User login
All together now: Impact of a regionalization and bedside rounding initiative on the efficiency and inclusiveness of clinical rounds
Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.1 Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.2-10
However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.2 Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.
There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.
METHODS
Setting
This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.
Intervention
In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.11
Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.
Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.
Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.
Study Design
We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.
We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).
Statistical Analysis
TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.
Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ2 test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.
RESULTS
Patient and Care Team Characteristics
Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).
Primary Outcomes
Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).
Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.
Secondary Outcomes
Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).
DISCUSSION
TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.
Regionalized teams have been found to improve interdisciplinary communication.1 The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.9 Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,1 as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.12 Additional interventions may be needed to ensure that communication during bedside rounds is effective.
Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.1,13-15
Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.9 Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.16
Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.
An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.
This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.
Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.12 However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.17Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.
In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.
Acknowledgments
The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.
Disclosures
Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.
1. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
2. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
3. Elliot DL, Hickam DH. Attending rounds on in-patient units: differences between medical and non-medical services. Med Educ. 1993;27(6):503-508. PubMed
4. Payson HE, Barchas JD. A time study of medical teaching rounds. N Engl J Med. 1965;273(27):1468-1471. PubMed
5. Tremonti LP, Biddle WB. Teaching behaviors of residents and faculty members. J Med Educ. 1982;57(11):854-859. PubMed
6. Miller M, Johnson B, Greene HL, Baier M, Nowlin S. An observational study of attending rounds. J Gen Intern Med. 1992;7(6):646-648. PubMed
7. Collins GF, Cassie JM, Daggett CJ. The role of the attending physician in clinical training. J Med Educ. 1978;53(5):429-431. PubMed
8. Ward DR, Ghali WA, Graham A, Lemaire JB. A real-time locating system observes physician time-motion patterns during walk-rounds: a pilot study. BMC Med Educ. 2014;14:37. PubMed
9. Gonzalo JD, Kuperman E, Lehman E, Haidet P. Bedside interprofessional rounds: perceptions of benefits and barriers by internal medicine nursing staff, attending physicians, and housestaff physicians. J Hosp Med. 2014;9(10):646-651. PubMed
10. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
11. Boxer R, Vitale M, Gershanik EF, et al. 5th time’s a charm: creation of unit-based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10(suppl 2).
12. Mueller SK, Schnipper JL, Giannelli K, Roy CL, Boxer R. Impact of regionalized care on concordance of plan and preventable adverse events on general medicine services. J Hosp Med. 2016;11(9):620-627. PubMed
13. Chauke HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
15. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):1150-1155. PubMed
16. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
17. Mueller SK, Yoon C, Schnipper JL. Association of a web-based handoff tool with rates of medical errors. JAMA Intern Med. 2016;176(9):1400-1402. PubMed
Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.1 Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.2-10
However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.2 Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.
There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.
METHODS
Setting
This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.
Intervention
In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.11
Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.
Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.
Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.
Study Design
We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.
We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).
Statistical Analysis
TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.
Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ2 test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.
RESULTS
Patient and Care Team Characteristics
Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).
Primary Outcomes
Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).
Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.
Secondary Outcomes
Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).
DISCUSSION
TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.
Regionalized teams have been found to improve interdisciplinary communication.1 The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.9 Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,1 as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.12 Additional interventions may be needed to ensure that communication during bedside rounds is effective.
Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.1,13-15
Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.9 Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.16
Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.
An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.
This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.
Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.12 However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.17Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.
In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.
Acknowledgments
The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.
Disclosures
Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.
Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.1 Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.2-10
However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.2 Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.
There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.
METHODS
Setting
This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.
Intervention
In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.11
Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.
Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.
Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.
Study Design
We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.
We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).
Statistical Analysis
TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.
Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ2 test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.
RESULTS
Patient and Care Team Characteristics
Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).
Primary Outcomes
Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).
Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.
Secondary Outcomes
Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).
DISCUSSION
TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.
Regionalized teams have been found to improve interdisciplinary communication.1 The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.9 Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,1 as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.12 Additional interventions may be needed to ensure that communication during bedside rounds is effective.
Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.1,13-15
Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.9 Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.16
Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.
An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.
This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.
Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.12 However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.17Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.
In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.
Acknowledgments
The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.
Disclosures
Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.
1. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
2. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
3. Elliot DL, Hickam DH. Attending rounds on in-patient units: differences between medical and non-medical services. Med Educ. 1993;27(6):503-508. PubMed
4. Payson HE, Barchas JD. A time study of medical teaching rounds. N Engl J Med. 1965;273(27):1468-1471. PubMed
5. Tremonti LP, Biddle WB. Teaching behaviors of residents and faculty members. J Med Educ. 1982;57(11):854-859. PubMed
6. Miller M, Johnson B, Greene HL, Baier M, Nowlin S. An observational study of attending rounds. J Gen Intern Med. 1992;7(6):646-648. PubMed
7. Collins GF, Cassie JM, Daggett CJ. The role of the attending physician in clinical training. J Med Educ. 1978;53(5):429-431. PubMed
8. Ward DR, Ghali WA, Graham A, Lemaire JB. A real-time locating system observes physician time-motion patterns during walk-rounds: a pilot study. BMC Med Educ. 2014;14:37. PubMed
9. Gonzalo JD, Kuperman E, Lehman E, Haidet P. Bedside interprofessional rounds: perceptions of benefits and barriers by internal medicine nursing staff, attending physicians, and housestaff physicians. J Hosp Med. 2014;9(10):646-651. PubMed
10. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
11. Boxer R, Vitale M, Gershanik EF, et al. 5th time’s a charm: creation of unit-based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10(suppl 2).
12. Mueller SK, Schnipper JL, Giannelli K, Roy CL, Boxer R. Impact of regionalized care on concordance of plan and preventable adverse events on general medicine services. J Hosp Med. 2016;11(9):620-627. PubMed
13. Chauke HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
15. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):1150-1155. PubMed
16. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
17. Mueller SK, Yoon C, Schnipper JL. Association of a web-based handoff tool with rates of medical errors. JAMA Intern Med. 2016;176(9):1400-1402. PubMed
1. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
2. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
3. Elliot DL, Hickam DH. Attending rounds on in-patient units: differences between medical and non-medical services. Med Educ. 1993;27(6):503-508. PubMed
4. Payson HE, Barchas JD. A time study of medical teaching rounds. N Engl J Med. 1965;273(27):1468-1471. PubMed
5. Tremonti LP, Biddle WB. Teaching behaviors of residents and faculty members. J Med Educ. 1982;57(11):854-859. PubMed
6. Miller M, Johnson B, Greene HL, Baier M, Nowlin S. An observational study of attending rounds. J Gen Intern Med. 1992;7(6):646-648. PubMed
7. Collins GF, Cassie JM, Daggett CJ. The role of the attending physician in clinical training. J Med Educ. 1978;53(5):429-431. PubMed
8. Ward DR, Ghali WA, Graham A, Lemaire JB. A real-time locating system observes physician time-motion patterns during walk-rounds: a pilot study. BMC Med Educ. 2014;14:37. PubMed
9. Gonzalo JD, Kuperman E, Lehman E, Haidet P. Bedside interprofessional rounds: perceptions of benefits and barriers by internal medicine nursing staff, attending physicians, and housestaff physicians. J Hosp Med. 2014;9(10):646-651. PubMed
10. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
11. Boxer R, Vitale M, Gershanik EF, et al. 5th time’s a charm: creation of unit-based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10(suppl 2).
12. Mueller SK, Schnipper JL, Giannelli K, Roy CL, Boxer R. Impact of regionalized care on concordance of plan and preventable adverse events on general medicine services. J Hosp Med. 2016;11(9):620-627. PubMed
13. Chauke HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
15. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):1150-1155. PubMed
16. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
17. Mueller SK, Yoon C, Schnipper JL. Association of a web-based handoff tool with rates of medical errors. JAMA Intern Med. 2016;176(9):1400-1402. PubMed
© 2017 Society of Hospital Medicine
Regionalized Care and Adverse Events
Failures in communication among healthcare professionals are known threats to patient safety. These failures account for over 60% of root causes of sentinel events, the most serious events reported to The Joint Commission.[1] As such, identifying both patterns of effective communication as well as barriers to successful communication has been a focus of efforts aimed at improving patient safety. However, to date, the majority of this work has centered on improving communication in settings such as the operating room and intensive care unit,[2, 3, 4] or at times of care transitions.[5, 6, 7, 8]
Unique barriers exist for effective interdisciplinary communication in the hospital setting, particularly physiciannurse communication regarding shared hospitalized patients.[9] Traditionally, care of hospitalized patients is provided by physicians, nurses, and other team members working in varied workflow patterns, leading to dispersed team membership, where each team member cares for different groups of patients in different locations across the hospital. This dispersion is further heightened on teaching services, where residents' rotation schedules lead to frequent changes of care team membership, leaving inpatient care teams particularly vulnerable to ineffective communication. Evidence suggests that communication between nurses and physicians is currently suboptimal, leading to frequent disagreement regarding the patient's plan of care.[9, 10] This divergence between physician and nursing perceptions of patients' care plans may leave patients at greater risk of adverse events (AEs).
Several studies have examined the effects of regionalized inpatient care teams, where multidisciplinary team members care for the same patients on the same hospital unit, on communication and patient outcomes.[4, 11, 12, 13, 14] Results of these studies have been inconsistent, perhaps due to the particular characteristics of the care teams or to the study methodology. Thus, further rigorously done studies are required to better understand the impact of team regionalization on patient care. The goal of this study was to examine whether the implementation of regionalized inpatient care teams was associated with improvements in care team communication and preventable AEs.
METHODS
Setting, Patients, and Study Design
We performed a cohort analysis of patients at a 700‐bed tertiary care center, pre‐ and postregionalization of inpatient general medicine care teams. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee. Patients were eligible for inclusion if they were 18 years of age or older and discharged from the general medicine service (GMS) from any of the 3 participating nursing units between April 1, 2012 and June 19, 2012 (preregionalization) or April 1, 2013 and June 19, 2013 (postregionalization).
Intervention
On June 20, 2012, regionalized care was implemented on the GMS such that each of 3 GMS teams was localized to 1 of 3, 15‐bed nursing units. Prior to regionalization, the GMS physician care teams, each consisting of 1 hospitalist attending, 1 medical resident, and 2 medical interns, would care for patients on an average of 7 and up to 13 different nursing units on a given day.
