162 (39%) | |
2014 | 6,268 (35%) | 185 (44%) | |
Race/ethnicity | 0.003 | ||
White non‐Hispanic | 9,702 (54%) | 242 (58%) | |
Black non‐Hispanic | 4,000 (22%) | 68 (16%) | |
Hispanic | 2,872(16%) | 67 (16%) | |
Asian | 578 (3%) | 9 (2%) | |
Other or unknown | 795 (4%) | 30 (7%) | |
Language preference | 0.795 | ||
English | 16,049 (89%) | 376 (90%) | |
Spanish | 1,052 (6%) | 23 (6%) | |
Other | 846 (5%) | 17 (4%) | |
Insurance | <0.001 | ||
Medicare | 5,462 (30%) | 109 (26%) | |
Medicaid | 3,406 (19%) | 126 (30%) | |
Dual | 2,815 (16%) | 64 (15%) | |
Private | 2,714 (15%) | 60 (14%) | |
Indigent/self‐pay | 2,829 (16%) | 42 (10%) | |
Other | 721 (4%) | 15 (4%) | |
Length of stay, d (SD) | 4.0 (3.5) | 39.5 (37.3) | <0.001 |
Discharge disposition | <0.001 | ||
Home with self‐care | 13,276 (74%) | 115 (28%) | |
Home with home health | 1,584 (9%) | 79 (19%) | |
Hospicehome or inpatient | 369 (2%) | 19 (5%) | |
Postacute‐care facility or LTAC | 1,761 (10%) | 141 (34%) | |
Expired | 113 (1%) | 18 (4%) | |
Other | 844 (5%) | 44 (11%) | |
No. of admission medications (SD) | 9.7 (7.4) | 10.9 (7.8) | 0.002 |
Primary diagnosis by ICD‐9 code* | |||
Sepsis, unspecified | 1,548 (9%) | 55 (13%) | 0.001 |
Acute respiratory failure | 293 (2%) | 9 (2%) | 0.400 |
MSSA septicemia | 36 (0.2%) | 8 (2%) | <0.001 |
MRSA septicemia | 13 (0.1%) | 7 (2%) | <0.001 |
Alcoholic cirrhosis of the liver | 111 (1%) | 7 (2%) | 0.007 |
Palliative care consult | 398 (2%) | 64 (15%) | <0.001 |
ICU stay | 2,030 (11%) | 246 (59%) | <0.001 |
Surgical procedure | 1,800 (10%) | 182 (44%) | <0.001 |
Unspecified sepsis was the most frequent primary diagnosis, regardless of LOS category (Table 2). However, the second through fifth most frequent diagnoses differed for patients with and without prolonged LOS.
N | % | |
---|---|---|
| ||
LOS 21 days | ||
1. Sepsis, unspecified | 1,548 | 8.6% |
2. Acute pancreatitis | 435 | 2.4% |
3. Pneumonia | 431 | 2.4% |
4. Acute kidney failure | 363 | 2.0% |
5. COPD exacerbation | 320 | 1.8% |
LOS >21 days | ||
1. Sepsis, unspecified | 55 | 13.0% |
2. Acute respiratory failure | 9 | 2.2% |
3. Methicillin‐sensitive Staphylococcus aureus septicemia | 8 | 1.9% |
4. Methicillin‐resistant Staphylococcus aureus septicemia | 7 | 1.7% |
5. Alcoholic cirrhosis of the liver | 7 | 1.7% |
In an adjusted logistic regression model (Table 3), we found lower odds of prolonged LOS for each 10‐year increase in age and higher odds of prolonged LOS for Medicaid insurance, discharge to home with home health, discharge to a postacute‐care or long‐term acute‐care facility, and in‐hospital death. Methicillin‐resistant Staphylococcus aureus (MRSA) septicemia, palliative care consults, ICU stays, and surgeries were all also associated with increased odds of prolonged LOS. We identified a statistically significant interaction between ICU stay and surgical procedure (odds ratio: 2.53, 95% confidence interval: 1.51‐4.26, P<0.001).
Outcome: LOS >21 Days | Odds Ratio | 95% CI | P Value |
---|---|---|---|
| |||
Age, per 10 years increase in age | 0.80 | 0.73‐0.87 | <0.001 |
Year of discharge | |||
2012 | 0.47 | 0.34‐0.67 | <0.001 |
2013 | 1.10 | 0.84‐1.43 | 0.493 |
2014 | Ref | ||
Race/ethnicity | |||
White non‐Hispanic | Ref | ||
Black non‐Hispanic | 0.89 | 0.64‐1.22 | 0.454 |
Hispanic | 1.01 | 0.70‐1.46 | 0.952 |
Asian | 0.85 | 0.40‐1.83 | 0.679 |
Other or unknown | 1.29 | 0.73‐2.26 | 0.378 |
Insurance | |||
Medicare | Ref | ||
Medicaid | 1.99 | 1.29‐3.05 | 0.002 |
Dual | 1.06 | 0.72‐1.57 | 0.765 |
Private | 1.13 | 0.70‐1.82 | 0.620 |
Indigent/self‐pay | 1.66 | 0.95‐2.88 | 0.073 |
Other | 0.96 | 0.47‐1.96 | 0.908 |
Discharge disposition | |||
Home with self‐care | Ref | ||
Home with home health | 4.48 | 3.10‐6.48 | <0.001 |
Hospicehome or inpatient | 2.11 | 0.98‐4.55 | 0.057 |
Postacute‐care facility or LTAC | 10.37 | 6.92‐15.56 | <0.001 |
Expired | 5.38 | 2.27‐12.75 | <0.001 |
Other | 4.04 | 2.64‐6.18 | <0.001 |
No. of admission medications | 1.00 | 0.99‐1.02 | 0.775 |
Primary diagnosis by ICD‐9 code | |||
Sepsis, unspecified | 1.11 | 0.78‐1.58 | 0.575 |
MSSA septicemia | 2.44 | 0.68‐8.67 | 0.074 |
MRSA septicemia | 8.83 | 1.72‐45.36 | 0.009 |
Alcoholic cirrhosis of the liver | 1.25 | 0.43‐3.65 | 0.687 |
Palliative care consult | 4.63 | 2.86‐7.49 | <0.001 |
ICU stay | 6.66 | 5.22‐8.50 | <0.001 |
Surgical procedure | 5.04 | 3.90‐6.52 | <0.001 |
Sensitivity analyses using LOS >14 and >30 days yielded similar results to LOS >21 days (see Supporting Appendix Table 1 in the online version of this article).
