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The Effects of a Multifaceted Intervention to Improve Care Transitions Within an Accountable Care Organization: Results of a Stepped-Wedge Cluster-Randomized Trial
The Effects of a Multifaceted Intervention to Improve Care Transitions Within an Accountable Care Organization: Results of a Stepped-Wedge Cluster-Randomized Trial
This work is licensed under a Creative Commons Attribution 4.0 International License
Transitions from the hospital to the ambulatory setting are high-risk periods for patients in terms of adverse events, poor clinical outcomes, and readmission. Processes of care during care transitions are suboptimal, including poor communication among inpatient providers, patients, and ambulatory providers1,2; suboptimal communication of postdischarge plans of care to patients and their ability to carry out these plans3; medication discrepancies and nonadherence after discharge4; and lack of timely follow-up with ambulatory providers.5 Healthcare organizations continue to struggle with the question of which interventions to implement and how best to implement them.
Interventions to improve care transitions typically focus on readmission rates, but some studies have focused on postdischarge adverse events, defined as injuries in the 30 days after discharge caused by medical management rather than underlying disease processes.2 These adverse events cause psychological distress, out-of-pocket expenses, decreases in functional status, and caregiver burden. An estimated 20% of hospitalized patients suffer a postdischarge adverse event.1,2 Approximately two-thirds of these may be preventable or ameliorable.
The advent of Accountable Care Organizations (ACOs), defined as “groups of doctors, hospitals, and other health care providers who come together voluntarily to give coordinated high quality care to their patients,” creates an opportunity for improvements in patient safety during care transitions.6 Another opportunity has been the advent of Patient-Centered Medical Homes (PCMH), consisting of patient-oriented, comprehensive, team-based primary care enhanced by health information technology and population-based disease management tools.7,8 In theory, a hospital-PCMH collaboration within an ACO can improve transitional interventions since optimal communication and collaboration are more likely when both inpatient and primary care providers (PCPs) share infrastructure and are similarly incentivized. The objectives of this study were to design and implement a collaborative hospital-PCMH care transitions intervention within an ACO and evaluate its effects.
METHODS
This study was a two-arm, single-blind (blinded outcomes assessor), stepped-wedge, multisite cluster-randomized clinical trial (NCT02130570) approved by the institutional review board of Partners HealthCare.
Study Design and Randomization
The study employed a “stepped-wedge” design, which is a cluster-randomized study design in which an intervention is sequentially rolled out to different groups at different, prespecified, randomly determined times.9 Each cluster (in this case, each primary care practice) served as its own control, while still allowing for adjustment for temporal trends. Originally, 18 practices participated, but one withdrew due to the low number of patients enrolled in the study, leaving 17 clusters and 16 sequences; see Figure 1 of Appendix 1 for a full description of the sample size and timeline for each cluster. Practices were not aware of this timeline until after recruitment.
Study Setting and Participants
Conducted within a large Pioneer ACO in Boston and funded by the Patient-Centered Outcomes Research Institute (PCORI), the Partners-PCORI Transitions Study was designed as a “real-world” quality improvement project. Potential participants were adult patients who were admitted to medical and surgical services of two large academic hospitals (Hospital A and Hospital B) affiliated with an ACO, who were likely to be discharged back to the community, and whose PCP belonged to a primary care practice that was affiliated with the ACO, agreed to participate, and were designated PCMHs or on their way to being designated by meeting certain criteria: electronic health record, patient portal, team-based care, practice redesign, care management, and identification of high-risk patients. See Study Protocol (Appendix 2) for detailed patient and primary care practice inclusion criteria.
Patient Enrollment
Study staff screened participants from a daily automated list of patients admitted the day before, using medical records to determine eligibility, which was then confirmed by the patient’s nurse. Exclusion criteria included likely discharge to a location other than home, being in police custody, lack of a telephone, being homeless, previous enrollment in the study, and being unable to communicate in English or Spanish. Allocation to study arm was concealed until the patient or proxy provided informed written consent. The research assistant administered questionnaires to all study subjects to assess potential confounders and functional status 1 month prior to admission (
Intervention
The intervention was based on a conceptual model of an ideal discharge11 that we developed based on work by Naylor et al,12 work by Coleman and Berenson,3 best practices in medication reconciliation and information transfer according to our own research,13-15 the best examples of interventions to improve the discharge process,12,16,17 and a systematic review of discharge interventions.18 Some of the factors necessary for an ideal care transition include complete, organized, and timely documentation of the patient’s hospital course and postdischarge plan; effective discharge planning; coordination of care among the patient’s providers; methods to ensure medication safety; advanced care planning in appropriate patients; and education and “coaching” of patients and their caregivers so they learn how to manage their conditions. The final multifaceted intervention addressed each component of the ideal discharge and included inpatient and outpatient components (Table 1 and Table 1 of Appendix 1).
Patient and Public Involvement in Research
As with all PCORI-funded studies, this study involved a patient-family advisory council (PFAC). Our PFAC included six recently hospitalized patients or caregivers of recently hospitalized patients. The PFAC participated in monthly meetings throughout the study period. They helped inform the research questions, including confirmation that the endpoints were patient centered, and provided valuable input for the design of the intervention and the patient-facing components of the data collection instruments. They also interviewed several patient participants in the study regarding their experiences with the intervention. Lastly, they helped develop plans for dissemination of study results to the public.19
We also formed a steering committee consisting of physician, nursing, pharmacy, information technology, and administrative leadership representing primary care, inpatient care, and transitional care at both hospitals and Partners Healthcare. PFAC members took turns participating in quarterly steering committee meetings.
Evolution of the Intervention and Implementation
The intervention was iteratively refined during the course of the study in response to input from the PFAC, steering committee, and members of the intervention team; cases of adverse events and readmissions from patients despite being in the intervention arm; exit interviews of patients who had recently completed the intervention; and informal feedback from inpatient and outpatient clinicians. For example, we learned that the more complicated a patient’s conditions are, the sooner the clinical team wanted them to be seen after discharge. However, these patients were also less likely to feel well enough to keep that appointment. Therefore, the timing of follow-up of appointments needed to be a negotiation among the inpatient team, the patient, any caregivers, and the outpatient provider. PFAC members also emphasized that patients wanted one person to trust and to be the “point person” during a complicated transition such as hospital discharge.