Regionalized care consisted of a multifaceted intervention codeveloped by hospitalist, residency, nursing, emergency department, and hospital leadership and included: (1) regionalizing GMS teams as much as possible; (2) change in resident call structure from a traditional 4‐day call cycle to daily admitting; (3) collaborative efforts to enhance GMS patient discharges before noon to promote regionalized placement of patients without prolonging time in the emergency department (ED); (4) daily morning and postround multidisciplinary huddles to prioritize sicker patients and discharges; (5) encouragement of daily rounds at patients' bedsides with presence of physician team, nurse, and team pharmacist if available; (6) creation of unit‐ and team‐level performance reports; and (7) creation of unit‐based physician and nursing co‐leadership (Figure 1).[15]
Concordance of Plan
Concordance of plan was measured via a 7‐question survey previously developed, pilot tested, and used to measure the impact of regionalized care on care team communication between inpatient nursephysician team members.[9] The survey was administered in‐person by 1 of 8 trained research assistants (RAs) (4/emntervention period) to nurse and intern pairs caring for patients on the study units pre‐ and postregionalization. GMS patients were eligible for inclusion if surveys could be administered to their nurse and intern within the first 24 hours of admission to the unit and within 48 hours of admission to the hospital, based on RA availability (thus excluding patients admitted on Fridays as surveys were not conducted over the weekend). Most often, all eligible patients admitted to the study units during time periods of data collection were included in the study. On limited occasions, the daily supply of patients surpassed RA capacity for inclusion, at which time computer‐generated randomization was utilized to randomly select patients for inclusion. Nurse and intern pairs were surveyed once during a patient's hospitalization, although they could be surveyed more than once about different patients, and patients could be included more than once if rehospitalized on the study unit and cared for by a different nurseintern pair. Of the 472 selected eligible patients, the nurses and interns of 418 patients were available and consented to survey administration, representing 361 unique nurse and intern pairs and 399 unique patients.
Each member of the pair was asked about 7 specific aspects of the patient's care plan for that day in isolation from the other team member, including: (1) the patient's primary diagnosis, (2) the patient's expressed chief concern, (3) the day's scheduled tests, (4) the day's scheduled procedures, (5) consulting services involved, (6) medication changes made that day, and (7) the patient's expected discharge date. In addition, each pair was asked the name of the other team member (ie, the nurse was asked the name of the intern and vice versa), and whether or not the patient care plan for the day had been discussed with the other team member, where concordance was defined as both members agreeing the plan had been discussed. All responses were recorded verbatim. Pairs were surveyed independently between 12 pm and 2 pm, limiting confounding by evolving plans of care over time.
Each set of surveys were then reviewed by 2 of 4 trained adjudicators, and responses to each question were scored as complete, partial, or no agreement. Rules for degree of agreement were based upon previously utilized parameters[9] as well as biweekly meetings during which common themes and disagreements in ratings were discussed, and rules generated to create consensus (see Supporting Information, Appendix, in the online version of this article).
Adverse Event Detection
Of the patients meeting eligibility criteria, 200 patients were randomly selected using computer‐generated randomization from each time period for AE outcome assessment, for a total of 400 patients.
Each patient's electronic medical record was retrospectively reviewed by a trained clinician using a previously validated screening tool to detect any possible AEs.[11] Any positive screen prompted documentation of a narrative summary including a short description of the possible AE and pertinent associated data. We defined AE as any injury due to medical management rather than the natural history of the illness, and further limited this definition to only include AEs that occurred on the study unit or as a result of care on that unit.
Two of 4 trained adjudicators, blinded to time period, then separately reviewed each narrative summary using previously validated 6‐point confidence scales to determine the presence and preventability of AE, with confidence ratings of 4 or greater used as cutoffs.[11] All AEs were also scored on a 4‐point severity scale (trivial, clinically significant, serious, or life threatening), with severe AE defined as serious or life threatening. Lastly, adjudicators grouped AEs into 1 of 10 prespecified categories.[11] Any disagreements in ratings or groupings were discussed by all 4 adjudicators to reach consensus.
Data Analysis
Patient characteristics are presented using descriptive statistics and were compared in the pre‐ and postregionalization time periods using 2 or t tests as appropriate.
To analyze whether regionalized care was associated with concordance of plan, adjudicated survey questions were assigned points of 1, 0.5, and 0 for complete, partial, and no agreement, respectively. Total mean concordance scores for any patient ranged from 0 to 7 points, and were divided by total number of answered questions (up to 7) for a range of 0 to 1. Total mean concordance scores as well as mean concordance score per survey question were compared pre‐ versus postregionalization using t tests. In sensitivity analyses, adjudicated survey responses were dichotomized with complete and partial agreement deemed concordant responses. Percent concordance for each question was then compared pre‐ versus postregionalization using 2 analysis. Questions about the name of the other team member and discussion of daily care plan with the other team member were excluded from total concordance score calculations and were compared individually pre‐ versus postregionalization, because they are not directly about the plan of care.
To analyze the association of regionalization with odds of preventable AE, we performed multivariable logistic regression adjusted for patient age, sex, race, language, and Elixhauser comorbidity score,[16] and utilized generalized estimating equations to account for clustering by hospital unit. Secondary outcomes included severe preventable AEs, nonpreventable AEs, and category of preventable AEs using similar methodology. Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (SAS Institute Inc., Cary, NC) was used for all analyses.
RESULTS
The fidelity of the intervention in achieving its goal of regionalized care is discussed separately.[15] Briefly, the intervention was successful at achieving 85% regionalization by team (ie, average daily percentage of team's patients assigned to team's unit) and 87% regionalization by unit (ie, average daily percentage of unit's patients with assigned team) following implementation, compared to 20% regionalization by team and unit in the preintervention period. Importantly, the average daily census of physician care teams rose by 32%, from a mean of 10.8 patients/physician care team preregionalization to a mean of 14.3 patients/physician care team postregionalization.
Concordance of Plan
Of the 418 nurse and intern paired surveys, 4 surveys were excluded due to repeat surveys of the same patient during the same hospitalization, for a total of 197 distinct paired surveys preregionalization and 217 paired surveys postregionalization. There were no statistically significant differences in patients' age, sex, race, language, admission source, length of stay, Elixhauser comorbidity score and diagnosis‐related group weight pre‐ versus postregionalization (Table 1).
| Characteristic | Concordance of Care Plan | Adverse Events | ||||
|---|---|---|---|---|---|---|
| Pre, n = 197 | Post, n = 217 | P Value | Pre, n = 198 | Post, n = 194 | P Value | |
| ||||||
| Age, mean (SD) | 60.5 (19.4) | 57.6 (20.8) | 0.15 | 60.4 (18.9) | 58.0 (21.2) | 0.24 |
| Male, n (%) | 77 (39.1) | 92 (42.4) | 0.49 | 94 (47.5) | 85 (43.8) | 0.55 |
| Race/ethnicity, n (%) | 0.34 | 0.12 | ||||
| White | 134 (68.0) | 141 (65.0) | 132 (66.5) | 121 (62.4) | ||
| Black | 42 (21.3) | 45 (20.7) | 41 (20.8) | 54 (27.8) | ||
| Hispanic | 18 (9.1) | 21 (9.7) | 22 (11.3) | 13 (6.8) | ||
| Other/unknown | 3 (1.5) | 10 (4.6) | 3 (1.4) | 6 (2.9) | ||
| Language, n (%) | 0.30 | 0.73 | ||||
| English | 183 (92.9) | 203 (93.5) | 176 (88.7) | 175 (90.2) | ||
| Spanish | 6 (3.0) | 10 (4.6) | 10 (5.2) | 10 (5.3) | ||
| Other | 8 (4.1) | 4 (1.8) | 12 (6.1) | 9 (4.5) | ||
| Admitting source, n (%) | 1.00 | 0.10 | ||||
| Physician office | 13 (6.6) | 13 (6.0) | 13 (6.6) | 6 (3.1) | ||
| Emergency department | 136 (69.0) | 150 (69.1) | 126 (63.6) | 127 (65.5) | ||
| Transfer from different hospital | 40 (20.3) | 45 (20.7) | 54 (27.3) | 50 (25.8) | ||
| Transfer from skilled nursing facility | 8 (4.1) | 9 (4.2) | 5 (2.5) | 11 (5.6) | ||
| Length of stay, d, median (IQR) | 3.0 (4.0) | 3.0 (4.0) | 0.57 | 4.0 (5.0) | 3.0 (4.0) | 0.16 |
| Elixhauser Comorbidity Score, mean (SD) | 8.0 (8.8) | 8.3 (9.3) | 0.74 | 8.0 (8.6) | 7.8 (8.4) | 0.86 |
| DRG weight, mean (SD) | 1.6 (1.0) | 1.5 (1.0) | 0.37 | 1.5 (0.93) | 1.5 (1.1) | 0.96 |
Kappa scores for adjudications of concordance surveys (defined as both adjudicators scoring the same level of agreement (ie, both complete or partial agreement versus no agreement) ranged from 0.69 to 0.95, by question. There were no significant differences in total mean concordance scores in the care plan pre‐ versus postregionalization (0.65 vs 0.67, P = 0.26) (Table 2). Similarly, there were no significant differences in mean concordance score for each survey question, except agreement on expected date of discharge (0.56 vs 0.68, P = 0.003), knowledge of the other provider's name, and agreement that discussion of the daily plan had taken place with the other pair member. Similar results were seen when results were dichotomized (ie, partial or complete agreement vs no agreement) (Table 2).
| Concordance Outcome | Pre, n = 197 | Post, n = 217 | P Value |
|---|---|---|---|
| |||
| Concordance score* | |||
| Total concordance score, mean (SD) | 0.65 (0.17) | 0.67 (0.16) | 0.26 |
| Subgroups | |||
| Diagnosis | 0.77 (0.32) | 0.72 (0.35) | 0.11 |
| Patient's chief concern | 0.48 (0.44) | 0.48 (0.43) | 0.94 |
| Tests today | 0.67 (0.40) | 0.71 (0.42) | 0.36 |
| Procedures today | 0.93 (0.25) | 0.92 (0.25) | 0.71 |
| Medication changes today | 0.56 (0.44) | 0.59 (0.43) | 0.54 |
| Consulting services | 0.59 (0.44) | 0.60 (0.44) | 0.82 |
| Expected discharge date | 0.56 (0.44) | 0.68 (0.38) | 0.003 |
| Responding clinician knowledge of nurse's name | 0.56 (0.50) | 0.86 (0.35) | <0.001 |
| Nurse's knowledge of responding clinician's name | 0.56 (0.50) | 0.88 (0.33) | <0.001 |
| Plan discussed | 0.73 (0.45) | 0.88 (0.32) | <0.001 |
| Percent concordance, mean (SD) | |||
| Diagnosis | 92.0 (27.3) | 88.6 (31.9) | 0.25 |
| Patient's chief concern | 59.6 (49.1) | 60.6 (49.0) | 0.84 |
| Tests today | 78.9 (40.9) | 77.2 (42.1) | 0.67 |
| Procedures today | 93.5 (24.8) | 94.1 (23.7) | 0.80 |
| Medication changes today | 66.3 (33.6) | 69.9 (46.0) | 0.44 |
| Consulting services | 69.3 (46.2) | 68.9 (46.4) | 0.93 |
| Expected discharge date | 67.5 (47.0) | 82.6 (38.0) | <0.001 |
| Responding clinician knowledge of nurse's name | 55.7 (49.8) | 85.6 (35.2) | <0.001 |
| Nurse's knowledge of responding clinician's name | 55.9 (49.8) | 87.9 (32.8) | <0.001 |
| Plan discussed | 72.9 (44.6) | 88.2 (32.3) | <0.001 |
Adverse Events
Of the 400 patients screened for AEs, 8 were excluded due to missing medical record number (5) and discharge outside of study period (3). Of the final 392 patient screens (198 pre, 194 post), there were no significant differences in patients' age, sex, race, language, length of stay, or Elixhauser score pre‐ versus postregionalization (Table 1).