DISCUSSION
We found that a small proportion of medicine patients with prolonged hospitalizations contributed substantially to both total inpatient days and average inpatient LOS. Such disproportionate healthcare utilization is concerning in light of the Institute of Medicine's charge for health systems to deliver timely, efficient, and equitable care.[11]
Few studies in the United States have analyzed patient characteristics that predict prolonged LOS, and to our knowledge, none have evaluated prolonged LOS specifically in general medicine patients.[12, 13, 14] Among selected surgical populations, prolonged hospitalizations are most often related to placement difficulties, operational delays, and payer‐related issues, rather than severity of illness, baseline comorbidities, or in‐hospital complications.[12, 13] In our study, we found that patients with prolonged LOS were more likely to require a palliative care consult, ICU stay, or surgery, all proxies for disease severity. Patients with prolonged LOS were also more likely to undergo surgery >24 hours after admission than those without prolonged LOS, suggesting that the former were either too unstable to proceed directly to surgery or developed complications later during their hospitalization. Even after controlling for palliative care consults, ICU stays, and surgeries, placement at a postacute‐care facility was strongly associated with prolonged LOS. Patients with prolonged LOS were also more likely to have Medicaid compared to other insurance types.
Our findings have several potential implications for efforts aimed at decreasing the number of and length of prolonged hospitalizations. Although demographic and clinical factors such as Medicaid insurance, ICU stays, and surgeries are generally not modifiable, they could, particularly in combination, be used to trigger earlier and more intensive case management involvement. A streamlined insurance approval process for Medicaid pending inpatients could be beneficial, given the recent expansion of Medicaid eligibility under the ACA. Hospital partnerships with postacute‐care facilities could also relieve bottlenecks in placement.[8] Chart review of the patients with MRSA septicemia and prolonged LOS indicated that development of an intensive outpatient parenteral antimicrobial therapy pathway with substance abuse counseling could provide an alternative to extended inpatient treatment for intravenous drug users with complicated infections.[15]
This study has several limitations. First, given the lack of consensus in the literature regarding the definition of a prolonged hospitalization, it is difficult to directly compare our results with existing studies.[8, 12, 13, 14] However, we believe LOS >21 days to be a meaningful cutoff for our cohort. Most demographic and clinical variables that were predictive at >21 days were also predictive at >14 and >30 days, which reassured us that the relationship between variables and prolonged LOS was stable at different thresholds. Second, our database did not allow us to fully adjust for baseline comorbidities or categorize the reasons for discharge delays. Finally, this was a single‐center program evaluation. Although this limits generalization to other institutions, we believe our approach may serve as a guide for others interested in reducing prolonged hospitalizations.
In summary, prolonged hospitalizations represent a potentially high‐yield target for LOS reduction efforts. Prolonged hospitalizations among medicine patients at our institution particularly affected Medicaid enrollees with complex hospital stays who were not discharged home. Further studies are needed to determine the specific reasons for unnecessary hospital days in this population.
Acknowledgements
The authors thank Essey Yirdaw for her contributions to the building and management of the database that informed this work.
Disclosure: M.E.A. conceived of the study concept and design and drafted the manuscript. J.J.G. assisted with the study design and made critical revisions to the final manuscript. D.A., R.P., and R.C. assisted with the study design and made critical revisions to the manuscript. C.D.J. assisted with the study design, performed data analyses, and made critical revisions to the manuscript. RC has disclosed that her time is funded by a National Institutes of Health grant unrelated to this study. A modified abstract was presented in poster format at the Society of Hospital Medicine Research, Innovations, and Vignettes Competition 2015 Annual Meeting, held March 29 April 1, 2015, in National Harbor, Maryland. The authors have no conflicts of interest to disclose.
- Excess hospitalization days in an academic medical center: perceptions of hospitalists and discharge planners. Am J Manag Care. 2011;17(2):e34–e42. , , , et al.
- A prospective study of reasons for prolonged hospitalizations on a general medicine teaching service. J Gen Intern Med. 2005;20(2):108–115. , ,
- Adoption of electronic health records grows rapidly, but fewer than half of U.S. hospitals had at least a basic system in 2012. Health Aff (Millwood). 2013;32(8):1478–1485. , , , et al.
- Observation and inpatient status: clinical impact of the 2‐midnight rule. J Hosp Med. 2014;9(4):203–209. , , , et al.
- Impact of insurance expansion on hospital uncompensated care costs in 2014. Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation. Available at: http://aspe.hhs.gov/health/reports/2014/uncompensatedcare/ib_uncompensatedcare.pdf. Accessed March 28, 2015. , ,
- Using real‐time demand capacity management to improve hospital‐wide patient flow. Jt Comm J Qual Patient Saf. 2011;37(5):217–227. , , ,
- Natural history of late discharges from a general medical ward. J Hosp Med. 2009;4(4):226–233. , , , , ,
- Addressing hospital length of stay outlier patients: a community‐wide approach. Adv Biosci Biotechol. 2014;5:188–196. , , ,
- American medical home runs. Health Aff (Millwood). 2009;28(5):1317–1326. ,
- The hot spotters: can we lower medical costs by giving the neediest patients better care? New Yorker. January 2011:40–51.
- Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.
- Excessively long hospital stays after trauma are not related to the severity of illness: let's aim to the right target! JAMA Surg. 2013;148(1):956–961. , , , et al.