At the same time, the intervention components evolved because of factors outside our control (eg, resource limitations). In keeping with the real-world nature of the research, the aim was for the intervention to be internally supported because incentives were theoretically more aligned with improvement of care transitions under the ACO model. By design, the PCORI contract only paid for limited parts of the intervention, such as a nurse practitioner to act as the discharge advocate at one hospital, overtime costs of inpatient pharmacists, and project manager time to facilitate inpatient-outpatient provider communication. (See Table 1 of Appendix 1 for details about the modifications to the intervention.)
Lastly, in keeping with PCORI’s methodology standards for studies of complex interventions,20 we strove to standardize the intervention by function across hospitals, units, and practices, while still allowing for local adaptation in the form. In other words, rather than specifying exactly how a task (eg, medication counseling) needed to be performed, the study design offered sites flexibility in how they implemented the task given their available personnel and institutional culture.
Intervention Fidelity
To determine the extent to which each patient in the intervention arm received each intervention component, a project manager unblinded to treatment arm reviewed the electronic medical record for documentation of each component implemented by providers (eg, inpatient pharmacists, outpatient nurses). Because each intervention component produced documentation, this provided an accurate assessment of intervention fidelity, ie, the extent to which the intervention was implemented as intended.
Outcome Assessment
Postdischarge Follow-up
Based on previous studies,2,21 a trained research assistant attempted to contact all study subjects 30 days (±5 days) after discharge and administered a questionnaire to identify any new or worsening symptoms since discharge, any healthcare use since discharge, and functional status in the previous week. Follow-up questions used branching logic to determine the relationship of any new or worsening symptoms to medications or other aspects of medical management. Research assistants followed up any positive responses with directed medical record review for objective findings, diagnoses, treatments, and responses. If patients could not be reached after five attempts, the research assistant instead conducted a thorough review of the outpatient medical record alone for provider reports of any new or worsening symptoms noted during follow-up within the 30-day postdischarge period. Research assistants also reviewed laboratory test results in all patients for evidence of postdischarge renal failure, elevated liver function tests, or new/worsening anemia.
Hospital Readmissions
We measured nonelective hospital readmissions within 30 days of discharge using a combination of administrative data for hospitalizations within the ACO network plus patient report during the 30-day phone call for all other readmissions.22
Adjudication of Outcomes
Adverse events and preventable adverse events: All cases of new or worsening symptoms or signs, along with all supporting documentation, were then presented to teams of two trained blinded physician adjudicators through application of methods established in previous studies.4,21 Each of the two adjudicators independently reviewed the information, along with the medical record, and completed a standardized form to confirm or deny the presence of any adverse events (ie, patient injury due to medical management) and to classify the type of event (eg, adverse drug event, hospital-acquired infection, procedural complication, diagnostic or management error), the severity and duration of the event, and whether the event was preventable or ameliorable. The two adjudicators then met to resolve any differences in their findings and come to consensus.
Preventable readmissions: If patients were readmitted to either study hospital, we conducted an evaluation, based on previous studies,23 to determine if and how the readmission could have been prevented including (a) a standardized patient and caregiver interview to identify possible problems with the transitions process and (b) an email questionnaire to the patient’s PCP and the inpatient teams who cared for the patient during the index admission and readmission regarding possible deficiencies with the transitions process. As with adverse event adjudications, two physician adjudicators worked independently to classify the preventability of the readmission and then met to come to consensus. Conflicts were resolved by a third adjudicator.
Analysis Plan
To evaluate the effects of the intervention on the primary outcome, the number of postdischarge adverse events per patient, we used multivariable Poisson regression, with study arm as the main predictor. A similar approach was used to evaluate the number of new or worsening postdischarge signs or symptoms and the number of preventable adverse events per patient. We used an intention-to-treat analysis: If a practice did not start the intervention when they were scheduled to, based on our randomization, we counted all patients in that practice admitted after that point as intervention patients. We adjusted for patient demographics, clinical characteristics, month, inpatient unit, and primary care practice as fixed effects. We clustered by PCP using general linear models. Intervention effects were expressed as both unadjusted and adjusted incidence rate ratios (IRRs). We also conducted a limited number of subgroup analyses, determined a priori, to determine whether the intervention was more effective in certain patient populations; we used interaction terms (intervention × subgroup) to determine the statistical significance of any effect modification.
To evaluate the effects of the intervention on nonelective readmissions and preventable readmissions, we used a similar approach, using multivariable logistic regression. Postdischarge functional status, adjusted for status prior to admission, was analyzed using multivariable linear regression and random effects by primary care practice. The general linear mixed model (GLIMMIX) procedure in the SAS 9.3 statistical package (SAS Institute) was used to carry out all analyses.
Power and Sample Size
We assumed a baseline rate of postdischarge adverse events of 0.30 per patient.21 We conservatively assumed an effect size of a change from 0.30 in the control group to 0.23 in the intervention group (a relative reduction of 22%, which was based on studies of preventability rates23 and close to the minimum clinically important difference). Based on prior studies,4,22 we assumed an intraclass correlation coefficient of 0.01 with an average cluster size of seven patients per PCP. Assuming a 10% loss to follow-up rate and an alpha of 0.05, we targeted a sample size of 1,800 patients to achieve 80% power, with one-third of the patients in the usual care arm and two-thirds in the intervention arm.
RESULTS
We enrolled 18 PCMH primary care practices to participate in the study, including 8 from Hospital A (out of 13 approached), 8 from Hospital B (out of 11), and 2 from other ACO practices (out of 9) (plus two pilot practices). Reasons for not participating included not having dedicated personnel to play the role of the responsible outpatient clinician, undergoing recent turn-over in practice leadership, and not having enough patients admitted to the two hospitals. One practice only enrolled 5 patients in the study and withdrew from participation, which left 17 practices.
Study Patients
We enrolled 1,679 patients (Figure 1). Reasons for nonenrollment included being unable to complete the screen prior to discharge, not meeting inclusion criteria or meeting exclusion criteria, being assigned to a pilot practice, and declining informed written consent. The baseline characteristics of enrolled patients are presented in Table 2. Differences between the two study arms were small. About 47% of the cohort was not reachable by phone after five attempts for the 30-day phone call, but only 69 (4.1%) were truly lost to follow-up because they were unreachable by phone and had no documentation in the electronic medical record in the 30-days after discharge.