Kappa scores for adjudicator agreement were 0.35 for presence of AE and 0.34 for preventability of AE. Of the 392 reviewed patient records, there were 133 total AEs detected (66 pre, 67 post), 27 preventable AEs (13 pre, 14 post), and 9 severe preventable AEs (4 pre, 5 post) (Table 3). There was no significant difference in the adjusted odds of preventable AEs post‐ versus preregionalization (adjusted odds ratio: 1.37, 95% confidence interval: 0.69, 2.69). Although the low number of AEs rated as severe or life threatening precluded adjusted analysis, unadjusted results similarly demonstrated no difference in odds of severe preventable AEs pre‐ versus postregionalization. As expected, there was no significant difference in adjusted odds of nonpreventable AE after implementation of regionalized care (Table 3).
| Adverse Events | No. of Adverse Events | Adjusted Odds Ratio Post vs Pre (95% CI) | |
|---|---|---|---|
| Pre, n = 198 | Post, n = 194 | ||
| |||
| Preventable | 13 | 14 | 1.37 (0.69, 2.69) |
| Serious and preventable | 4 | 5 | |
| Nonpreventable | 47 | 50 | 1.20 (0.85, 1.75) |
Similarly, there were no significant differences in category of preventable AE pre‐ versus postregionalization. The most frequent preventable AEs in both time periods were those related to adverse drug events and to manifestations of poor glycemic control, examples of which are illustrated (Table 4).
| |
| Adverse drug event | 29‐year‐old male with history of alcohol abuse, complicated by prior withdrawal seizures/emntensive care unit admissions, presented with alcohol withdrawal. Started on standing and PRN lorazepam, kept on home medications including standing clonidine, gabapentin, citalopram, quetiapine. Became somnolent due to polypharmacy, ultimately discontinued quetiapine as discovered took only as needed at home for insomnia |
| Manifestations of poor glycemic control | 78‐year‐old male with recently diagnosed lymphoma, distant history of bladder and prostate cancer status post ileal loop diversion, presented status post syncopal event; during event, spilled boiling water on himself leading to second‐degree burns on 3% of his body. Initially admitted to trauma/burn service, ultimately transferred to medical service for ongoing multiple medical issues including obstructive uropathy, acute on chronic renal failure. Adverse event was hyperglycemia (>350 mg/dL on >2 consecutive readings) in the setting of holding his home insulin detemir and insulin aspart (had been placed on insulin aspart sliding scale alone). After hyperglycemic episodes, was placed back on weight‐based basal/nutritional insulin |
DISCUSSION
In this study of general medicine patients at a large academic medical center, we found that regionalization of care teams on general medicine services was associated with improved recognition of care team members and agreement on estimated date of patient discharge, but was not associated with improvement in overall nurse and physician concordance of the patient care plan, or the odds of preventable AEs.
This intervention importantly addresses the barrier of dispersion of team membership, a well‐recognized barrier to interdisciplinary collaboration,[17, 18] particularly with resident physician teams due to frequently changing team membership. Localization of all team members, in addition to encouragement of daily collaborative bedside rounds as part of the regionalization initiative, likely contributed to our observed improvement in team member identification and discussion of daily care plans. Similarly, regionalization resulted in improved agreement in estimations of date of patient discharge. Focus on early patient discharges was an integral part of the implementation efforts; we therefore hypothesize that mutual focus on discharge planning by both nurses and responding clinicians may have explained this observed result.
On the other hand, regionalization did not appreciably improve the overall concordance of care plan between nurses and interns, despite a significant increase in team members agreeing that the plan had been discussed. Our findings support similar prior research demonstrating that regionalizing hospitalist attendings to single nursing units had limited impact on agreement of care plan between physicians and nurses.[13] Similarly, in settings where physicians and nurses are inherently regionalized, such as the intensive care unit[4] or the operating room,[3] communication between physicians and nurses remains difficult. Collectively, our findings suggest that colocalization of physicians and nurses alone is likely insufficient to improve measured communication between care team members. Existing literature suggests that more standardized approaches to improve communication, such as structured communication tools used during daily inpatient care[19, 20] or formalized team training,[21, 22, 23] lead to improvements in communication and collaboration. Despite these findings, it is important to highlight that this study did not assess other measures of workplace culture, such as teamwork and care team cohesiveness, which may have been positively affected by this intervention, even without measurable effect on concordance of care plan. Additionally, as noted, the average daily census on each team increased by almost a third postintervention, which may have impeded improvements in care team communication.
In addition, we found that our intervention had no significant impact on preventable AEs or severe preventable AEs. Although we cannot exclude the possibility that more subtle AEs were missed with our methodology, our results indicate that regionalized care alone may be inadequate to improve major patient safety outcomes. As discussed, the volume of patients did increase postintervention; thus, another way to state our results is that we were able to increase the daily volume of patients without any significant decreases in patient safety. Nevertheless, the results on patient safety were less than desired. A recent review of interdisciplinary team care interventions on general medical wards similarly demonstrated underwhelming improvements in patient safety outcomes, although the reviewed interventions did not specifically address preventable AEs, a gap in the literature commented on by the authors.[24] Other albeit limited literature has demonstrated improvement in patient safety outcomes via multifaceted efforts aimed at improving care team member communication. Notably, these efforts include colocalization of care team members to single units but also involve additional measures to improve communication and collaboration between care team members, such as structured communication during interdisciplinary rounds, and certification of key interdisciplinary teamwork skills.[11, 14] Although our regionalized care intervention included many similar features to these accountable care units (ACUs) including unit‐based care teams, unit‐level performance reporting, and unit‐based physician and nursing coleadership, significant differences existed. Notably, in addition to the above features, the ACU model also incorporated highly structured communication models for interdisciplinary rounding, and certification processes to ensure an appropriate communication skill base among care team members.[14] Thus, although creation of regionalized care teams is likely a necessary precursor to implementation of these additional measures, alone it may be insufficient to improve patient safety outcomes.
Importantly, in our study we identified that adverse drug events and manifestations of poor glycemic control occurred in high frequency both before and following implementation of regionalized care, supporting other literature that describes the prevalence of these AEs.[11, 25, 26, 27] These results suggest that targeted interventions to address these specific AEs are likely necessary. Notably, the intervention units in our study did not consistently employ clinical pharmacists assigned specifically to that unit's care team to allow for integration within the care team. As prior research has suggested that greater collaboration with clinical pharmacists results in reduction of adverse drug events,[28] next steps may include improved integration of team‐based pharmacists into the activities of the regionalized care teams. Inpatient management of diabetes also requires specific interventions,[29, 30, 31] only some of which may be addressable by having regionalized care and better interdisciplinary communication.
Our findings are subject to several limitations. First, this was a single‐site study and thus our findings may not be generalizable to other institutions. However, regionalized care is increasingly encouraged to optimize communication between care team members.[17, 18] Therefore, our null findings may be pertinent to other institutions looking to improve patient safety outcomes, demonstrating that additional initiatives will likely be required. Second, our modes of outcome measurement possess limitations. In measuring concordance of care plan, although previously used survey techniques were employed,[9] the concordance survey has not been formally validated, and we believe some of the questions may have led to ambiguity on the part of the responders that may have resulted in less accurate responses, thus biasing toward the null. Similarly, in measuring AEs, the screening tool relied on retrospective chart review looking for specific AE types[11] and thus may not have captured more subtle AEs. Additionally, our study may have been underpowered to demonstrate significant reduction in preventable AEs, although other studies of similar methodology demonstrated significant results with similar sample size.[11] This was due in part to our lower‐than‐expected baseline AE rate (6.6% compared with approximately 10.3% in previous studies).[11] Lastly, our study solely examined the association of regionalization with concordance of care plan and preventable AEs, but importantly excluded other clinically important outcomes that may have been positively (or negatively) impacted by these regionalization efforts, such as ED wait times, provider efficiency (eg, fewer pages, less time in transit, more time at the bedside), interdisciplinary teamwork, or patient or provider satisfaction.
CONCLUSION
In summary, our findings suggest that regionalized care teams alone may be insufficient to effectively promote communication between care team members regarding the care plan or to lead to improvements in patient safety, although we recognize that there may have been benefits (or unintended harms) not measured in this study but are nonetheless important for clinical care and workplace culture. This is an important lesson, as many hospitals move toward regionalized care in an effort to improve patient safety outcomes. However, strengthening the infrastructure by colocalizing care team members to maximize opportunity for communication is likely a necessary first step toward facilitating implementation of additional initiatives that may lead to more robust patient safety improvements, such as structured interdisciplinary bedside rounds (eg, facilitating and training all team members to fulfill specific roles), teamwork training, and certification of key interdisciplinary teamwork skills. Additionally, close examination of identified prevalent and preventable AEs can help to determine which additional initiatives are most likely to have greatest impact in improving patient safety.
Disclosures: This research was supported by funds provided by Brigham and Women's Hospital (BWH) and by funds provided by the Department of Medicine at BWH. All authors had full access to all of the data in the study and were integrally involved in the design, implementation, data collection, and analyses. The first author, Dr. Stephanie Mueller, takes responsibility for the integrity for the data and the accuracy of the data analysis. Dr. Schnipper reports grants from Sanofi Aventis, outside the submitted work.
- Joint Commission on Accreditation of Healthcare Organizations. Understanding and Preventing Sentinel Events in Your Health Care Organization. Oak Brook, IL: Joint Commission; 2008.
- , , , et al. Communication failures in the operating room: an observational classification of recurrent types and effects. Qual Saf Health Care. 2004;13(5):330–334.
- , , , et al. Operating room teamwork among physicians and nurses: teamwork in the eye of the beholder. J Am Coll Surg. 2006;202(5):746–752.
- , , . Discrepant attitudes about teamwork among critical care nurses and physicians. Crit Care Med. 2003;31(3):956–959.
- , , , , . Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
- , , , et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
- , , , et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28–E33.
- , , , , , . Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841.
- , , , et al. Patterns of nurse‐physician communication and agreement on the plan of care. Qual Saf Health Care. 2010;19(3):195–199.
- , , , , , . Can we talk? Priorities for patient care differed among health care providers. In: Henriksen K, Battles JB, Marks ES, Lewin DI, eds. Advances in Patient Safety: From Research to Implementation. Vol 1. Rockville, MD: Agency for Healthcare Research and Quality; 2005.
- , , , et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684.
- , , , , , . Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6(2):88–93.
- , , , et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227.
- , , , et al. Reorganizing a hospital ward as an accountable care unit. J Hosp Med. 2015;10(1):36–40.
- , , , et al. 5th time's a charm: creation of unit‐based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10 (suppl. 2). Available at: http://www.shmabstracts.com/abstract/5th‐times‐a‐charm‐creation‐of‐unit‐based‐care‐teams‐in‐a‐high‐occupancy‐hospital. Accessed July 28, 2015.
- , , , . Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
- , , , , , . Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19(2):117–121.
- , , , et al. A model for quality improvement programs in academic departments of medicine. Am J Med. 2008;121(10):922–929.
- , , , , . Improving nurse‐physician communication and satisfaction in the intensive care unit with a daily goals worksheet. Am J Crit Care. 2006;15(2):217–222.