- Extended length of stay after surgery: complications, inefficient practice, or sick patients? JAMA Surg. 2014;149(8):815–820. , ,
- Nonmedical factors associated with prolonged hospital length of stay in an urban homebound population. J Hosp Med. 2012;7(2):73–78. , , , ,
- Safe and successful treatment of intravenous drug users with a peripherally inserted central catheter in an outpatient parenteral antibiotic treatment service. J Antimicrob Chemother. 2010;65(12):2641–2644. , , ,
Hospitalizations frequently last longer than warranted by medical necessity alone, due to inefficiencies within the US healthcare system.[1, 2] Discharge delays place patients at risk for hospital‐acquired complications and increase costs. With the growing emphasis on high‐value care, hospital length of stay (LOS) has emerged as a key metric for inpatient care and will remain a central focus of hospital‐based improvement initiatives for the foreseeable future.
Hospitals may find it difficult to identify the primary drivers of inpatient LOS in a dynamic and increasingly complex healthcare system. Multiple recent policy changes have affected inpatient care. The Health Information Technology for Economic and Clinical Health Act of 2009 has led to widespread adoption of electronic health records (EHRs) that have markedly impacted provider workflows.[3] In October 2013, the Centers for Medicare & Medicaid Services implemented the 2‐midnight rule, which reclassified lower acuity inpatients with an expected stay <48 hours to observation status.[4] In January 2014, expansion of insurance coverage under the Affordable Care Act (ACA) altered payer mix for hospitals nationwide.[5] At a local level, hospitals that are rapidly adjusting resource allocation, capital investments, and marketing efforts and making complex operational decisions (eg, to open new units or change admission or referral algorithms) may simultaneously experience shifts in patient volumes, case‐mix index, and staffing ratios with downstream effects on LOS.
Given the myriad factors influencing inpatient LOS, hospital leaders may encounter real challenges in designing effective LOS reduction strategies. For example, they may expend significant resources on real‐time demand‐capacity management systems to improve hospital‐wide patient flow, but the resultant emphasis on bed placement and early discharges may shave only hours off average LOS.[6, 7] An alternative approach may be to target the small percentage of patients with prolonged hospitalizations who contribute disproportionately to the average LOS, as other initiatives focused on high utilizers have done.[8, 9, 10]
Our institution noted an increase in the average inpatient LOS for general medicine patients from 2012 to 2014, prompting a call to action by hospital leaders. We sought to characterize the predictors of prolonged hospitalizations among medicine patients to guide future efforts aimed at mitigating the contribution of prolonged LOS to overall LOS.
METHODS
Study Design
We performed a retrospective analysis of medicine patients discharged between January 1, 2012 and December 31, 2014, from the University of Colorado Hospital, a 551‐bed urban, quaternary‐care academic medical center in Aurora, Colorado. Patients were included if they were admitted under inpatient status, 18 years of age, and discharged from 1 of our 10 medicine services: 7 services with residents, staffed by hospitalists, general internists, or subspecialists; and 3 services with advanced practice providers, staffed by hospitalists.
Data Collection
We obtained LOS, calendar year of discharge, demographic data, insurance type, discharge disposition, number of medications, consults, intensive care unit (ICU) stays, surgeries (ie, procedures requiring anesthesia), and primary diagnosis by International Classification of Diseases, Ninth Revision codes from an administrative database that had been developed, validated, and maintained by our hospital medicine group. This database was populated with variables from our EHR, which was implemented in September 2011; to minimize variability in data input during the EHR rollout, we excluded data from September 2011 through December 2011. The Colorado Multiple Institutional Review Board reviewed and exempted this database (protocol 13‐2953) as a program evaluation.
Outcomes
We defined a prolonged hospitalization or LOS as >21 days in duration. This represented approximately 2 standard deviations above the mean LOS in our cohort. This cutoff also helped to remove provider‐level variability, as each medicine service was staffed by 2 attendings per month, each working approximately 7 days on and 7 days off. We examined LOS >14 and >30 days in sensitivity analyses to ensure that the selection of >21 days did not impose an arbitrary and invalid limitation on our statistical analysis.
Statistical Analysis
Demographic and clinical data were compared in the group with LOS 21 days versus the group with LOS >21 days with a 2 test for dichotomous variables and Student t test for continuous variables. We then built a multivariable logistic regression model to predict LOS >21 days using the variables that were significantly different between groups in bivariate analyses. A two‐sided P value of <0.05 was considered statistically significant. All data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 18,363 inpatient discharges among 12,511 medicine patients between January 1, 2012 and December 31, 2014. Of these discharges, 416 (2.3%) demonstrated prolonged LOS. Prolonged hospitalizations accounted for 18.6% of total inpatient days. The average LOS during the study period was 4.8 days including patients with prolonged LOS and 4.0 days excluding patients with prolonged LOS, a contribution of 0.8 days.
Table 1 compares the characteristics of patients with and without prolonged LOS. Age, insurance, discharge disposition, palliative care consults, ICU stays, and surgeries were among the variables that differed significantly between the 2 groups. Among patients undergoing surgery, those with prolonged LOS were more likely to have surgery >24 hours after admission than those without prolonged LOS (85.7% vs 51.4%, P<0.001).