Intervention Fidelity
The majority of patients did not receive most intervention components, even those components that were supposed to be delivered to all intervention patients (Table 3). A minority of patients were referred to visiting nurse services and to the home pharmacy program. However, 855 patients (87%) in the intervention arm received at least one intervention component.
Outcome Measures
The intervention was associated with a statistically significant reduction in several of the outcomes of interest, including the primary outcome, number of postdischarge adverse events (45% reduction), and new or worsening postdischarge signs or symptoms (22% reduction), as well as preventable postdischarge adverse events (58% reduction) (Table 4). There was a nonsignificant difference in functional status. There was no significant effect on total nonelective or on preventable readmission rates. When analyzed by type of adverse event, the intervention was associated with a reduction in adverse drug events and in procedural complications (Table 2 of Appendix 1). Of note, there was no significant difference in the proportion of patients with at least one adverse event whether the outcome was determined by phone call and medical record review (49%) or medical record review alone (51%) (P = .48).
In subgroup analyses, there was no evidence of effect modification by service, hospital, patient age, readmission risk, health literacy, or comorbidity score (Table 3 of Appendix 1). Table 4 of Appendix 1 provides examples of postdischarge adverse events seen in the usual care arm that might have been prevented in the intervention.
DISCUSSION
This intervention was associated with a reduction in postdischarge adverse events. The relative improvement in each outcome aligned with the hypothesized sensitivity to change: the smallest improvement was seen in new or worsening signs or symptoms, followed by postdischarge adverse events and then by preventable postdischarge adverse events. The intervention was not associated with a difference in readmissions. The lack of effect on hospital readmissions may have been caused by the low proportion of readmissions that are preventable, as well as low intervention fidelity and lack of resources to implement facets such as postdischarge coaching, an evidence-based intervention that was never adopted.16,23 One lesson of this study is that it may be easier to reduce postdischarge injury (still an important outcome) than readmissions.
Putting this study in context, we should note that the literature on interventions to improve care transitions is mixed.18 While there are several reports of successful interventions, there are many reports of unsuccessful ones, often using similar components. Success is often the result of adequate resources and attention to myriad details regarding implementation.24 The intervention in our study likely contributed to improvements in patient and caregiver engagement in the hospital, enhancements of communication between inpatient and outpatient clinicians, and implementation of pharmacist-led interventions to improve medication safety. Regarding the latter, several prior studies have shown the benefits of pharmacist interventions in decreasing postdischarge adverse drug events.4,25,26 Therefore, even an intervention with incomplete intervention fidelity can reduce postdischarge adverse events, especially because adverse drug events make up the majority of adverse events.1,2,21
Perhaps the biggest lesson we learned was regarding the limitations of the hospital-led ACO model to incentivize sufficient up-front investments in transitional care interventions. By design, we chose a real-world approach in which interventions were integrated with existing ACO efforts, which were paid for internally by the institution. As a result, many of the interventions had to be scaled back because of resource constraints. The ACO model theoretically incentivizes more integrated care, but this may not always be true in practice. Emerging evidence suggests that physician group–led ACOs are associated with lower costs and use compared with hospital-led ACOs, likely because of more aligned incentives in physician group–led ACOs to reduce use of inpatient care.27,28
An unresolved question is whether the ideal implementation approach is to protect the time of existing clinical personnel to carry out transitional care tasks or to hire external personnel to do these tasks. We purposely spread the intervention over several clinician types to minimize the additional burden on any one of them, minimize additional costs, and play to each clinician’s expertise, but in retrospect, this may not have been the right approach. By providing additional personnel with dedicated time, interest, and training in care transitions, the intervention may be delivered with higher quantitative and qualitative fidelity, and it could create a single point of contact for patients, which was considered highly desirable by our PFAC.
This study has several limitations. A large proportion of patients (44%) were unavailable for postdischarge phone calls. However, we were able to perform medical record review for worsening signs (eg, lab abnormalities) and symptoms (as reported by patients’ providers) in the postdischarge period and adjudicate them for adverse events for all but 69 of these patients. Because all these patients had ACO-affiliated PCPs, we would expect most of their utilization to have been within the system and, therefore, to be present in the medical record. The proportion of patients with at least one adverse event did not vary by the method of follow-up, which suggests that this issue is an unlikely source of bias. Assessment of readmission was imperfect because we do not have statewide or national data. However, our combination of administrative data for Partners readmissions plus self-report for non-Partners readmission has been shown to be fairly complete in previous studies.29 Adjudicators could not be fully blinded to intervention status due to the lack of blinding of admission date. We did not calculate a kappa value for interrater reliability of individual assessments of adverse events; rather, coming to consensus among the two adjudicators was part of the process. In only a handful of cases was a third adjudicator required. Lastly, this study was conducted at two academic medical centers and their affiliated primary care clinics, which potentially limits generalizability; however, the results are likely generalizable to other ACOs that include major academic medical centers.
CONCLUSION
In conclusion, in this real-world clinical trial, we designed, implemented, and iteratively refined a multifaceted intervention to improve care transitions within a hospital-based academic ACO. Evolution of the intervention components was the result of stakeholder input, experience with the intervention, and ACO resource constraints. The intervention reduced postdischarge adverse events. However, across the ACO network, intervention fidelity was low, and this may have contributed to the lack of effect on readmission rates. ACOs that implement interventions without hiring new personnel or protecting the time of existing personnel to conduct transitional tasks are likely to face the same challenges of low fidelity.
Acknowledgments
The authors would like to acknowledge the many people who worked on designing, implementing, and evaluating this intervention, including but not limited to: Natasha Isaac, Hilary Heyison, Jacqueline Minahan, Molly O’Reilly, Michelle Potter, Nailah Khoory, Maureen Fagan, David Bates, Laura Carr, Joseph Frolkis, Eric Weil, Jacqueline Somerville, Stephanie Ahmed, Marcy Bergeron, Jessica Smith, and Jane Millett. We would also like to thank the members of our Patient-Family Advisory Council: Maureen Fagan, Karen Spikes, Margie Hodges, Win Hodges, Aureldon Henderson, Dena Salzberg, and Kay Bander.
1. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345-349.
2. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161-167. https://doi.org/10.7326/0003-4819-138-3-200302040-00007
3. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141(7):533-536. https://doi.org/10.7326/0003-4819-141-7-200410050-00009
4. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166(5):565-571. https://doi.org/10.1001/archinte.166.5.565
5. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. https://doi.org/10.1056/nejmsa0803563
6. Accountable Care Organizations (ACOs). Centers for Medicare & Medicaid Services. 2012. Updated February 11, 2020. Accessed July 15, 2012. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO/index.html?redirect=/ACO/
7. Bates DW, Bitton A. The future of health information technology in the patient-centered medical home. Health Aff (Millwood). 2010;29(4):614-621. https://doi.org/10.1377/hlthaff.2010.0007
8. Bitton A, Martin C, Landon BE. A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med. 2010;25(6):584-592. https://doi.org/10.1007/s11606-010-1262-8
9. Brown C, Lilford R. Evaluating service delivery interventions to enhance patient safety. BMJ. 2008;337:a2764. https://doi.org/10.1136/bmj.a2764
10. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. https://doi.org/10.1097/00005650-199603000-00003
11. Burke RE, Kripalani S, Vasilevskis EE, Schnipper JL. Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102-109. https://doi.org/10.1002/jhm.1990
12. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620. https://doi.org/10.1001/jama.281.7.613
13. Gandara E, Ungar J, Lee J, Chan-Macrae M, O’Malley T, Schnipper JL. Discharge documentation of patients discharged to subacute facilities: a three-year quality improvement process across an integrated health care system. Jt Comm J Qual Patient Saf. 2010;36(6):243-251. https://doi.org/10.1016/s1553-7250(10)36039-9
14. Pippins JR, Gandhi TK, Hamann C, et al. Classifying and predicting errors of inpatient medication reconciliation. J Gen Intern Med. 2008;23(9):1414-1422. https://doi.org/10.1007/s11606-008-0687-9
15. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. https://doi.org/10.1001/archinternmed.2009.51
16. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822-1828. https://doi.org/10.1001/archinte.166.17.1822
17. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178-187. https://doi.org/10.7326/0003-4819-150-3-200902030-00007
18. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. https://doi.org/10.7326/0003-4819-155-8-201110180-00008
19. Schnipper J, Levine C. The important thing to do before leaving the hospital: many patients and families forget, which can lead to complications later. Next Avenue. October 22, 2019. Accessed September 10, 2020. https://www.nextavenue.org/before-leaving-hospital/
20. PCORI Methodology Standards: Standards for Studies of Complex Interventions. Patient-Centered Outcomes Research Institute; November 12, 2015. Updated: February 26, 2019. Accessed June 3, 2019. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Complex
21. Tsilimingras D, Schnipper J, Duke A, et al. Post-discharge adverse events among urban and rural patients of an urban community hospital: a prospective cohort study. J Gen Intern Med. 2015;30(8):1164-1171. https://doi.org/10.1007/s11606-015-3260-3
22. Kripalani S, Roumie CL, Dalal AK, 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. https://doi.org/10.7326/0003-4819-157-1-201207030-00003
23. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176(4):484-493. https://doi.org/10.1001/jamainternmed.2015.7863
24. Vasilevskis EE, Kripalani S, Ong MK, et al. Variability in implementation of interventions aimed at reducing readmissions among patients with heart failure: a survey of teaching hospitals. Acad Med. 2016;91(4):522-529. https://doi.org/10.1097/acm.0000000000000994
25. Gardella JE, Cardwell TB, Nnadi M. Improving medication safety with accurate preadmission medication lists and postdischarge education. Jt Comm J Qual Patient Saf. 2012;38(10):452-458. https://doi.org/10.1016/s1553-7250(12)38060-4
26. Kaboli PJ, Hoth AB, McClimon BJ, Schnipper JL. Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955-964. https://doi.org/10.1001/archinte.166.9.955
27. McWilliams JM, Hatfield LA, Chernew ME, Landon BE, Schwartz AL. Early performance of accountable care organizations in Medicare. N Engl J Med. 2016;374(24):2357-2366. https://doi.org/10.1056/nejmsa1600142
28. McWilliams JM, Hatfield LA, Landon BE, Hamed P, Chernew ME. Medicare spending after 3 years of the Medicare Shared Savings Program. N Engl J Med. 2018;379(12):1139-1149. https://doi.org/10.1056/nejmsa1803388
29. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211-219. https://doi.org/10.1007/s11606-009-1196-1
30. Donzé JD, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632-638. https://doi.org/10.001/jamainternmed.2013.3023
31. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016;176(4):496-502. https://doi.org/10.1001/jamainternmed.2015.8462
Transitions from the hospital to the ambulatory setting are high-risk periods for patients in terms of adverse events, poor clinical outcomes, and readmission. Processes of care during care transitions are suboptimal, including poor communication among inpatient providers, patients, and ambulatory providers1,2; suboptimal communication of postdischarge plans of care to patients and their ability to carry out these plans3; medication discrepancies and nonadherence after discharge4; and lack of timely follow-up with ambulatory providers.5 Healthcare organizations continue to struggle with the question of which interventions to implement and how best to implement them.
Interventions to improve care transitions typically focus on readmission rates, but some studies have focused on postdischarge adverse events, defined as injuries in the 30 days after discharge caused by medical management rather than underlying disease processes.2 These adverse events cause psychological distress, out-of-pocket expenses, decreases in functional status, and caregiver burden. An estimated 20% of hospitalized patients suffer a postdischarge adverse event.1,2 Approximately two-thirds of these may be preventable or ameliorable.
The advent of Accountable Care Organizations (ACOs), defined as “groups of doctors, hospitals, and other health care providers who come together voluntarily to give coordinated high quality care to their patients,” creates an opportunity for improvements in patient safety during care transitions.6 Another opportunity has been the advent of Patient-Centered Medical Homes (PCMH), consisting of patient-oriented, comprehensive, team-based primary care enhanced by health information technology and population-based disease management tools.7,8 In theory, a hospital-PCMH collaboration within an ACO can improve transitional interventions since optimal communication and collaboration are more likely when both inpatient and primary care providers (PCPs) share infrastructure and are similarly incentivized. The objectives of this study were to design and implement a collaborative hospital-PCMH care transitions intervention within an ACO and evaluate its effects.