- , , , , , . Improving communication in the ICU using daily goals. J Crit Care. 2003;18(2):71–75.
- , , , et al. Effect of crew resource management training in a multidisciplinary obstetrical setting. Int J Qual Health Care. 2008;20(4):254–263.
- , , , et al. Error reduction and performance improvement in the emergency department through formal teamwork training: evaluation results of the MedTeams project. Health Serv Res. 2002;37(6):1553–1581.
- , , , et al. Effects of teamwork training on adverse outcomes and process of care in labor and delivery: a randomized controlled trial. Obstet Gynecol. 2007;109(1):48–55.
- , , , et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288–1298.
- , , , et al. Patient risk factors for adverse drug events in hospitalized patients. ADE Prevention Study Group. Arch Intern Med. 1999;159(21):2553–2560.
- , , , et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10.
- , , , . Use of a standardized protocol to decrease medication errors and adverse events related to sliding scale insulin. Qual Saf Health Care. 2006;15(2):89–91.
- , , , . Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955–964.
- , , , , . Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: effect of structured subcutaneous insulin orders and an insulin management algorithm. J Hosp Med. 2009;4(1):3–15.
- , , , . Effects of a computerized order set on the inpatient management of hyperglycemia: a cluster‐randomized controlled trial. Endocr Pract. 2010;16(2):209–218.
- , , , . Effects of a subcutaneous insulin protocol, clinical education, and computerized order set on the quality of inpatient management of hyperglycemia: results of a clinical trial. J Hosp Med. 2009;4(1):16–27.
Failures in communication among healthcare professionals are known threats to patient safety. These failures account for over 60% of root causes of sentinel events, the most serious events reported to The Joint Commission.[1] As such, identifying both patterns of effective communication as well as barriers to successful communication has been a focus of efforts aimed at improving patient safety. However, to date, the majority of this work has centered on improving communication in settings such as the operating room and intensive care unit,[2, 3, 4] or at times of care transitions.[5, 6, 7, 8]
Unique barriers exist for effective interdisciplinary communication in the hospital setting, particularly physiciannurse communication regarding shared hospitalized patients.[9] Traditionally, care of hospitalized patients is provided by physicians, nurses, and other team members working in varied workflow patterns, leading to dispersed team membership, where each team member cares for different groups of patients in different locations across the hospital. This dispersion is further heightened on teaching services, where residents' rotation schedules lead to frequent changes of care team membership, leaving inpatient care teams particularly vulnerable to ineffective communication. Evidence suggests that communication between nurses and physicians is currently suboptimal, leading to frequent disagreement regarding the patient's plan of care.[9, 10] This divergence between physician and nursing perceptions of patients' care plans may leave patients at greater risk of adverse events (AEs).
Several studies have examined the effects of regionalized inpatient care teams, where multidisciplinary team members care for the same patients on the same hospital unit, on communication and patient outcomes.[4, 11, 12, 13, 14] Results of these studies have been inconsistent, perhaps due to the particular characteristics of the care teams or to the study methodology. Thus, further rigorously done studies are required to better understand the impact of team regionalization on patient care. The goal of this study was to examine whether the implementation of regionalized inpatient care teams was associated with improvements in care team communication and preventable AEs.
METHODS
Setting, Patients, and Study Design
We performed a cohort analysis of patients at a 700‐bed tertiary care center, pre‐ and postregionalization of inpatient general medicine care teams. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee. Patients were eligible for inclusion if they were 18 years of age or older and discharged from the general medicine service (GMS) from any of the 3 participating nursing units between April 1, 2012 and June 19, 2012 (preregionalization) or April 1, 2013 and June 19, 2013 (postregionalization).
Intervention
On June 20, 2012, regionalized care was implemented on the GMS such that each of 3 GMS teams was localized to 1 of 3, 15‐bed nursing units. Prior to regionalization, the GMS physician care teams, each consisting of 1 hospitalist attending, 1 medical resident, and 2 medical interns, would care for patients on an average of 7 and up to 13 different nursing units on a given day.
Regionalized care consisted of a multifaceted intervention codeveloped by hospitalist, residency, nursing, emergency department, and hospital leadership and included: (1) regionalizing GMS teams as much as possible; (2) change in resident call structure from a traditional 4‐day call cycle to daily admitting; (3) collaborative efforts to enhance GMS patient discharges before noon to promote regionalized placement of patients without prolonging time in the emergency department (ED); (4) daily morning and postround multidisciplinary huddles to prioritize sicker patients and discharges; (5) encouragement of daily rounds at patients' bedsides with presence of physician team, nurse, and team pharmacist if available; (6) creation of unit‐ and team‐level performance reports; and (7) creation of unit‐based physician and nursing co‐leadership (Figure 1).[15]
Concordance of Plan
Concordance of plan was measured via a 7‐question survey previously developed, pilot tested, and used to measure the impact of regionalized care on care team communication between inpatient nursephysician team members.[9] The survey was administered in‐person by 1 of 8 trained research assistants (RAs) (4/emntervention period) to nurse and intern pairs caring for patients on the study units pre‐ and postregionalization. GMS patients were eligible for inclusion if surveys could be administered to their nurse and intern within the first 24 hours of admission to the unit and within 48 hours of admission to the hospital, based on RA availability (thus excluding patients admitted on Fridays as surveys were not conducted over the weekend). Most often, all eligible patients admitted to the study units during time periods of data collection were included in the study. On limited occasions, the daily supply of patients surpassed RA capacity for inclusion, at which time computer‐generated randomization was utilized to randomly select patients for inclusion. Nurse and intern pairs were surveyed once during a patient's hospitalization, although they could be surveyed more than once about different patients, and patients could be included more than once if rehospitalized on the study unit and cared for by a different nurseintern pair. Of the 472 selected eligible patients, the nurses and interns of 418 patients were available and consented to survey administration, representing 361 unique nurse and intern pairs and 399 unique patients.
Each member of the pair was asked about 7 specific aspects of the patient's care plan for that day in isolation from the other team member, including: (1) the patient's primary diagnosis, (2) the patient's expressed chief concern, (3) the day's scheduled tests, (4) the day's scheduled procedures, (5) consulting services involved, (6) medication changes made that day, and (7) the patient's expected discharge date. In addition, each pair was asked the name of the other team member (ie, the nurse was asked the name of the intern and vice versa), and whether or not the patient care plan for the day had been discussed with the other team member, where concordance was defined as both members agreeing the plan had been discussed. All responses were recorded verbatim. Pairs were surveyed independently between 12 pm and 2 pm, limiting confounding by evolving plans of care over time.
Each set of surveys were then reviewed by 2 of 4 trained adjudicators, and responses to each question were scored as complete, partial, or no agreement. Rules for degree of agreement were based upon previously utilized parameters[9] as well as biweekly meetings during which common themes and disagreements in ratings were discussed, and rules generated to create consensus (see Supporting Information, Appendix, in the online version of this article).
Adverse Event Detection
Of the patients meeting eligibility criteria, 200 patients were randomly selected using computer‐generated randomization from each time period for AE outcome assessment, for a total of 400 patients.
Each patient's electronic medical record was retrospectively reviewed by a trained clinician using a previously validated screening tool to detect any possible AEs.[11] Any positive screen prompted documentation of a narrative summary including a short description of the possible AE and pertinent associated data. We defined AE as any injury due to medical management rather than the natural history of the illness, and further limited this definition to only include AEs that occurred on the study unit or as a result of care on that unit.
Two of 4 trained adjudicators, blinded to time period, then separately reviewed each narrative summary using previously validated 6‐point confidence scales to determine the presence and preventability of AE, with confidence ratings of 4 or greater used as cutoffs.[11] All AEs were also scored on a 4‐point severity scale (trivial, clinically significant, serious, or life threatening), with severe AE defined as serious or life threatening. Lastly, adjudicators grouped AEs into 1 of 10 prespecified categories.[11] Any disagreements in ratings or groupings were discussed by all 4 adjudicators to reach consensus.
Data Analysis
Patient characteristics are presented using descriptive statistics and were compared in the pre‐ and postregionalization time periods using 2 or t tests as appropriate.
To analyze whether regionalized care was associated with concordance of plan, adjudicated survey questions were assigned points of 1, 0.5, and 0 for complete, partial, and no agreement, respectively. Total mean concordance scores for any patient ranged from 0 to 7 points, and were divided by total number of answered questions (up to 7) for a range of 0 to 1. Total mean concordance scores as well as mean concordance score per survey question were compared pre‐ versus postregionalization using t tests. In sensitivity analyses, adjudicated survey responses were dichotomized with complete and partial agreement deemed concordant responses. Percent concordance for each question was then compared pre‐ versus postregionalization using 2 analysis. Questions about the name of the other team member and discussion of daily care plan with the other team member were excluded from total concordance score calculations and were compared individually pre‐ versus postregionalization, because they are not directly about the plan of care.
To analyze the association of regionalization with odds of preventable AE, we performed multivariable logistic regression adjusted for patient age, sex, race, language, and Elixhauser comorbidity score,[16] and utilized generalized estimating equations to account for clustering by hospital unit. Secondary outcomes included severe preventable AEs, nonpreventable AEs, and category of preventable AEs using similar methodology. Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (SAS Institute Inc., Cary, NC) was used for all analyses.
RESULTS
The fidelity of the intervention in achieving its goal of regionalized care is discussed separately.[15] Briefly, the intervention was successful at achieving 85% regionalization by team (ie, average daily percentage of team's patients assigned to team's unit) and 87% regionalization by unit (ie, average daily percentage of unit's patients with assigned team) following implementation, compared to 20% regionalization by team and unit in the preintervention period. Importantly, the average daily census of physician care teams rose by 32%, from a mean of 10.8 patients/physician care team preregionalization to a mean of 14.3 patients/physician care team postregionalization.