Variable | LOS 21 Days, N=17,947 | LOS >21 Days, N=416 | P Value |
---|---|---|---|
| |||
Age, y, mean (SD) | 56.4 (18.7) | 54.4 (17.1) | 0.030 |
Female | 9,256 (52%) | 199 (48%) | 0.132 |
Year of discharge | <0.001 | ||
2012 | 5,486 (31%) | 69 (17%) | |
2013 | 6,193 (35%) | 162 (39%) | |
2014 | 6,268 (35%) | 185 (44%) | |
Race/ethnicity | 0.003 | ||
White non‐Hispanic | 9,702 (54%) | 242 (58%) | |
Black non‐Hispanic | 4,000 (22%) | 68 (16%) | |
Hispanic | 2,872(16%) | 67 (16%) | |
Asian | 578 (3%) | 9 (2%) | |
Other or unknown | 795 (4%) | 30 (7%) | |
Language preference | 0.795 | ||
English | 16,049 (89%) | 376 (90%) | |
Spanish | 1,052 (6%) | 23 (6%) | |
Other | 846 (5%) | 17 (4%) | |
Insurance | <0.001 | ||
Medicare | 5,462 (30%) | 109 (26%) | |
Medicaid | 3,406 (19%) | 126 (30%) | |
Dual | 2,815 (16%) | 64 (15%) | |
Private | 2,714 (15%) | 60 (14%) | |
Indigent/self‐pay | 2,829 (16%) | 42 (10%) | |
Other | 721 (4%) | 15 (4%) | |
Length of stay, d (SD) | 4.0 (3.5) | 39.5 (37.3) | <0.001 |
Discharge disposition | <0.001 | ||
Home with self‐care | 13,276 (74%) | 115 (28%) | |
Home with home health | 1,584 (9%) | 79 (19%) | |
Hospicehome or inpatient | 369 (2%) | 19 (5%) | |
Postacute‐care facility or LTAC | 1,761 (10%) | 141 (34%) | |
Expired | 113 (1%) | 18 (4%) | |
Other | 844 (5%) | 44 (11%) | |
No. of admission medications (SD) | 9.7 (7.4) | 10.9 (7.8) | 0.002 |
Primary diagnosis by ICD‐9 code* | |||
Sepsis, unspecified | 1,548 (9%) | 55 (13%) | 0.001 |
Acute respiratory failure | 293 (2%) | 9 (2%) | 0.400 |
MSSA septicemia | 36 (0.2%) | 8 (2%) | <0.001 |
MRSA septicemia | 13 (0.1%) | 7 (2%) | <0.001 |
Alcoholic cirrhosis of the liver | 111 (1%) | 7 (2%) | 0.007 |
Palliative care consult | 398 (2%) | 64 (15%) | <0.001 |
ICU stay | 2,030 (11%) | 246 (59%) | <0.001 |
Surgical procedure | 1,800 (10%) | 182 (44%) | <0.001 |
Unspecified sepsis was the most frequent primary diagnosis, regardless of LOS category (Table 2). However, the second through fifth most frequent diagnoses differed for patients with and without prolonged LOS.
N | % | |
---|---|---|
| ||
LOS 21 days | ||
1. Sepsis, unspecified | 1,548 | 8.6% |
2. Acute pancreatitis | 435 | 2.4% |
3. Pneumonia | 431 | 2.4% |
4. Acute kidney failure | 363 | 2.0% |
5. COPD exacerbation | 320 | 1.8% |
LOS >21 days | ||
1. Sepsis, unspecified | 55 | 13.0% |
2. Acute respiratory failure | 9 | 2.2% |
3. Methicillin‐sensitive Staphylococcus aureus septicemia | 8 | 1.9% |
4. Methicillin‐resistant Staphylococcus aureus septicemia | 7 | 1.7% |
5. Alcoholic cirrhosis of the liver | 7 | 1.7% |
In an adjusted logistic regression model (Table 3), we found lower odds of prolonged LOS for each 10‐year increase in age and higher odds of prolonged LOS for Medicaid insurance, discharge to home with home health, discharge to a postacute‐care or long‐term acute‐care facility, and in‐hospital death. Methicillin‐resistant Staphylococcus aureus (MRSA) septicemia, palliative care consults, ICU stays, and surgeries were all also associated with increased odds of prolonged LOS. We identified a statistically significant interaction between ICU stay and surgical procedure (odds ratio: 2.53, 95% confidence interval: 1.51‐4.26, P<0.001).
Outcome: LOS >21 Days | Odds Ratio | 95% CI | P Value |
---|---|---|---|
| |||
Age, per 10 years increase in age | 0.80 | 0.73‐0.87 | <0.001 |
Year of discharge | |||
2012 | 0.47 | 0.34‐0.67 | <0.001 |
2013 | 1.10 | 0.84‐1.43 | 0.493 |
2014 | Ref | ||
Race/ethnicity | |||
White non‐Hispanic | Ref | ||
Black non‐Hispanic | 0.89 | 0.64‐1.22 | 0.454 |
Hispanic | 1.01 | 0.70‐1.46 | 0.952 |
Asian | 0.85 | 0.40‐1.83 | 0.679 |
Other or unknown | 1.29 | 0.73‐2.26 | 0.378 |
Insurance | |||
Medicare | Ref | ||
Medicaid | 1.99 | 1.29‐3.05 | 0.002 |
Dual | 1.06 | 0.72‐1.57 | 0.765 |
Private | 1.13 | 0.70‐1.82 | 0.620 |
Indigent/self‐pay | 1.66 | 0.95‐2.88 | 0.073 |
Other | 0.96 | 0.47‐1.96 | 0.908 |
Discharge disposition | |||
Home with self‐care | Ref | ||
Home with home health | 4.48 | 3.10‐6.48 | <0.001 |
Hospicehome or inpatient | 2.11 | 0.98‐4.55 | 0.057 |
Postacute‐care facility or LTAC | 10.37 | 6.92‐15.56 | <0.001 |
Expired | 5.38 | 2.27‐12.75 | <0.001 |
Other | 4.04 | 2.64‐6.18 | <0.001 |
No. of admission medications | 1.00 | 0.99‐1.02 | 0.775 |
Primary diagnosis by ICD‐9 code | |||
Sepsis, unspecified | 1.11 | 0.78‐1.58 | 0.575 |
MSSA septicemia | 2.44 | 0.68‐8.67 | 0.074 |
MRSA septicemia | 8.83 | 1.72‐45.36 | 0.009 |
Alcoholic cirrhosis of the liver | 1.25 | 0.43‐3.65 | 0.687 |
Palliative care consult | 4.63 | 2.86‐7.49 | <0.001 |
ICU stay | 6.66 | 5.22‐8.50 | <0.001 |
Surgical procedure | 5.04 | 3.90‐6.52 | <0.001 |
Sensitivity analyses using LOS >14 and >30 days yielded similar results to LOS >21 days (see Supporting Appendix Table 1 in the online version of this article).