METHODS
This study was a two-arm, single-blind (blinded outcomes assessor), stepped-wedge, multisite cluster-randomized clinical trial (NCT02130570) approved by the institutional review board of Partners HealthCare.
Study Design and Randomization
The study employed a “stepped-wedge” design, which is a cluster-randomized study design in which an intervention is sequentially rolled out to different groups at different, prespecified, randomly determined times.9 Each cluster (in this case, each primary care practice) served as its own control, while still allowing for adjustment for temporal trends. Originally, 18 practices participated, but one withdrew due to the low number of patients enrolled in the study, leaving 17 clusters and 16 sequences; see Figure 1 of Appendix 1 for a full description of the sample size and timeline for each cluster. Practices were not aware of this timeline until after recruitment.
Study Setting and Participants
Conducted within a large Pioneer ACO in Boston and funded by the Patient-Centered Outcomes Research Institute (PCORI), the Partners-PCORI Transitions Study was designed as a “real-world” quality improvement project. Potential participants were adult patients who were admitted to medical and surgical services of two large academic hospitals (Hospital A and Hospital B) affiliated with an ACO, who were likely to be discharged back to the community, and whose PCP belonged to a primary care practice that was affiliated with the ACO, agreed to participate, and were designated PCMHs or on their way to being designated by meeting certain criteria: electronic health record, patient portal, team-based care, practice redesign, care management, and identification of high-risk patients. See Study Protocol (Appendix 2) for detailed patient and primary care practice inclusion criteria.
Patient Enrollment
Study staff screened participants from a daily automated list of patients admitted the day before, using medical records to determine eligibility, which was then confirmed by the patient’s nurse. Exclusion criteria included likely discharge to a location other than home, being in police custody, lack of a telephone, being homeless, previous enrollment in the study, and being unable to communicate in English or Spanish. Allocation to study arm was concealed until the patient or proxy provided informed written consent. The research assistant administered questionnaires to all study subjects to assess potential confounders and functional status 1 month prior to admission (
Intervention
The intervention was based on a conceptual model of an ideal discharge11 that we developed based on work by Naylor et al,12 work by Coleman and Berenson,3 best practices in medication reconciliation and information transfer according to our own research,13-15 the best examples of interventions to improve the discharge process,12,16,17 and a systematic review of discharge interventions.18 Some of the factors necessary for an ideal care transition include complete, organized, and timely documentation of the patient’s hospital course and postdischarge plan; effective discharge planning; coordination of care among the patient’s providers; methods to ensure medication safety; advanced care planning in appropriate patients; and education and “coaching” of patients and their caregivers so they learn how to manage their conditions. The final multifaceted intervention addressed each component of the ideal discharge and included inpatient and outpatient components (Table 1 and Table 1 of Appendix 1).
Patient and Public Involvement in Research
As with all PCORI-funded studies, this study involved a patient-family advisory council (PFAC). Our PFAC included six recently hospitalized patients or caregivers of recently hospitalized patients. The PFAC participated in monthly meetings throughout the study period. They helped inform the research questions, including confirmation that the endpoints were patient centered, and provided valuable input for the design of the intervention and the patient-facing components of the data collection instruments. They also interviewed several patient participants in the study regarding their experiences with the intervention. Lastly, they helped develop plans for dissemination of study results to the public.19
We also formed a steering committee consisting of physician, nursing, pharmacy, information technology, and administrative leadership representing primary care, inpatient care, and transitional care at both hospitals and Partners Healthcare. PFAC members took turns participating in quarterly steering committee meetings.
Evolution of the Intervention and Implementation
The intervention was iteratively refined during the course of the study in response to input from the PFAC, steering committee, and members of the intervention team; cases of adverse events and readmissions from patients despite being in the intervention arm; exit interviews of patients who had recently completed the intervention; and informal feedback from inpatient and outpatient clinicians. For example, we learned that the more complicated a patient’s conditions are, the sooner the clinical team wanted them to be seen after discharge. However, these patients were also less likely to feel well enough to keep that appointment. Therefore, the timing of follow-up of appointments needed to be a negotiation among the inpatient team, the patient, any caregivers, and the outpatient provider. PFAC members also emphasized that patients wanted one person to trust and to be the “point person” during a complicated transition such as hospital discharge.
At the same time, the intervention components evolved because of factors outside our control (eg, resource limitations). In keeping with the real-world nature of the research, the aim was for the intervention to be internally supported because incentives were theoretically more aligned with improvement of care transitions under the ACO model. By design, the PCORI contract only paid for limited parts of the intervention, such as a nurse practitioner to act as the discharge advocate at one hospital, overtime costs of inpatient pharmacists, and project manager time to facilitate inpatient-outpatient provider communication. (See Table 1 of Appendix 1 for details about the modifications to the intervention.)
Lastly, in keeping with PCORI’s methodology standards for studies of complex interventions,20 we strove to standardize the intervention by function across hospitals, units, and practices, while still allowing for local adaptation in the form. In other words, rather than specifying exactly how a task (eg, medication counseling) needed to be performed, the study design offered sites flexibility in how they implemented the task given their available personnel and institutional culture.
Intervention Fidelity
To determine the extent to which each patient in the intervention arm received each intervention component, a project manager unblinded to treatment arm reviewed the electronic medical record for documentation of each component implemented by providers (eg, inpatient pharmacists, outpatient nurses). Because each intervention component produced documentation, this provided an accurate assessment of intervention fidelity, ie, the extent to which the intervention was implemented as intended.
Outcome Assessment
Postdischarge Follow-up
Based on previous studies,2,21 a trained research assistant attempted to contact all study subjects 30 days (±5 days) after discharge and administered a questionnaire to identify any new or worsening symptoms since discharge, any healthcare use since discharge, and functional status in the previous week. Follow-up questions used branching logic to determine the relationship of any new or worsening symptoms to medications or other aspects of medical management. Research assistants followed up any positive responses with directed medical record review for objective findings, diagnoses, treatments, and responses. If patients could not be reached after five attempts, the research assistant instead conducted a thorough review of the outpatient medical record alone for provider reports of any new or worsening symptoms noted during follow-up within the 30-day postdischarge period. Research assistants also reviewed laboratory test results in all patients for evidence of postdischarge renal failure, elevated liver function tests, or new/worsening anemia.