Concordance of Plan
Of the 418 nurse and intern paired surveys, 4 surveys were excluded due to repeat surveys of the same patient during the same hospitalization, for a total of 197 distinct paired surveys preregionalization and 217 paired surveys postregionalization. There were no statistically significant differences in patients' age, sex, race, language, admission source, length of stay, Elixhauser comorbidity score and diagnosis‐related group weight pre‐ versus postregionalization (Table 1).
| Characteristic | Concordance of Care Plan | Adverse Events | ||||
|---|---|---|---|---|---|---|
| Pre, n = 197 | Post, n = 217 | P Value | Pre, n = 198 | Post, n = 194 | P Value | |
| ||||||
| Age, mean (SD) | 60.5 (19.4) | 57.6 (20.8) | 0.15 | 60.4 (18.9) | 58.0 (21.2) | 0.24 |
| Male, n (%) | 77 (39.1) | 92 (42.4) | 0.49 | 94 (47.5) | 85 (43.8) | 0.55 |
| Race/ethnicity, n (%) | 0.34 | 0.12 | ||||
| White | 134 (68.0) | 141 (65.0) | 132 (66.5) | 121 (62.4) | ||
| Black | 42 (21.3) | 45 (20.7) | 41 (20.8) | 54 (27.8) | ||
| Hispanic | 18 (9.1) | 21 (9.7) | 22 (11.3) | 13 (6.8) | ||
| Other/unknown | 3 (1.5) | 10 (4.6) | 3 (1.4) | 6 (2.9) | ||
| Language, n (%) | 0.30 | 0.73 | ||||
| English | 183 (92.9) | 203 (93.5) | 176 (88.7) | 175 (90.2) | ||
| Spanish | 6 (3.0) | 10 (4.6) | 10 (5.2) | 10 (5.3) | ||
| Other | 8 (4.1) | 4 (1.8) | 12 (6.1) | 9 (4.5) | ||
| Admitting source, n (%) | 1.00 | 0.10 | ||||
| Physician office | 13 (6.6) | 13 (6.0) | 13 (6.6) | 6 (3.1) | ||
| Emergency department | 136 (69.0) | 150 (69.1) | 126 (63.6) | 127 (65.5) | ||
| Transfer from different hospital | 40 (20.3) | 45 (20.7) | 54 (27.3) | 50 (25.8) | ||
| Transfer from skilled nursing facility | 8 (4.1) | 9 (4.2) | 5 (2.5) | 11 (5.6) | ||
| Length of stay, d, median (IQR) | 3.0 (4.0) | 3.0 (4.0) | 0.57 | 4.0 (5.0) | 3.0 (4.0) | 0.16 |
| Elixhauser Comorbidity Score, mean (SD) | 8.0 (8.8) | 8.3 (9.3) | 0.74 | 8.0 (8.6) | 7.8 (8.4) | 0.86 |
| DRG weight, mean (SD) | 1.6 (1.0) | 1.5 (1.0) | 0.37 | 1.5 (0.93) | 1.5 (1.1) | 0.96 |
Kappa scores for adjudications of concordance surveys (defined as both adjudicators scoring the same level of agreement (ie, both complete or partial agreement versus no agreement) ranged from 0.69 to 0.95, by question. There were no significant differences in total mean concordance scores in the care plan pre‐ versus postregionalization (0.65 vs 0.67, P = 0.26) (Table 2). Similarly, there were no significant differences in mean concordance score for each survey question, except agreement on expected date of discharge (0.56 vs 0.68, P = 0.003), knowledge of the other provider's name, and agreement that discussion of the daily plan had taken place with the other pair member. Similar results were seen when results were dichotomized (ie, partial or complete agreement vs no agreement) (Table 2).
| Concordance Outcome | Pre, n = 197 | Post, n = 217 | P Value |
|---|---|---|---|
| |||
| Concordance score* | |||
| Total concordance score, mean (SD) | 0.65 (0.17) | 0.67 (0.16) | 0.26 |
| Subgroups | |||
| Diagnosis | 0.77 (0.32) | 0.72 (0.35) | 0.11 |
| Patient's chief concern | 0.48 (0.44) | 0.48 (0.43) | 0.94 |
| Tests today | 0.67 (0.40) | 0.71 (0.42) | 0.36 |
| Procedures today | 0.93 (0.25) | 0.92 (0.25) | 0.71 |
| Medication changes today | 0.56 (0.44) | 0.59 (0.43) | 0.54 |
| Consulting services | 0.59 (0.44) | 0.60 (0.44) | 0.82 |
| Expected discharge date | 0.56 (0.44) | 0.68 (0.38) | 0.003 |
| Responding clinician knowledge of nurse's name | 0.56 (0.50) | 0.86 (0.35) | <0.001 |
| Nurse's knowledge of responding clinician's name | 0.56 (0.50) | 0.88 (0.33) | <0.001 |
| Plan discussed | 0.73 (0.45) | 0.88 (0.32) | <0.001 |
| Percent concordance, mean (SD) | |||
| Diagnosis | 92.0 (27.3) | 88.6 (31.9) | 0.25 |
| Patient's chief concern | 59.6 (49.1) | 60.6 (49.0) | 0.84 |
| Tests today | 78.9 (40.9) | 77.2 (42.1) | 0.67 |
| Procedures today | 93.5 (24.8) | 94.1 (23.7) | 0.80 |
| Medication changes today | 66.3 (33.6) | 69.9 (46.0) | 0.44 |
| Consulting services | 69.3 (46.2) | 68.9 (46.4) | 0.93 |
| Expected discharge date | 67.5 (47.0) | 82.6 (38.0) | <0.001 |
| Responding clinician knowledge of nurse's name | 55.7 (49.8) | 85.6 (35.2) | <0.001 |
| Nurse's knowledge of responding clinician's name | 55.9 (49.8) | 87.9 (32.8) | <0.001 |
| Plan discussed | 72.9 (44.6) | 88.2 (32.3) | <0.001 |
Adverse Events
Of the 400 patients screened for AEs, 8 were excluded due to missing medical record number (5) and discharge outside of study period (3). Of the final 392 patient screens (198 pre, 194 post), there were no significant differences in patients' age, sex, race, language, length of stay, or Elixhauser score pre‐ versus postregionalization (Table 1).
Kappa scores for adjudicator agreement were 0.35 for presence of AE and 0.34 for preventability of AE. Of the 392 reviewed patient records, there were 133 total AEs detected (66 pre, 67 post), 27 preventable AEs (13 pre, 14 post), and 9 severe preventable AEs (4 pre, 5 post) (Table 3). There was no significant difference in the adjusted odds of preventable AEs post‐ versus preregionalization (adjusted odds ratio: 1.37, 95% confidence interval: 0.69, 2.69). Although the low number of AEs rated as severe or life threatening precluded adjusted analysis, unadjusted results similarly demonstrated no difference in odds of severe preventable AEs pre‐ versus postregionalization. As expected, there was no significant difference in adjusted odds of nonpreventable AE after implementation of regionalized care (Table 3).
| Adverse Events | No. of Adverse Events | Adjusted Odds Ratio Post vs Pre (95% CI) | |
|---|---|---|---|
| Pre, n = 198 | Post, n = 194 | ||
| |||
| Preventable | 13 | 14 | 1.37 (0.69, 2.69) |
| Serious and preventable | 4 | 5 | |
| Nonpreventable | 47 | 50 | 1.20 (0.85, 1.75) |
Similarly, there were no significant differences in category of preventable AE pre‐ versus postregionalization. The most frequent preventable AEs in both time periods were those related to adverse drug events and to manifestations of poor glycemic control, examples of which are illustrated (Table 4).
| |
| Adverse drug event | 29‐year‐old male with history of alcohol abuse, complicated by prior withdrawal seizures/emntensive care unit admissions, presented with alcohol withdrawal. Started on standing and PRN lorazepam, kept on home medications including standing clonidine, gabapentin, citalopram, quetiapine. Became somnolent due to polypharmacy, ultimately discontinued quetiapine as discovered took only as needed at home for insomnia |
| Manifestations of poor glycemic control | 78‐year‐old male with recently diagnosed lymphoma, distant history of bladder and prostate cancer status post ileal loop diversion, presented status post syncopal event; during event, spilled boiling water on himself leading to second‐degree burns on 3% of his body. Initially admitted to trauma/burn service, ultimately transferred to medical service for ongoing multiple medical issues including obstructive uropathy, acute on chronic renal failure. Adverse event was hyperglycemia (>350 mg/dL on >2 consecutive readings) in the setting of holding his home insulin detemir and insulin aspart (had been placed on insulin aspart sliding scale alone). After hyperglycemic episodes, was placed back on weight‐based basal/nutritional insulin |
DISCUSSION
In this study of general medicine patients at a large academic medical center, we found that regionalization of care teams on general medicine services was associated with improved recognition of care team members and agreement on estimated date of patient discharge, but was not associated with improvement in overall nurse and physician concordance of the patient care plan, or the odds of preventable AEs.
This intervention importantly addresses the barrier of dispersion of team membership, a well‐recognized barrier to interdisciplinary collaboration,[17, 18] particularly with resident physician teams due to frequently changing team membership. Localization of all team members, in addition to encouragement of daily collaborative bedside rounds as part of the regionalization initiative, likely contributed to our observed improvement in team member identification and discussion of daily care plans. Similarly, regionalization resulted in improved agreement in estimations of date of patient discharge. Focus on early patient discharges was an integral part of the implementation efforts; we therefore hypothesize that mutual focus on discharge planning by both nurses and responding clinicians may have explained this observed result.
On the other hand, regionalization did not appreciably improve the overall concordance of care plan between nurses and interns, despite a significant increase in team members agreeing that the plan had been discussed. Our findings support similar prior research demonstrating that regionalizing hospitalist attendings to single nursing units had limited impact on agreement of care plan between physicians and nurses.[13] Similarly, in settings where physicians and nurses are inherently regionalized, such as the intensive care unit[4] or the operating room,[3] communication between physicians and nurses remains difficult. Collectively, our findings suggest that colocalization of physicians and nurses alone is likely insufficient to improve measured communication between care team members. Existing literature suggests that more standardized approaches to improve communication, such as structured communication tools used during daily inpatient care[19, 20] or formalized team training,[21, 22, 23] lead to improvements in communication and collaboration. Despite these findings, it is important to highlight that this study did not assess other measures of workplace culture, such as teamwork and care team cohesiveness, which may have been positively affected by this intervention, even without measurable effect on concordance of care plan. Additionally, as noted, the average daily census on each team increased by almost a third postintervention, which may have impeded improvements in care team communication.
In addition, we found that our intervention had no significant impact on preventable AEs or severe preventable AEs. Although we cannot exclude the possibility that more subtle AEs were missed with our methodology, our results indicate that regionalized care alone may be inadequate to improve major patient safety outcomes. As discussed, the volume of patients did increase postintervention; thus, another way to state our results is that we were able to increase the daily volume of patients without any significant decreases in patient safety. Nevertheless, the results on patient safety were less than desired. A recent review of interdisciplinary team care interventions on general medical wards similarly demonstrated underwhelming improvements in patient safety outcomes, although the reviewed interventions did not specifically address preventable AEs, a gap in the literature commented on by the authors.[24] Other albeit limited literature has demonstrated improvement in patient safety outcomes via multifaceted efforts aimed at improving care team member communication. Notably, these efforts include colocalization of care team members to single units but also involve additional measures to improve communication and collaboration between care team members, such as structured communication during interdisciplinary rounds, and certification of key interdisciplinary teamwork skills.[11, 14] Although our regionalized care intervention included many similar features to these accountable care units (ACUs) including unit‐based care teams, unit‐level performance reporting, and unit‐based physician and nursing coleadership, significant differences existed. Notably, in addition to the above features, the ACU model also incorporated highly structured communication models for interdisciplinary rounding, and certification processes to ensure an appropriate communication skill base among care team members.[14] Thus, although creation of regionalized care teams is likely a necessary precursor to implementation of these additional measures, alone it may be insufficient to improve patient safety outcomes.
Importantly, in our study we identified that adverse drug events and manifestations of poor glycemic control occurred in high frequency both before and following implementation of regionalized care, supporting other literature that describes the prevalence of these AEs.[11, 25, 26, 27] These results suggest that targeted interventions to address these specific AEs are likely necessary. Notably, the intervention units in our study did not consistently employ clinical pharmacists assigned specifically to that unit's care team to allow for integration within the care team. As prior research has suggested that greater collaboration with clinical pharmacists results in reduction of adverse drug events,[28] next steps may include improved integration of team‐based pharmacists into the activities of the regionalized care teams. Inpatient management of diabetes also requires specific interventions,[29, 30, 31] only some of which may be addressable by having regionalized care and better interdisciplinary communication.