DISCUSSION
We found that a small proportion of medicine patients with prolonged hospitalizations contributed substantially to both total inpatient days and average inpatient LOS. Such disproportionate healthcare utilization is concerning in light of the Institute of Medicine's charge for health systems to deliver timely, efficient, and equitable care.[11]
Few studies in the United States have analyzed patient characteristics that predict prolonged LOS, and to our knowledge, none have evaluated prolonged LOS specifically in general medicine patients.[12, 13, 14] Among selected surgical populations, prolonged hospitalizations are most often related to placement difficulties, operational delays, and payer‐related issues, rather than severity of illness, baseline comorbidities, or in‐hospital complications.[12, 13] In our study, we found that patients with prolonged LOS were more likely to require a palliative care consult, ICU stay, or surgery, all proxies for disease severity. Patients with prolonged LOS were also more likely to undergo surgery >24 hours after admission than those without prolonged LOS, suggesting that the former were either too unstable to proceed directly to surgery or developed complications later during their hospitalization. Even after controlling for palliative care consults, ICU stays, and surgeries, placement at a postacute‐care facility was strongly associated with prolonged LOS. Patients with prolonged LOS were also more likely to have Medicaid compared to other insurance types.
Our findings have several potential implications for efforts aimed at decreasing the number of and length of prolonged hospitalizations. Although demographic and clinical factors such as Medicaid insurance, ICU stays, and surgeries are generally not modifiable, they could, particularly in combination, be used to trigger earlier and more intensive case management involvement. A streamlined insurance approval process for Medicaid pending inpatients could be beneficial, given the recent expansion of Medicaid eligibility under the ACA. Hospital partnerships with postacute‐care facilities could also relieve bottlenecks in placement.[8] Chart review of the patients with MRSA septicemia and prolonged LOS indicated that development of an intensive outpatient parenteral antimicrobial therapy pathway with substance abuse counseling could provide an alternative to extended inpatient treatment for intravenous drug users with complicated infections.[15]
This study has several limitations. First, given the lack of consensus in the literature regarding the definition of a prolonged hospitalization, it is difficult to directly compare our results with existing studies.[8, 12, 13, 14] However, we believe LOS >21 days to be a meaningful cutoff for our cohort. Most demographic and clinical variables that were predictive at >21 days were also predictive at >14 and >30 days, which reassured us that the relationship between variables and prolonged LOS was stable at different thresholds. Second, our database did not allow us to fully adjust for baseline comorbidities or categorize the reasons for discharge delays. Finally, this was a single‐center program evaluation. Although this limits generalization to other institutions, we believe our approach may serve as a guide for others interested in reducing prolonged hospitalizations.
In summary, prolonged hospitalizations represent a potentially high‐yield target for LOS reduction efforts. Prolonged hospitalizations among medicine patients at our institution particularly affected Medicaid enrollees with complex hospital stays who were not discharged home. Further studies are needed to determine the specific reasons for unnecessary hospital days in this population.
Acknowledgements
The authors thank Essey Yirdaw for her contributions to the building and management of the database that informed this work.
Disclosure: M.E.A. conceived of the study concept and design and drafted the manuscript. J.J.G. assisted with the study design and made critical revisions to the final manuscript. D.A., R.P., and R.C. assisted with the study design and made critical revisions to the manuscript. C.D.J. assisted with the study design, performed data analyses, and made critical revisions to the manuscript. RC has disclosed that her time is funded by a National Institutes of Health grant unrelated to this study. A modified abstract was presented in poster format at the Society of Hospital Medicine Research, Innovations, and Vignettes Competition 2015 Annual Meeting, held March 29 April 1, 2015, in National Harbor, Maryland. The authors have no conflicts of interest to disclose.
Hospitalizations frequently last longer than warranted by medical necessity alone, due to inefficiencies within the US healthcare system.[1, 2] Discharge delays place patients at risk for hospital‐acquired complications and increase costs. With the growing emphasis on high‐value care, hospital length of stay (LOS) has emerged as a key metric for inpatient care and will remain a central focus of hospital‐based improvement initiatives for the foreseeable future.
Hospitals may find it difficult to identify the primary drivers of inpatient LOS in a dynamic and increasingly complex healthcare system. Multiple recent policy changes have affected inpatient care. The Health Information Technology for Economic and Clinical Health Act of 2009 has led to widespread adoption of electronic health records (EHRs) that have markedly impacted provider workflows.[3] In October 2013, the Centers for Medicare & Medicaid Services implemented the 2‐midnight rule, which reclassified lower acuity inpatients with an expected stay <48 hours to observation status.[4] In January 2014, expansion of insurance coverage under the Affordable Care Act (ACA) altered payer mix for hospitals nationwide.[5] At a local level, hospitals that are rapidly adjusting resource allocation, capital investments, and marketing efforts and making complex operational decisions (eg, to open new units or change admission or referral algorithms) may simultaneously experience shifts in patient volumes, case‐mix index, and staffing ratios with downstream effects on LOS.
Given the myriad factors influencing inpatient LOS, hospital leaders may encounter real challenges in designing effective LOS reduction strategies. For example, they may expend significant resources on real‐time demand‐capacity management systems to improve hospital‐wide patient flow, but the resultant emphasis on bed placement and early discharges may shave only hours off average LOS.[6, 7] An alternative approach may be to target the small percentage of patients with prolonged hospitalizations who contribute disproportionately to the average LOS, as other initiatives focused on high utilizers have done.[8, 9, 10]
Our institution noted an increase in the average inpatient LOS for general medicine patients from 2012 to 2014, prompting a call to action by hospital leaders. We sought to characterize the predictors of prolonged hospitalizations among medicine patients to guide future efforts aimed at mitigating the contribution of prolonged LOS to overall LOS.