Hospital Readmissions
We measured nonelective hospital readmissions within 30 days of discharge using a combination of administrative data for hospitalizations within the ACO network plus patient report during the 30-day phone call for all other readmissions.22
Adjudication of Outcomes
Adverse events and preventable adverse events: All cases of new or worsening symptoms or signs, along with all supporting documentation, were then presented to teams of two trained blinded physician adjudicators through application of methods established in previous studies.4,21 Each of the two adjudicators independently reviewed the information, along with the medical record, and completed a standardized form to confirm or deny the presence of any adverse events (ie, patient injury due to medical management) and to classify the type of event (eg, adverse drug event, hospital-acquired infection, procedural complication, diagnostic or management error), the severity and duration of the event, and whether the event was preventable or ameliorable. The two adjudicators then met to resolve any differences in their findings and come to consensus.
Preventable readmissions: If patients were readmitted to either study hospital, we conducted an evaluation, based on previous studies,23 to determine if and how the readmission could have been prevented including (a) a standardized patient and caregiver interview to identify possible problems with the transitions process and (b) an email questionnaire to the patient’s PCP and the inpatient teams who cared for the patient during the index admission and readmission regarding possible deficiencies with the transitions process. As with adverse event adjudications, two physician adjudicators worked independently to classify the preventability of the readmission and then met to come to consensus. Conflicts were resolved by a third adjudicator.
Analysis Plan
To evaluate the effects of the intervention on the primary outcome, the number of postdischarge adverse events per patient, we used multivariable Poisson regression, with study arm as the main predictor. A similar approach was used to evaluate the number of new or worsening postdischarge signs or symptoms and the number of preventable adverse events per patient. We used an intention-to-treat analysis: If a practice did not start the intervention when they were scheduled to, based on our randomization, we counted all patients in that practice admitted after that point as intervention patients. We adjusted for patient demographics, clinical characteristics, month, inpatient unit, and primary care practice as fixed effects. We clustered by PCP using general linear models. Intervention effects were expressed as both unadjusted and adjusted incidence rate ratios (IRRs). We also conducted a limited number of subgroup analyses, determined a priori, to determine whether the intervention was more effective in certain patient populations; we used interaction terms (intervention × subgroup) to determine the statistical significance of any effect modification.
To evaluate the effects of the intervention on nonelective readmissions and preventable readmissions, we used a similar approach, using multivariable logistic regression. Postdischarge functional status, adjusted for status prior to admission, was analyzed using multivariable linear regression and random effects by primary care practice. The general linear mixed model (GLIMMIX) procedure in the SAS 9.3 statistical package (SAS Institute) was used to carry out all analyses.
Power and Sample Size
We assumed a baseline rate of postdischarge adverse events of 0.30 per patient.21 We conservatively assumed an effect size of a change from 0.30 in the control group to 0.23 in the intervention group (a relative reduction of 22%, which was based on studies of preventability rates23 and close to the minimum clinically important difference). Based on prior studies,4,22 we assumed an intraclass correlation coefficient of 0.01 with an average cluster size of seven patients per PCP. Assuming a 10% loss to follow-up rate and an alpha of 0.05, we targeted a sample size of 1,800 patients to achieve 80% power, with one-third of the patients in the usual care arm and two-thirds in the intervention arm.
RESULTS
We enrolled 18 PCMH primary care practices to participate in the study, including 8 from Hospital A (out of 13 approached), 8 from Hospital B (out of 11), and 2 from other ACO practices (out of 9) (plus two pilot practices). Reasons for not participating included not having dedicated personnel to play the role of the responsible outpatient clinician, undergoing recent turn-over in practice leadership, and not having enough patients admitted to the two hospitals. One practice only enrolled 5 patients in the study and withdrew from participation, which left 17 practices.
Study Patients
We enrolled 1,679 patients (Figure 1). Reasons for nonenrollment included being unable to complete the screen prior to discharge, not meeting inclusion criteria or meeting exclusion criteria, being assigned to a pilot practice, and declining informed written consent. The baseline characteristics of enrolled patients are presented in Table 2. Differences between the two study arms were small. About 47% of the cohort was not reachable by phone after five attempts for the 30-day phone call, but only 69 (4.1%) were truly lost to follow-up because they were unreachable by phone and had no documentation in the electronic medical record in the 30-days after discharge.
Intervention Fidelity
The majority of patients did not receive most intervention components, even those components that were supposed to be delivered to all intervention patients (Table 3). A minority of patients were referred to visiting nurse services and to the home pharmacy program. However, 855 patients (87%) in the intervention arm received at least one intervention component.
Outcome Measures
The intervention was associated with a statistically significant reduction in several of the outcomes of interest, including the primary outcome, number of postdischarge adverse events (45% reduction), and new or worsening postdischarge signs or symptoms (22% reduction), as well as preventable postdischarge adverse events (58% reduction) (Table 4). There was a nonsignificant difference in functional status. There was no significant effect on total nonelective or on preventable readmission rates. When analyzed by type of adverse event, the intervention was associated with a reduction in adverse drug events and in procedural complications (Table 2 of Appendix 1). Of note, there was no significant difference in the proportion of patients with at least one adverse event whether the outcome was determined by phone call and medical record review (49%) or medical record review alone (51%) (P = .48).
In subgroup analyses, there was no evidence of effect modification by service, hospital, patient age, readmission risk, health literacy, or comorbidity score (Table 3 of Appendix 1). Table 4 of Appendix 1 provides examples of postdischarge adverse events seen in the usual care arm that might have been prevented in the intervention.
DISCUSSION
This intervention was associated with a reduction in postdischarge adverse events. The relative improvement in each outcome aligned with the hypothesized sensitivity to change: the smallest improvement was seen in new or worsening signs or symptoms, followed by postdischarge adverse events and then by preventable postdischarge adverse events. The intervention was not associated with a difference in readmissions. The lack of effect on hospital readmissions may have been caused by the low proportion of readmissions that are preventable, as well as low intervention fidelity and lack of resources to implement facets such as postdischarge coaching, an evidence-based intervention that was never adopted.16,23 One lesson of this study is that it may be easier to reduce postdischarge injury (still an important outcome) than readmissions.