Our findings are subject to several limitations. First, this was a single‐site study and thus our findings may not be generalizable to other institutions. However, regionalized care is increasingly encouraged to optimize communication between care team members.[17, 18] Therefore, our null findings may be pertinent to other institutions looking to improve patient safety outcomes, demonstrating that additional initiatives will likely be required. Second, our modes of outcome measurement possess limitations. In measuring concordance of care plan, although previously used survey techniques were employed,[9] the concordance survey has not been formally validated, and we believe some of the questions may have led to ambiguity on the part of the responders that may have resulted in less accurate responses, thus biasing toward the null. Similarly, in measuring AEs, the screening tool relied on retrospective chart review looking for specific AE types[11] and thus may not have captured more subtle AEs. Additionally, our study may have been underpowered to demonstrate significant reduction in preventable AEs, although other studies of similar methodology demonstrated significant results with similar sample size.[11] This was due in part to our lower‐than‐expected baseline AE rate (6.6% compared with approximately 10.3% in previous studies).[11] Lastly, our study solely examined the association of regionalization with concordance of care plan and preventable AEs, but importantly excluded other clinically important outcomes that may have been positively (or negatively) impacted by these regionalization efforts, such as ED wait times, provider efficiency (eg, fewer pages, less time in transit, more time at the bedside), interdisciplinary teamwork, or patient or provider satisfaction.
CONCLUSION
In summary, our findings suggest that regionalized care teams alone may be insufficient to effectively promote communication between care team members regarding the care plan or to lead to improvements in patient safety, although we recognize that there may have been benefits (or unintended harms) not measured in this study but are nonetheless important for clinical care and workplace culture. This is an important lesson, as many hospitals move toward regionalized care in an effort to improve patient safety outcomes. However, strengthening the infrastructure by colocalizing care team members to maximize opportunity for communication is likely a necessary first step toward facilitating implementation of additional initiatives that may lead to more robust patient safety improvements, such as structured interdisciplinary bedside rounds (eg, facilitating and training all team members to fulfill specific roles), teamwork training, and certification of key interdisciplinary teamwork skills. Additionally, close examination of identified prevalent and preventable AEs can help to determine which additional initiatives are most likely to have greatest impact in improving patient safety.
Disclosures: This research was supported by funds provided by Brigham and Women's Hospital (BWH) and by funds provided by the Department of Medicine at BWH. All authors had full access to all of the data in the study and were integrally involved in the design, implementation, data collection, and analyses. The first author, Dr. Stephanie Mueller, takes responsibility for the integrity for the data and the accuracy of the data analysis. Dr. Schnipper reports grants from Sanofi Aventis, outside the submitted work.
Failures in communication among healthcare professionals are known threats to patient safety. These failures account for over 60% of root causes of sentinel events, the most serious events reported to The Joint Commission.[1] As such, identifying both patterns of effective communication as well as barriers to successful communication has been a focus of efforts aimed at improving patient safety. However, to date, the majority of this work has centered on improving communication in settings such as the operating room and intensive care unit,[2, 3, 4] or at times of care transitions.[5, 6, 7, 8]
Unique barriers exist for effective interdisciplinary communication in the hospital setting, particularly physiciannurse communication regarding shared hospitalized patients.[9] Traditionally, care of hospitalized patients is provided by physicians, nurses, and other team members working in varied workflow patterns, leading to dispersed team membership, where each team member cares for different groups of patients in different locations across the hospital. This dispersion is further heightened on teaching services, where residents' rotation schedules lead to frequent changes of care team membership, leaving inpatient care teams particularly vulnerable to ineffective communication. Evidence suggests that communication between nurses and physicians is currently suboptimal, leading to frequent disagreement regarding the patient's plan of care.[9, 10] This divergence between physician and nursing perceptions of patients' care plans may leave patients at greater risk of adverse events (AEs).
Several studies have examined the effects of regionalized inpatient care teams, where multidisciplinary team members care for the same patients on the same hospital unit, on communication and patient outcomes.[4, 11, 12, 13, 14] Results of these studies have been inconsistent, perhaps due to the particular characteristics of the care teams or to the study methodology. Thus, further rigorously done studies are required to better understand the impact of team regionalization on patient care. The goal of this study was to examine whether the implementation of regionalized inpatient care teams was associated with improvements in care team communication and preventable AEs.
METHODS
Setting, Patients, and Study Design
We performed a cohort analysis of patients at a 700‐bed tertiary care center, pre‐ and postregionalization of inpatient general medicine care teams. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee. Patients were eligible for inclusion if they were 18 years of age or older and discharged from the general medicine service (GMS) from any of the 3 participating nursing units between April 1, 2012 and June 19, 2012 (preregionalization) or April 1, 2013 and June 19, 2013 (postregionalization).
Intervention
On June 20, 2012, regionalized care was implemented on the GMS such that each of 3 GMS teams was localized to 1 of 3, 15‐bed nursing units. Prior to regionalization, the GMS physician care teams, each consisting of 1 hospitalist attending, 1 medical resident, and 2 medical interns, would care for patients on an average of 7 and up to 13 different nursing units on a given day.
Regionalized care consisted of a multifaceted intervention codeveloped by hospitalist, residency, nursing, emergency department, and hospital leadership and included: (1) regionalizing GMS teams as much as possible; (2) change in resident call structure from a traditional 4‐day call cycle to daily admitting; (3) collaborative efforts to enhance GMS patient discharges before noon to promote regionalized placement of patients without prolonging time in the emergency department (ED); (4) daily morning and postround multidisciplinary huddles to prioritize sicker patients and discharges; (5) encouragement of daily rounds at patients' bedsides with presence of physician team, nurse, and team pharmacist if available; (6) creation of unit‐ and team‐level performance reports; and (7) creation of unit‐based physician and nursing co‐leadership (Figure 1).[15]
Concordance of Plan
Concordance of plan was measured via a 7‐question survey previously developed, pilot tested, and used to measure the impact of regionalized care on care team communication between inpatient nursephysician team members.[9] The survey was administered in‐person by 1 of 8 trained research assistants (RAs) (4/emntervention period) to nurse and intern pairs caring for patients on the study units pre‐ and postregionalization. GMS patients were eligible for inclusion if surveys could be administered to their nurse and intern within the first 24 hours of admission to the unit and within 48 hours of admission to the hospital, based on RA availability (thus excluding patients admitted on Fridays as surveys were not conducted over the weekend). Most often, all eligible patients admitted to the study units during time periods of data collection were included in the study. On limited occasions, the daily supply of patients surpassed RA capacity for inclusion, at which time computer‐generated randomization was utilized to randomly select patients for inclusion. Nurse and intern pairs were surveyed once during a patient's hospitalization, although they could be surveyed more than once about different patients, and patients could be included more than once if rehospitalized on the study unit and cared for by a different nurseintern pair. Of the 472 selected eligible patients, the nurses and interns of 418 patients were available and consented to survey administration, representing 361 unique nurse and intern pairs and 399 unique patients.
Each member of the pair was asked about 7 specific aspects of the patient's care plan for that day in isolation from the other team member, including: (1) the patient's primary diagnosis, (2) the patient's expressed chief concern, (3) the day's scheduled tests, (4) the day's scheduled procedures, (5) consulting services involved, (6) medication changes made that day, and (7) the patient's expected discharge date. In addition, each pair was asked the name of the other team member (ie, the nurse was asked the name of the intern and vice versa), and whether or not the patient care plan for the day had been discussed with the other team member, where concordance was defined as both members agreeing the plan had been discussed. All responses were recorded verbatim. Pairs were surveyed independently between 12 pm and 2 pm, limiting confounding by evolving plans of care over time.
Each set of surveys were then reviewed by 2 of 4 trained adjudicators, and responses to each question were scored as complete, partial, or no agreement. Rules for degree of agreement were based upon previously utilized parameters[9] as well as biweekly meetings during which common themes and disagreements in ratings were discussed, and rules generated to create consensus (see Supporting Information, Appendix, in the online version of this article).
Adverse Event Detection
Of the patients meeting eligibility criteria, 200 patients were randomly selected using computer‐generated randomization from each time period for AE outcome assessment, for a total of 400 patients.
Each patient's electronic medical record was retrospectively reviewed by a trained clinician using a previously validated screening tool to detect any possible AEs.[11] Any positive screen prompted documentation of a narrative summary including a short description of the possible AE and pertinent associated data. We defined AE as any injury due to medical management rather than the natural history of the illness, and further limited this definition to only include AEs that occurred on the study unit or as a result of care on that unit.
Two of 4 trained adjudicators, blinded to time period, then separately reviewed each narrative summary using previously validated 6‐point confidence scales to determine the presence and preventability of AE, with confidence ratings of 4 or greater used as cutoffs.[11] All AEs were also scored on a 4‐point severity scale (trivial, clinically significant, serious, or life threatening), with severe AE defined as serious or life threatening. Lastly, adjudicators grouped AEs into 1 of 10 prespecified categories.[11] Any disagreements in ratings or groupings were discussed by all 4 adjudicators to reach consensus.
Data Analysis
Patient characteristics are presented using descriptive statistics and were compared in the pre‐ and postregionalization time periods using 2 or t tests as appropriate.
To analyze whether regionalized care was associated with concordance of plan, adjudicated survey questions were assigned points of 1, 0.5, and 0 for complete, partial, and no agreement, respectively. Total mean concordance scores for any patient ranged from 0 to 7 points, and were divided by total number of answered questions (up to 7) for a range of 0 to 1. Total mean concordance scores as well as mean concordance score per survey question were compared pre‐ versus postregionalization using t tests. In sensitivity analyses, adjudicated survey responses were dichotomized with complete and partial agreement deemed concordant responses. Percent concordance for each question was then compared pre‐ versus postregionalization using 2 analysis. Questions about the name of the other team member and discussion of daily care plan with the other team member were excluded from total concordance score calculations and were compared individually pre‐ versus postregionalization, because they are not directly about the plan of care.
To analyze the association of regionalization with odds of preventable AE, we performed multivariable logistic regression adjusted for patient age, sex, race, language, and Elixhauser comorbidity score,[16] and utilized generalized estimating equations to account for clustering by hospital unit. Secondary outcomes included severe preventable AEs, nonpreventable AEs, and category of preventable AEs using similar methodology. Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (SAS Institute Inc., Cary, NC) was used for all analyses.
RESULTS
The fidelity of the intervention in achieving its goal of regionalized care is discussed separately.[15] Briefly, the intervention was successful at achieving 85% regionalization by team (ie, average daily percentage of team's patients assigned to team's unit) and 87% regionalization by unit (ie, average daily percentage of unit's patients with assigned team) following implementation, compared to 20% regionalization by team and unit in the preintervention period. Importantly, the average daily census of physician care teams rose by 32%, from a mean of 10.8 patients/physician care team preregionalization to a mean of 14.3 patients/physician care team postregionalization.