METHODS
Study Design
We performed a retrospective analysis of medicine patients discharged between January 1, 2012 and December 31, 2014, from the University of Colorado Hospital, a 551‐bed urban, quaternary‐care academic medical center in Aurora, Colorado. Patients were included if they were admitted under inpatient status, 18 years of age, and discharged from 1 of our 10 medicine services: 7 services with residents, staffed by hospitalists, general internists, or subspecialists; and 3 services with advanced practice providers, staffed by hospitalists.
Data Collection
We obtained LOS, calendar year of discharge, demographic data, insurance type, discharge disposition, number of medications, consults, intensive care unit (ICU) stays, surgeries (ie, procedures requiring anesthesia), and primary diagnosis by International Classification of Diseases, Ninth Revision codes from an administrative database that had been developed, validated, and maintained by our hospital medicine group. This database was populated with variables from our EHR, which was implemented in September 2011; to minimize variability in data input during the EHR rollout, we excluded data from September 2011 through December 2011. The Colorado Multiple Institutional Review Board reviewed and exempted this database (protocol 13‐2953) as a program evaluation.
Outcomes
We defined a prolonged hospitalization or LOS as >21 days in duration. This represented approximately 2 standard deviations above the mean LOS in our cohort. This cutoff also helped to remove provider‐level variability, as each medicine service was staffed by 2 attendings per month, each working approximately 7 days on and 7 days off. We examined LOS >14 and >30 days in sensitivity analyses to ensure that the selection of >21 days did not impose an arbitrary and invalid limitation on our statistical analysis.
Statistical Analysis
Demographic and clinical data were compared in the group with LOS 21 days versus the group with LOS >21 days with a 2 test for dichotomous variables and Student t test for continuous variables. We then built a multivariable logistic regression model to predict LOS >21 days using the variables that were significantly different between groups in bivariate analyses. A two‐sided P value of <0.05 was considered statistically significant. All data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 18,363 inpatient discharges among 12,511 medicine patients between January 1, 2012 and December 31, 2014. Of these discharges, 416 (2.3%) demonstrated prolonged LOS. Prolonged hospitalizations accounted for 18.6% of total inpatient days. The average LOS during the study period was 4.8 days including patients with prolonged LOS and 4.0 days excluding patients with prolonged LOS, a contribution of 0.8 days.
Table 1 compares the characteristics of patients with and without prolonged LOS. Age, insurance, discharge disposition, palliative care consults, ICU stays, and surgeries were among the variables that differed significantly between the 2 groups. Among patients undergoing surgery, those with prolonged LOS were more likely to have surgery >24 hours after admission than those without prolonged LOS (85.7% vs 51.4%, P<0.001).
Variable | LOS 21 Days, N=17,947 | LOS >21 Days, N=416 | P Value |
---|---|---|---|
| |||
Age, y, mean (SD) | 56.4 (18.7) | 54.4 (17.1) | 0.030 |
Female | 9,256 (52%) | 199 (48%) | 0.132 |
Year of discharge | <0.001 | ||
2012 | 5,486 (31%) | 69 (17%) | |
2013 | 6,193 (35%) | 162 (39%) | |
2014 | 6,268 (35%) | 185 (44%) | |
Race/ethnicity | 0.003 | ||
White non‐Hispanic | 9,702 (54%) | 242 (58%) | |
Black non‐Hispanic | 4,000 (22%) | 68 (16%) | |
Hispanic | 2,872(16%) | 67 (16%) | |
Asian | 578 (3%) | 9 (2%) | |
Other or unknown | 795 (4%) | 30 (7%) | |
Language preference | 0.795 | ||
English | 16,049 (89%) | 376 (90%) | |
Spanish | 1,052 (6%) | 23 (6%) | |
Other | 846 (5%) | 17 (4%) | |
Insurance | <0.001 | ||
Medicare | 5,462 (30%) | 109 (26%) | |
Medicaid | 3,406 (19%) | 126 (30%) | |
Dual | 2,815 (16%) | 64 (15%) | |
Private | 2,714 (15%) | 60 (14%) | |
Indigent/self‐pay | 2,829 (16%) | 42 (10%) | |
Other | 721 (4%) | 15 (4%) | |
Length of stay, d (SD) | 4.0 (3.5) | 39.5 (37.3) | <0.001 |
Discharge disposition | <0.001 | ||
Home with self‐care | 13,276 (74%) | 115 (28%) | |
Home with home health | 1,584 (9%) | 79 (19%) | |
Hospicehome or inpatient | 369 (2%) | 19 (5%) | |
Postacute‐care facility or LTAC | 1,761 (10%) | 141 (34%) | |
Expired | 113 (1%) | 18 (4%) | |
Other | 844 (5%) | 44 (11%) | |
No. of admission medications (SD) | 9.7 (7.4) | 10.9 (7.8) | 0.002 |
Primary diagnosis by ICD‐9 code* | |||
Sepsis, unspecified | 1,548 (9%) | 55 (13%) | 0.001 |
Acute respiratory failure | 293 (2%) | 9 (2%) | 0.400 |
MSSA septicemia | 36 (0.2%) | 8 (2%) | <0.001 |
MRSA septicemia | 13 (0.1%) | 7 (2%) | <0.001 |
Alcoholic cirrhosis of the liver | 111 (1%) | 7 (2%) | 0.007 |
Palliative care consult | 398 (2%) | 64 (15%) | <0.001 |
ICU stay | 2,030 (11%) | 246 (59%) | <0.001 |
Surgical procedure | 1,800 (10%) | 182 (44%) | <0.001 |
Unspecified sepsis was the most frequent primary diagnosis, regardless of LOS category (Table 2). However, the second through fifth most frequent diagnoses differed for patients with and without prolonged LOS.