Putting this study in context, we should note that the literature on interventions to improve care transitions is mixed.18 While there are several reports of successful interventions, there are many reports of unsuccessful ones, often using similar components. Success is often the result of adequate resources and attention to myriad details regarding implementation.24 The intervention in our study likely contributed to improvements in patient and caregiver engagement in the hospital, enhancements of communication between inpatient and outpatient clinicians, and implementation of pharmacist-led interventions to improve medication safety. Regarding the latter, several prior studies have shown the benefits of pharmacist interventions in decreasing postdischarge adverse drug events.4,25,26 Therefore, even an intervention with incomplete intervention fidelity can reduce postdischarge adverse events, especially because adverse drug events make up the majority of adverse events.1,2,21
Perhaps the biggest lesson we learned was regarding the limitations of the hospital-led ACO model to incentivize sufficient up-front investments in transitional care interventions. By design, we chose a real-world approach in which interventions were integrated with existing ACO efforts, which were paid for internally by the institution. As a result, many of the interventions had to be scaled back because of resource constraints. The ACO model theoretically incentivizes more integrated care, but this may not always be true in practice. Emerging evidence suggests that physician group–led ACOs are associated with lower costs and use compared with hospital-led ACOs, likely because of more aligned incentives in physician group–led ACOs to reduce use of inpatient care.27,28
An unresolved question is whether the ideal implementation approach is to protect the time of existing clinical personnel to carry out transitional care tasks or to hire external personnel to do these tasks. We purposely spread the intervention over several clinician types to minimize the additional burden on any one of them, minimize additional costs, and play to each clinician’s expertise, but in retrospect, this may not have been the right approach. By providing additional personnel with dedicated time, interest, and training in care transitions, the intervention may be delivered with higher quantitative and qualitative fidelity, and it could create a single point of contact for patients, which was considered highly desirable by our PFAC.
This study has several limitations. A large proportion of patients (44%) were unavailable for postdischarge phone calls. However, we were able to perform medical record review for worsening signs (eg, lab abnormalities) and symptoms (as reported by patients’ providers) in the postdischarge period and adjudicate them for adverse events for all but 69 of these patients. Because all these patients had ACO-affiliated PCPs, we would expect most of their utilization to have been within the system and, therefore, to be present in the medical record. The proportion of patients with at least one adverse event did not vary by the method of follow-up, which suggests that this issue is an unlikely source of bias. Assessment of readmission was imperfect because we do not have statewide or national data. However, our combination of administrative data for Partners readmissions plus self-report for non-Partners readmission has been shown to be fairly complete in previous studies.29 Adjudicators could not be fully blinded to intervention status due to the lack of blinding of admission date. We did not calculate a kappa value for interrater reliability of individual assessments of adverse events; rather, coming to consensus among the two adjudicators was part of the process. In only a handful of cases was a third adjudicator required. Lastly, this study was conducted at two academic medical centers and their affiliated primary care clinics, which potentially limits generalizability; however, the results are likely generalizable to other ACOs that include major academic medical centers.
CONCLUSION
In conclusion, in this real-world clinical trial, we designed, implemented, and iteratively refined a multifaceted intervention to improve care transitions within a hospital-based academic ACO. Evolution of the intervention components was the result of stakeholder input, experience with the intervention, and ACO resource constraints. The intervention reduced postdischarge adverse events. However, across the ACO network, intervention fidelity was low, and this may have contributed to the lack of effect on readmission rates. ACOs that implement interventions without hiring new personnel or protecting the time of existing personnel to conduct transitional tasks are likely to face the same challenges of low fidelity.
Acknowledgments
The authors would like to acknowledge the many people who worked on designing, implementing, and evaluating this intervention, including but not limited to: Natasha Isaac, Hilary Heyison, Jacqueline Minahan, Molly O’Reilly, Michelle Potter, Nailah Khoory, Maureen Fagan, David Bates, Laura Carr, Joseph Frolkis, Eric Weil, Jacqueline Somerville, Stephanie Ahmed, Marcy Bergeron, Jessica Smith, and Jane Millett. We would also like to thank the members of our Patient-Family Advisory Council: Maureen Fagan, Karen Spikes, Margie Hodges, Win Hodges, Aureldon Henderson, Dena Salzberg, and Kay Bander.
Transitions from the hospital to the ambulatory setting are high-risk periods for patients in terms of adverse events, poor clinical outcomes, and readmission. Processes of care during care transitions are suboptimal, including poor communication among inpatient providers, patients, and ambulatory providers1,2; suboptimal communication of postdischarge plans of care to patients and their ability to carry out these plans3; medication discrepancies and nonadherence after discharge4; and lack of timely follow-up with ambulatory providers.5 Healthcare organizations continue to struggle with the question of which interventions to implement and how best to implement them.
Interventions to improve care transitions typically focus on readmission rates, but some studies have focused on postdischarge adverse events, defined as injuries in the 30 days after discharge caused by medical management rather than underlying disease processes.2 These adverse events cause psychological distress, out-of-pocket expenses, decreases in functional status, and caregiver burden. An estimated 20% of hospitalized patients suffer a postdischarge adverse event.1,2 Approximately two-thirds of these may be preventable or ameliorable.
The advent of Accountable Care Organizations (ACOs), defined as “groups of doctors, hospitals, and other health care providers who come together voluntarily to give coordinated high quality care to their patients,” creates an opportunity for improvements in patient safety during care transitions.6 Another opportunity has been the advent of Patient-Centered Medical Homes (PCMH), consisting of patient-oriented, comprehensive, team-based primary care enhanced by health information technology and population-based disease management tools.7,8 In theory, a hospital-PCMH collaboration within an ACO can improve transitional interventions since optimal communication and collaboration are more likely when both inpatient and primary care providers (PCPs) share infrastructure and are similarly incentivized. The objectives of this study were to design and implement a collaborative hospital-PCMH care transitions intervention within an ACO and evaluate its effects.
METHODS
This study was a two-arm, single-blind (blinded outcomes assessor), stepped-wedge, multisite cluster-randomized clinical trial (NCT02130570) approved by the institutional review board of Partners HealthCare.