Concordance of Plan
Of the 418 nurse and intern paired surveys, 4 surveys were excluded due to repeat surveys of the same patient during the same hospitalization, for a total of 197 distinct paired surveys preregionalization and 217 paired surveys postregionalization. There were no statistically significant differences in patients' age, sex, race, language, admission source, length of stay, Elixhauser comorbidity score and diagnosis‐related group weight pre‐ versus postregionalization (Table 1).
| Characteristic | Concordance of Care Plan | Adverse Events | ||||
|---|---|---|---|---|---|---|
| Pre, n = 197 | Post, n = 217 | P Value | Pre, n = 198 | Post, n = 194 | P Value | |
| ||||||
| Age, mean (SD) | 60.5 (19.4) | 57.6 (20.8) | 0.15 | 60.4 (18.9) | 58.0 (21.2) | 0.24 |
| Male, n (%) | 77 (39.1) | 92 (42.4) | 0.49 | 94 (47.5) | 85 (43.8) | 0.55 |
| Race/ethnicity, n (%) | 0.34 | 0.12 | ||||
| White | 134 (68.0) | 141 (65.0) | 132 (66.5) | 121 (62.4) | ||
| Black | 42 (21.3) | 45 (20.7) | 41 (20.8) | 54 (27.8) | ||
| Hispanic | 18 (9.1) | 21 (9.7) | 22 (11.3) | 13 (6.8) | ||
| Other/unknown | 3 (1.5) | 10 (4.6) | 3 (1.4) | 6 (2.9) | ||
| Language, n (%) | 0.30 | 0.73 | ||||
| English | 183 (92.9) | 203 (93.5) | 176 (88.7) | 175 (90.2) | ||
| Spanish | 6 (3.0) | 10 (4.6) | 10 (5.2) | 10 (5.3) | ||
| Other | 8 (4.1) | 4 (1.8) | 12 (6.1) | 9 (4.5) | ||
| Admitting source, n (%) | 1.00 | 0.10 | ||||
| Physician office | 13 (6.6) | 13 (6.0) | 13 (6.6) | 6 (3.1) | ||
| Emergency department | 136 (69.0) | 150 (69.1) | 126 (63.6) | 127 (65.5) | ||
| Transfer from different hospital | 40 (20.3) | 45 (20.7) | 54 (27.3) | 50 (25.8) | ||
| Transfer from skilled nursing facility | 8 (4.1) | 9 (4.2) | 5 (2.5) | 11 (5.6) | ||
| Length of stay, d, median (IQR) | 3.0 (4.0) | 3.0 (4.0) | 0.57 | 4.0 (5.0) | 3.0 (4.0) | 0.16 |
| Elixhauser Comorbidity Score, mean (SD) | 8.0 (8.8) | 8.3 (9.3) | 0.74 | 8.0 (8.6) | 7.8 (8.4) | 0.86 |
| DRG weight, mean (SD) | 1.6 (1.0) | 1.5 (1.0) | 0.37 | 1.5 (0.93) | 1.5 (1.1) | 0.96 |
Kappa scores for adjudications of concordance surveys (defined as both adjudicators scoring the same level of agreement (ie, both complete or partial agreement versus no agreement) ranged from 0.69 to 0.95, by question. There were no significant differences in total mean concordance scores in the care plan pre‐ versus postregionalization (0.65 vs 0.67, P = 0.26) (Table 2). Similarly, there were no significant differences in mean concordance score for each survey question, except agreement on expected date of discharge (0.56 vs 0.68, P = 0.003), knowledge of the other provider's name, and agreement that discussion of the daily plan had taken place with the other pair member. Similar results were seen when results were dichotomized (ie, partial or complete agreement vs no agreement) (Table 2).
| Concordance Outcome | Pre, n = 197 | Post, n = 217 | P Value |
|---|---|---|---|
| |||
| Concordance score* | |||
| Total concordance score, mean (SD) | 0.65 (0.17) | 0.67 (0.16) | 0.26 |
| Subgroups | |||
| Diagnosis | 0.77 (0.32) | 0.72 (0.35) | 0.11 |
| Patient's chief concern | 0.48 (0.44) | 0.48 (0.43) | 0.94 |
| Tests today | 0.67 (0.40) | 0.71 (0.42) | 0.36 |
| Procedures today | 0.93 (0.25) | 0.92 (0.25) | 0.71 |
| Medication changes today | 0.56 (0.44) | 0.59 (0.43) | 0.54 |
| Consulting services | 0.59 (0.44) | 0.60 (0.44) | 0.82 |
| Expected discharge date | 0.56 (0.44) | 0.68 (0.38) | 0.003 |
| Responding clinician knowledge of nurse's name | 0.56 (0.50) | 0.86 (0.35) | <0.001 |
| Nurse's knowledge of responding clinician's name | 0.56 (0.50) | 0.88 (0.33) | <0.001 |
| Plan discussed | 0.73 (0.45) | 0.88 (0.32) | <0.001 |
| Percent concordance, mean (SD) | |||
| Diagnosis | 92.0 (27.3) | 88.6 (31.9) | 0.25 |
| Patient's chief concern | 59.6 (49.1) | 60.6 (49.0) | 0.84 |
| Tests today | 78.9 (40.9) | 77.2 (42.1) | 0.67 |
| Procedures today | 93.5 (24.8) | 94.1 (23.7) | 0.80 |
| Medication changes today | 66.3 (33.6) | 69.9 (46.0) | 0.44 |
| Consulting services | 69.3 (46.2) | 68.9 (46.4) | 0.93 |
| Expected discharge date | 67.5 (47.0) | 82.6 (38.0) | <0.001 |
| Responding clinician knowledge of nurse's name | 55.7 (49.8) | 85.6 (35.2) | <0.001 |
| Nurse's knowledge of responding clinician's name | 55.9 (49.8) | 87.9 (32.8) | <0.001 |
| Plan discussed | 72.9 (44.6) | 88.2 (32.3) | <0.001 |
Adverse Events
Of the 400 patients screened for AEs, 8 were excluded due to missing medical record number (5) and discharge outside of study period (3). Of the final 392 patient screens (198 pre, 194 post), there were no significant differences in patients' age, sex, race, language, length of stay, or Elixhauser score pre‐ versus postregionalization (Table 1).
Kappa scores for adjudicator agreement were 0.35 for presence of AE and 0.34 for preventability of AE. Of the 392 reviewed patient records, there were 133 total AEs detected (66 pre, 67 post), 27 preventable AEs (13 pre, 14 post), and 9 severe preventable AEs (4 pre, 5 post) (Table 3). There was no significant difference in the adjusted odds of preventable AEs post‐ versus preregionalization (adjusted odds ratio: 1.37, 95% confidence interval: 0.69, 2.69). Although the low number of AEs rated as severe or life threatening precluded adjusted analysis, unadjusted results similarly demonstrated no difference in odds of severe preventable AEs pre‐ versus postregionalization. As expected, there was no significant difference in adjusted odds of nonpreventable AE after implementation of regionalized care (Table 3).
| Adverse Events | No. of Adverse Events | Adjusted Odds Ratio Post vs Pre (95% CI) | |
|---|---|---|---|
| Pre, n = 198 | Post, n = 194 | ||
| |||
| Preventable | 13 | 14 | 1.37 (0.69, 2.69) |
| Serious and preventable | 4 | 5 | |
| Nonpreventable | 47 | 50 | 1.20 (0.85, 1.75) |
Similarly, there were no significant differences in category of preventable AE pre‐ versus postregionalization. The most frequent preventable AEs in both time periods were those related to adverse drug events and to manifestations of poor glycemic control, examples of which are illustrated (Table 4).
| |
| Adverse drug event | 29‐year‐old male with history of alcohol abuse, complicated by prior withdrawal seizures/emntensive care unit admissions, presented with alcohol withdrawal. Started on standing and PRN lorazepam, kept on home medications including standing clonidine, gabapentin, citalopram, quetiapine. Became somnolent due to polypharmacy, ultimately discontinued quetiapine as discovered took only as needed at home for insomnia |
| Manifestations of poor glycemic control | 78‐year‐old male with recently diagnosed lymphoma, distant history of bladder and prostate cancer status post ileal loop diversion, presented status post syncopal event; during event, spilled boiling water on himself leading to second‐degree burns on 3% of his body. Initially admitted to trauma/burn service, ultimately transferred to medical service for ongoing multiple medical issues including obstructive uropathy, acute on chronic renal failure. Adverse event was hyperglycemia (>350 mg/dL on >2 consecutive readings) in the setting of holding his home insulin detemir and insulin aspart (had been placed on insulin aspart sliding scale alone). After hyperglycemic episodes, was placed back on weight‐based basal/nutritional insulin |
DISCUSSION
In this study of general medicine patients at a large academic medical center, we found that regionalization of care teams on general medicine services was associated with improved recognition of care team members and agreement on estimated date of patient discharge, but was not associated with improvement in overall nurse and physician concordance of the patient care plan, or the odds of preventable AEs.
This intervention importantly addresses the barrier of dispersion of team membership, a well‐recognized barrier to interdisciplinary collaboration,[17, 18] particularly with resident physician teams due to frequently changing team membership. Localization of all team members, in addition to encouragement of daily collaborative bedside rounds as part of the regionalization initiative, likely contributed to our observed improvement in team member identification and discussion of daily care plans. Similarly, regionalization resulted in improved agreement in estimations of date of patient discharge. Focus on early patient discharges was an integral part of the implementation efforts; we therefore hypothesize that mutual focus on discharge planning by both nurses and responding clinicians may have explained this observed result.
On the other hand, regionalization did not appreciably improve the overall concordance of care plan between nurses and interns, despite a significant increase in team members agreeing that the plan had been discussed. Our findings support similar prior research demonstrating that regionalizing hospitalist attendings to single nursing units had limited impact on agreement of care plan between physicians and nurses.[13] Similarly, in settings where physicians and nurses are inherently regionalized, such as the intensive care unit[4] or the operating room,[3] communication between physicians and nurses remains difficult. Collectively, our findings suggest that colocalization of physicians and nurses alone is likely insufficient to improve measured communication between care team members. Existing literature suggests that more standardized approaches to improve communication, such as structured communication tools used during daily inpatient care[19, 20] or formalized team training,[21, 22, 23] lead to improvements in communication and collaboration. Despite these findings, it is important to highlight that this study did not assess other measures of workplace culture, such as teamwork and care team cohesiveness, which may have been positively affected by this intervention, even without measurable effect on concordance of care plan. Additionally, as noted, the average daily census on each team increased by almost a third postintervention, which may have impeded improvements in care team communication.