N | % | |
---|---|---|
| ||
LOS 21 days | ||
1. Sepsis, unspecified | 1,548 | 8.6% |
2. Acute pancreatitis | 435 | 2.4% |
3. Pneumonia | 431 | 2.4% |
4. Acute kidney failure | 363 | 2.0% |
5. COPD exacerbation | 320 | 1.8% |
LOS >21 days | ||
1. Sepsis, unspecified | 55 | 13.0% |
2. Acute respiratory failure | 9 | 2.2% |
3. Methicillin‐sensitive Staphylococcus aureus septicemia | 8 | 1.9% |
4. Methicillin‐resistant Staphylococcus aureus septicemia | 7 | 1.7% |
5. Alcoholic cirrhosis of the liver | 7 | 1.7% |
In an adjusted logistic regression model (Table 3), we found lower odds of prolonged LOS for each 10‐year increase in age and higher odds of prolonged LOS for Medicaid insurance, discharge to home with home health, discharge to a postacute‐care or long‐term acute‐care facility, and in‐hospital death. Methicillin‐resistant Staphylococcus aureus (MRSA) septicemia, palliative care consults, ICU stays, and surgeries were all also associated with increased odds of prolonged LOS. We identified a statistically significant interaction between ICU stay and surgical procedure (odds ratio: 2.53, 95% confidence interval: 1.51‐4.26, P<0.001).
Outcome: LOS >21 Days | Odds Ratio | 95% CI | P Value |
---|---|---|---|
| |||
Age, per 10 years increase in age | 0.80 | 0.73‐0.87 | <0.001 |
Year of discharge | |||
2012 | 0.47 | 0.34‐0.67 | <0.001 |
2013 | 1.10 | 0.84‐1.43 | 0.493 |
2014 | Ref | ||
Race/ethnicity | |||
White non‐Hispanic | Ref | ||
Black non‐Hispanic | 0.89 | 0.64‐1.22 | 0.454 |
Hispanic | 1.01 | 0.70‐1.46 | 0.952 |
Asian | 0.85 | 0.40‐1.83 | 0.679 |
Other or unknown | 1.29 | 0.73‐2.26 | 0.378 |
Insurance | |||
Medicare | Ref | ||
Medicaid | 1.99 | 1.29‐3.05 | 0.002 |
Dual | 1.06 | 0.72‐1.57 | 0.765 |
Private | 1.13 | 0.70‐1.82 | 0.620 |
Indigent/self‐pay | 1.66 | 0.95‐2.88 | 0.073 |
Other | 0.96 | 0.47‐1.96 | 0.908 |
Discharge disposition | |||
Home with self‐care | Ref | ||
Home with home health | 4.48 | 3.10‐6.48 | <0.001 |
Hospicehome or inpatient | 2.11 | 0.98‐4.55 | 0.057 |
Postacute‐care facility or LTAC | 10.37 | 6.92‐15.56 | <0.001 |
Expired | 5.38 | 2.27‐12.75 | <0.001 |
Other | 4.04 | 2.64‐6.18 | <0.001 |
No. of admission medications | 1.00 | 0.99‐1.02 | 0.775 |
Primary diagnosis by ICD‐9 code | |||
Sepsis, unspecified | 1.11 | 0.78‐1.58 | 0.575 |
MSSA septicemia | 2.44 | 0.68‐8.67 | 0.074 |
MRSA septicemia | 8.83 | 1.72‐45.36 | 0.009 |
Alcoholic cirrhosis of the liver | 1.25 | 0.43‐3.65 | 0.687 |
Palliative care consult | 4.63 | 2.86‐7.49 | <0.001 |
ICU stay | 6.66 | 5.22‐8.50 | <0.001 |
Surgical procedure | 5.04 | 3.90‐6.52 | <0.001 |
Sensitivity analyses using LOS >14 and >30 days yielded similar results to LOS >21 days (see Supporting Appendix Table 1 in the online version of this article).
DISCUSSION
We found that a small proportion of medicine patients with prolonged hospitalizations contributed substantially to both total inpatient days and average inpatient LOS. Such disproportionate healthcare utilization is concerning in light of the Institute of Medicine's charge for health systems to deliver timely, efficient, and equitable care.[11]
Few studies in the United States have analyzed patient characteristics that predict prolonged LOS, and to our knowledge, none have evaluated prolonged LOS specifically in general medicine patients.[12, 13, 14] Among selected surgical populations, prolonged hospitalizations are most often related to placement difficulties, operational delays, and payer‐related issues, rather than severity of illness, baseline comorbidities, or in‐hospital complications.[12, 13] In our study, we found that patients with prolonged LOS were more likely to require a palliative care consult, ICU stay, or surgery, all proxies for disease severity. Patients with prolonged LOS were also more likely to undergo surgery >24 hours after admission than those without prolonged LOS, suggesting that the former were either too unstable to proceed directly to surgery or developed complications later during their hospitalization. Even after controlling for palliative care consults, ICU stays, and surgeries, placement at a postacute‐care facility was strongly associated with prolonged LOS. Patients with prolonged LOS were also more likely to have Medicaid compared to other insurance types.
Our findings have several potential implications for efforts aimed at decreasing the number of and length of prolonged hospitalizations. Although demographic and clinical factors such as Medicaid insurance, ICU stays, and surgeries are generally not modifiable, they could, particularly in combination, be used to trigger earlier and more intensive case management involvement. A streamlined insurance approval process for Medicaid pending inpatients could be beneficial, given the recent expansion of Medicaid eligibility under the ACA. Hospital partnerships with postacute‐care facilities could also relieve bottlenecks in placement.[8] Chart review of the patients with MRSA septicemia and prolonged LOS indicated that development of an intensive outpatient parenteral antimicrobial therapy pathway with substance abuse counseling could provide an alternative to extended inpatient treatment for intravenous drug users with complicated infections.[15]
This study has several limitations. First, given the lack of consensus in the literature regarding the definition of a prolonged hospitalization, it is difficult to directly compare our results with existing studies.[8, 12, 13, 14] However, we believe LOS >21 days to be a meaningful cutoff for our cohort. Most demographic and clinical variables that were predictive at >21 days were also predictive at >14 and >30 days, which reassured us that the relationship between variables and prolonged LOS was stable at different thresholds. Second, our database did not allow us to fully adjust for baseline comorbidities or categorize the reasons for discharge delays. Finally, this was a single‐center program evaluation. Although this limits generalization to other institutions, we believe our approach may serve as a guide for others interested in reducing prolonged hospitalizations.