In addition, we found that our intervention had no significant impact on preventable AEs or severe preventable AEs. Although we cannot exclude the possibility that more subtle AEs were missed with our methodology, our results indicate that regionalized care alone may be inadequate to improve major patient safety outcomes. As discussed, the volume of patients did increase postintervention; thus, another way to state our results is that we were able to increase the daily volume of patients without any significant decreases in patient safety. Nevertheless, the results on patient safety were less than desired. A recent review of interdisciplinary team care interventions on general medical wards similarly demonstrated underwhelming improvements in patient safety outcomes, although the reviewed interventions did not specifically address preventable AEs, a gap in the literature commented on by the authors.[24] Other albeit limited literature has demonstrated improvement in patient safety outcomes via multifaceted efforts aimed at improving care team member communication. Notably, these efforts include colocalization of care team members to single units but also involve additional measures to improve communication and collaboration between care team members, such as structured communication during interdisciplinary rounds, and certification of key interdisciplinary teamwork skills.[11, 14] Although our regionalized care intervention included many similar features to these accountable care units (ACUs) including unit‐based care teams, unit‐level performance reporting, and unit‐based physician and nursing coleadership, significant differences existed. Notably, in addition to the above features, the ACU model also incorporated highly structured communication models for interdisciplinary rounding, and certification processes to ensure an appropriate communication skill base among care team members.[14] Thus, although creation of regionalized care teams is likely a necessary precursor to implementation of these additional measures, alone it may be insufficient to improve patient safety outcomes.
Importantly, in our study we identified that adverse drug events and manifestations of poor glycemic control occurred in high frequency both before and following implementation of regionalized care, supporting other literature that describes the prevalence of these AEs.[11, 25, 26, 27] These results suggest that targeted interventions to address these specific AEs are likely necessary. Notably, the intervention units in our study did not consistently employ clinical pharmacists assigned specifically to that unit's care team to allow for integration within the care team. As prior research has suggested that greater collaboration with clinical pharmacists results in reduction of adverse drug events,[28] next steps may include improved integration of team‐based pharmacists into the activities of the regionalized care teams. Inpatient management of diabetes also requires specific interventions,[29, 30, 31] only some of which may be addressable by having regionalized care and better interdisciplinary communication.
Our findings are subject to several limitations. First, this was a single‐site study and thus our findings may not be generalizable to other institutions. However, regionalized care is increasingly encouraged to optimize communication between care team members.[17, 18] Therefore, our null findings may be pertinent to other institutions looking to improve patient safety outcomes, demonstrating that additional initiatives will likely be required. Second, our modes of outcome measurement possess limitations. In measuring concordance of care plan, although previously used survey techniques were employed,[9] the concordance survey has not been formally validated, and we believe some of the questions may have led to ambiguity on the part of the responders that may have resulted in less accurate responses, thus biasing toward the null. Similarly, in measuring AEs, the screening tool relied on retrospective chart review looking for specific AE types[11] and thus may not have captured more subtle AEs. Additionally, our study may have been underpowered to demonstrate significant reduction in preventable AEs, although other studies of similar methodology demonstrated significant results with similar sample size.[11] This was due in part to our lower‐than‐expected baseline AE rate (6.6% compared with approximately 10.3% in previous studies).[11] Lastly, our study solely examined the association of regionalization with concordance of care plan and preventable AEs, but importantly excluded other clinically important outcomes that may have been positively (or negatively) impacted by these regionalization efforts, such as ED wait times, provider efficiency (eg, fewer pages, less time in transit, more time at the bedside), interdisciplinary teamwork, or patient or provider satisfaction.
CONCLUSION
In summary, our findings suggest that regionalized care teams alone may be insufficient to effectively promote communication between care team members regarding the care plan or to lead to improvements in patient safety, although we recognize that there may have been benefits (or unintended harms) not measured in this study but are nonetheless important for clinical care and workplace culture. This is an important lesson, as many hospitals move toward regionalized care in an effort to improve patient safety outcomes. However, strengthening the infrastructure by colocalizing care team members to maximize opportunity for communication is likely a necessary first step toward facilitating implementation of additional initiatives that may lead to more robust patient safety improvements, such as structured interdisciplinary bedside rounds (eg, facilitating and training all team members to fulfill specific roles), teamwork training, and certification of key interdisciplinary teamwork skills. Additionally, close examination of identified prevalent and preventable AEs can help to determine which additional initiatives are most likely to have greatest impact in improving patient safety.
Disclosures: This research was supported by funds provided by Brigham and Women's Hospital (BWH) and by funds provided by the Department of Medicine at BWH. All authors had full access to all of the data in the study and were integrally involved in the design, implementation, data collection, and analyses. The first author, Dr. Stephanie Mueller, takes responsibility for the integrity for the data and the accuracy of the data analysis. Dr. Schnipper reports grants from Sanofi Aventis, outside the submitted work.
- Joint Commission on Accreditation of Healthcare Organizations. Understanding and Preventing Sentinel Events in Your Health Care Organization. Oak Brook, IL: Joint Commission; 2008.
- , , , et al. Communication failures in the operating room: an observational classification of recurrent types and effects. Qual Saf Health Care. 2004;13(5):330–334.
- , , , et al. Operating room teamwork among physicians and nurses: teamwork in the eye of the beholder. J Am Coll Surg. 2006;202(5):746–752.
- , , . Discrepant attitudes about teamwork among critical care nurses and physicians. Crit Care Med. 2003;31(3):956–959.
- , , , , . Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
- , , , et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
- , , , et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28–E33.
- , , , , , . Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841.
- , , , et al. Patterns of nurse‐physician communication and agreement on the plan of care. Qual Saf Health Care. 2010;19(3):195–199.
- , , , , , . Can we talk? Priorities for patient care differed among health care providers. In: Henriksen K, Battles JB, Marks ES, Lewin DI, eds. Advances in Patient Safety: From Research to Implementation. Vol 1. Rockville, MD: Agency for Healthcare Research and Quality; 2005.
- , , , et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684.
- , , , , , . Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6(2):88–93.
- , , , et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227.
- , , , et al. Reorganizing a hospital ward as an accountable care unit. J Hosp Med. 2015;10(1):36–40.
- , , , et al. 5th time's a charm: creation of unit‐based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10 (suppl. 2). Available at: http://www.shmabstracts.com/abstract/5th‐times‐a‐charm‐creation‐of‐unit‐based‐care‐teams‐in‐a‐high‐occupancy‐hospital. Accessed July 28, 2015.
- , , , . Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
- , , , , , . Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19(2):117–121.
- , , , et al. A model for quality improvement programs in academic departments of medicine. Am J Med. 2008;121(10):922–929.
- , , , , . Improving nurse‐physician communication and satisfaction in the intensive care unit with a daily goals worksheet. Am J Crit Care. 2006;15(2):217–222.
- , , , , , . Improving communication in the ICU using daily goals. J Crit Care. 2003;18(2):71–75.
- , , , et al. Effect of crew resource management training in a multidisciplinary obstetrical setting. Int J Qual Health Care. 2008;20(4):254–263.
- , , , et al. Error reduction and performance improvement in the emergency department through formal teamwork training: evaluation results of the MedTeams project. Health Serv Res. 2002;37(6):1553–1581.
- , , , et al. Effects of teamwork training on adverse outcomes and process of care in labor and delivery: a randomized controlled trial. Obstet Gynecol. 2007;109(1):48–55.
- , , , et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288–1298.
- , , , et al. Patient risk factors for adverse drug events in hospitalized patients. ADE Prevention Study Group. Arch Intern Med. 1999;159(21):2553–2560.
- , , , et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10.
- , , , . Use of a standardized protocol to decrease medication errors and adverse events related to sliding scale insulin. Qual Saf Health Care. 2006;15(2):89–91.
- , , , . Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955–964.
- , , , , . Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: effect of structured subcutaneous insulin orders and an insulin management algorithm. J Hosp Med. 2009;4(1):3–15.
- , , , . Effects of a computerized order set on the inpatient management of hyperglycemia: a cluster‐randomized controlled trial. Endocr Pract. 2010;16(2):209–218.
- , , , . Effects of a subcutaneous insulin protocol, clinical education, and computerized order set on the quality of inpatient management of hyperglycemia: results of a clinical trial. J Hosp Med. 2009;4(1):16–27.
- Joint Commission on Accreditation of Healthcare Organizations. Understanding and Preventing Sentinel Events in Your Health Care Organization. Oak Brook, IL: Joint Commission; 2008.
- , , , et al. Communication failures in the operating room: an observational classification of recurrent types and effects. Qual Saf Health Care. 2004;13(5):330–334.
- , , , et al. Operating room teamwork among physicians and nurses: teamwork in the eye of the beholder. J Am Coll Surg. 2006;202(5):746–752.
- , , . Discrepant attitudes about teamwork among critical care nurses and physicians. Crit Care Med. 2003;31(3):956–959.
- , , , , . Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
- , , , et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
- , , , et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28–E33.
- , , , , , . Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841.
- , , , et al. Patterns of nurse‐physician communication and agreement on the plan of care. Qual Saf Health Care. 2010;19(3):195–199.
- , , , , , . Can we talk? Priorities for patient care differed among health care providers. In: Henriksen K, Battles JB, Marks ES, Lewin DI, eds. Advances in Patient Safety: From Research to Implementation. Vol 1. Rockville, MD: Agency for Healthcare Research and Quality; 2005.
- , , , et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684.
- , , , , , . Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6(2):88–93.
- , , , et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227.
- , , , et al. Reorganizing a hospital ward as an accountable care unit. J Hosp Med. 2015;10(1):36–40.
- , , , et al. 5th time's a charm: creation of unit‐based care teams in a high occupancy hospital [abstract]. J Hosp Med. 2015;10 (suppl. 2). Available at: http://www.shmabstracts.com/abstract/5th‐times‐a‐charm‐creation‐of‐unit‐based‐care‐teams‐in‐a‐high‐occupancy‐hospital. Accessed July 28, 2015.
- , , , . Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
- , , , , , . Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19(2):117–121.
- , , , et al. A model for quality improvement programs in academic departments of medicine. Am J Med. 2008;121(10):922–929.
- , , , , . Improving nurse‐physician communication and satisfaction in the intensive care unit with a daily goals worksheet. Am J Crit Care. 2006;15(2):217–222.
- , , , , , . Improving communication in the ICU using daily goals. J Crit Care. 2003;18(2):71–75.
- , , , et al. Effect of crew resource management training in a multidisciplinary obstetrical setting. Int J Qual Health Care. 2008;20(4):254–263.
- , , , et al. Error reduction and performance improvement in the emergency department through formal teamwork training: evaluation results of the MedTeams project. Health Serv Res. 2002;37(6):1553–1581.
- , , , et al. Effects of teamwork training on adverse outcomes and process of care in labor and delivery: a randomized controlled trial. Obstet Gynecol. 2007;109(1):48–55.
- , , , et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288–1298.
- , , , et al. Patient risk factors for adverse drug events in hospitalized patients. ADE Prevention Study Group. Arch Intern Med. 1999;159(21):2553–2560.
- , , , et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10.
- , , , . Use of a standardized protocol to decrease medication errors and adverse events related to sliding scale insulin. Qual Saf Health Care. 2006;15(2):89–91.
- , , , . Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955–964.
- , , , , . Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: effect of structured subcutaneous insulin orders and an insulin management algorithm. J Hosp Med. 2009;4(1):3–15.
- , , , . Effects of a computerized order set on the inpatient management of hyperglycemia: a cluster‐randomized controlled trial. Endocr Pract. 2010;16(2):209–218.
- , , , . Effects of a subcutaneous insulin protocol, clinical education, and computerized order set on the quality of inpatient management of hyperglycemia: results of a clinical trial. J Hosp Med. 2009;4(1):16–27.