In summary, prolonged hospitalizations represent a potentially high‐yield target for LOS reduction efforts. Prolonged hospitalizations among medicine patients at our institution particularly affected Medicaid enrollees with complex hospital stays who were not discharged home. Further studies are needed to determine the specific reasons for unnecessary hospital days in this population.
Acknowledgements
The authors thank Essey Yirdaw for her contributions to the building and management of the database that informed this work.
Disclosure: M.E.A. conceived of the study concept and design and drafted the manuscript. J.J.G. assisted with the study design and made critical revisions to the final manuscript. D.A., R.P., and R.C. assisted with the study design and made critical revisions to the manuscript. C.D.J. assisted with the study design, performed data analyses, and made critical revisions to the manuscript. RC has disclosed that her time is funded by a National Institutes of Health grant unrelated to this study. A modified abstract was presented in poster format at the Society of Hospital Medicine Research, Innovations, and Vignettes Competition 2015 Annual Meeting, held March 29 April 1, 2015, in National Harbor, Maryland. The authors have no conflicts of interest to disclose.
- Excess hospitalization days in an academic medical center: perceptions of hospitalists and discharge planners. Am J Manag Care. 2011;17(2):e34–e42. , , , et al.
- A prospective study of reasons for prolonged hospitalizations on a general medicine teaching service. J Gen Intern Med. 2005;20(2):108–115. , ,
- Adoption of electronic health records grows rapidly, but fewer than half of U.S. hospitals had at least a basic system in 2012. Health Aff (Millwood). 2013;32(8):1478–1485. , , , et al.
- Observation and inpatient status: clinical impact of the 2‐midnight rule. J Hosp Med. 2014;9(4):203–209. , , , et al.
- Impact of insurance expansion on hospital uncompensated care costs in 2014. Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation. Available at: http://aspe.hhs.gov/health/reports/2014/uncompensatedcare/ib_uncompensatedcare.pdf. Accessed March 28, 2015. , ,
- Using real‐time demand capacity management to improve hospital‐wide patient flow. Jt Comm J Qual Patient Saf. 2011;37(5):217–227. , , ,
- Natural history of late discharges from a general medical ward. J Hosp Med. 2009;4(4):226–233. , , , , ,
- Addressing hospital length of stay outlier patients: a community‐wide approach. Adv Biosci Biotechol. 2014;5:188–196. , , ,
- American medical home runs. Health Aff (Millwood). 2009;28(5):1317–1326. ,
- The hot spotters: can we lower medical costs by giving the neediest patients better care? New Yorker. January 2011:40–51.
- Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.
- Excessively long hospital stays after trauma are not related to the severity of illness: let's aim to the right target! JAMA Surg. 2013;148(1):956–961. , , , et al.
- Extended length of stay after surgery: complications, inefficient practice, or sick patients? JAMA Surg. 2014;149(8):815–820. , ,
- Nonmedical factors associated with prolonged hospital length of stay in an urban homebound population. J Hosp Med. 2012;7(2):73–78. , , , ,
- Safe and successful treatment of intravenous drug users with a peripherally inserted central catheter in an outpatient parenteral antibiotic treatment service. J Antimicrob Chemother. 2010;65(12):2641–2644. , , ,
- Excess hospitalization days in an academic medical center: perceptions of hospitalists and discharge planners. Am J Manag Care. 2011;17(2):e34–e42. , , , et al.
- A prospective study of reasons for prolonged hospitalizations on a general medicine teaching service. J Gen Intern Med. 2005;20(2):108–115. , ,
- Adoption of electronic health records grows rapidly, but fewer than half of U.S. hospitals had at least a basic system in 2012. Health Aff (Millwood). 2013;32(8):1478–1485. , , , et al.
- Observation and inpatient status: clinical impact of the 2‐midnight rule. J Hosp Med. 2014;9(4):203–209. , , , et al.
- Impact of insurance expansion on hospital uncompensated care costs in 2014. Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation. Available at: http://aspe.hhs.gov/health/reports/2014/uncompensatedcare/ib_uncompensatedcare.pdf. Accessed March 28, 2015. , ,
- Using real‐time demand capacity management to improve hospital‐wide patient flow. Jt Comm J Qual Patient Saf. 2011;37(5):217–227. , , ,
- Natural history of late discharges from a general medical ward. J Hosp Med. 2009;4(4):226–233. , , , , ,
- Addressing hospital length of stay outlier patients: a community‐wide approach. Adv Biosci Biotechol. 2014;5:188–196. , , ,
- American medical home runs. Health Aff (Millwood). 2009;28(5):1317–1326. ,
- The hot spotters: can we lower medical costs by giving the neediest patients better care? New Yorker. January 2011:40–51.
- Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.
- Excessively long hospital stays after trauma are not related to the severity of illness: let's aim to the right target! JAMA Surg. 2013;148(1):956–961. , , , et al.
- Extended length of stay after surgery: complications, inefficient practice, or sick patients? JAMA Surg. 2014;149(8):815–820. , ,
- Nonmedical factors associated with prolonged hospital length of stay in an urban homebound population. J Hosp Med. 2012;7(2):73–78. , , , ,
- Safe and successful treatment of intravenous drug users with a peripherally inserted central catheter in an outpatient parenteral antibiotic treatment service. J Antimicrob Chemother. 2010;65(12):2641–2644. , , ,