Study Design and Randomization
The study employed a “stepped-wedge” design, which is a cluster-randomized study design in which an intervention is sequentially rolled out to different groups at different, prespecified, randomly determined times.9 Each cluster (in this case, each primary care practice) served as its own control, while still allowing for adjustment for temporal trends. Originally, 18 practices participated, but one withdrew due to the low number of patients enrolled in the study, leaving 17 clusters and 16 sequences; see Figure 1 of Appendix 1 for a full description of the sample size and timeline for each cluster. Practices were not aware of this timeline until after recruitment.
Study Setting and Participants
Conducted within a large Pioneer ACO in Boston and funded by the Patient-Centered Outcomes Research Institute (PCORI), the Partners-PCORI Transitions Study was designed as a “real-world” quality improvement project. Potential participants were adult patients who were admitted to medical and surgical services of two large academic hospitals (Hospital A and Hospital B) affiliated with an ACO, who were likely to be discharged back to the community, and whose PCP belonged to a primary care practice that was affiliated with the ACO, agreed to participate, and were designated PCMHs or on their way to being designated by meeting certain criteria: electronic health record, patient portal, team-based care, practice redesign, care management, and identification of high-risk patients. See Study Protocol (Appendix 2) for detailed patient and primary care practice inclusion criteria.
Patient Enrollment
Study staff screened participants from a daily automated list of patients admitted the day before, using medical records to determine eligibility, which was then confirmed by the patient’s nurse. Exclusion criteria included likely discharge to a location other than home, being in police custody, lack of a telephone, being homeless, previous enrollment in the study, and being unable to communicate in English or Spanish. Allocation to study arm was concealed until the patient or proxy provided informed written consent. The research assistant administered questionnaires to all study subjects to assess potential confounders and functional status 1 month prior to admission (
Intervention
The intervention was based on a conceptual model of an ideal discharge11 that we developed based on work by Naylor et al,12 work by Coleman and Berenson,3 best practices in medication reconciliation and information transfer according to our own research,13-15 the best examples of interventions to improve the discharge process,12,16,17 and a systematic review of discharge interventions.18 Some of the factors necessary for an ideal care transition include complete, organized, and timely documentation of the patient’s hospital course and postdischarge plan; effective discharge planning; coordination of care among the patient’s providers; methods to ensure medication safety; advanced care planning in appropriate patients; and education and “coaching” of patients and their caregivers so they learn how to manage their conditions. The final multifaceted intervention addressed each component of the ideal discharge and included inpatient and outpatient components (Table 1 and Table 1 of Appendix 1).
Patient and Public Involvement in Research
As with all PCORI-funded studies, this study involved a patient-family advisory council (PFAC). Our PFAC included six recently hospitalized patients or caregivers of recently hospitalized patients. The PFAC participated in monthly meetings throughout the study period. They helped inform the research questions, including confirmation that the endpoints were patient centered, and provided valuable input for the design of the intervention and the patient-facing components of the data collection instruments. They also interviewed several patient participants in the study regarding their experiences with the intervention. Lastly, they helped develop plans for dissemination of study results to the public.19
We also formed a steering committee consisting of physician, nursing, pharmacy, information technology, and administrative leadership representing primary care, inpatient care, and transitional care at both hospitals and Partners Healthcare. PFAC members took turns participating in quarterly steering committee meetings.
Evolution of the Intervention and Implementation
The intervention was iteratively refined during the course of the study in response to input from the PFAC, steering committee, and members of the intervention team; cases of adverse events and readmissions from patients despite being in the intervention arm; exit interviews of patients who had recently completed the intervention; and informal feedback from inpatient and outpatient clinicians. For example, we learned that the more complicated a patient’s conditions are, the sooner the clinical team wanted them to be seen after discharge. However, these patients were also less likely to feel well enough to keep that appointment. Therefore, the timing of follow-up of appointments needed to be a negotiation among the inpatient team, the patient, any caregivers, and the outpatient provider. PFAC members also emphasized that patients wanted one person to trust and to be the “point person” during a complicated transition such as hospital discharge.
At the same time, the intervention components evolved because of factors outside our control (eg, resource limitations). In keeping with the real-world nature of the research, the aim was for the intervention to be internally supported because incentives were theoretically more aligned with improvement of care transitions under the ACO model. By design, the PCORI contract only paid for limited parts of the intervention, such as a nurse practitioner to act as the discharge advocate at one hospital, overtime costs of inpatient pharmacists, and project manager time to facilitate inpatient-outpatient provider communication. (See Table 1 of Appendix 1 for details about the modifications to the intervention.)
Lastly, in keeping with PCORI’s methodology standards for studies of complex interventions,20 we strove to standardize the intervention by function across hospitals, units, and practices, while still allowing for local adaptation in the form. In other words, rather than specifying exactly how a task (eg, medication counseling) needed to be performed, the study design offered sites flexibility in how they implemented the task given their available personnel and institutional culture.
Intervention Fidelity
To determine the extent to which each patient in the intervention arm received each intervention component, a project manager unblinded to treatment arm reviewed the electronic medical record for documentation of each component implemented by providers (eg, inpatient pharmacists, outpatient nurses). Because each intervention component produced documentation, this provided an accurate assessment of intervention fidelity, ie, the extent to which the intervention was implemented as intended.
Outcome Assessment
Postdischarge Follow-up
Based on previous studies,2,21 a trained research assistant attempted to contact all study subjects 30 days (±5 days) after discharge and administered a questionnaire to identify any new or worsening symptoms since discharge, any healthcare use since discharge, and functional status in the previous week. Follow-up questions used branching logic to determine the relationship of any new or worsening symptoms to medications or other aspects of medical management. Research assistants followed up any positive responses with directed medical record review for objective findings, diagnoses, treatments, and responses. If patients could not be reached after five attempts, the research assistant instead conducted a thorough review of the outpatient medical record alone for provider reports of any new or worsening symptoms noted during follow-up within the 30-day postdischarge period. Research assistants also reviewed laboratory test results in all patients for evidence of postdischarge renal failure, elevated liver function tests, or new/worsening anemia.
Hospital Readmissions
We measured nonelective hospital readmissions within 30 days of discharge using a combination of administrative data for hospitalizations within the ACO network plus patient report during the 30-day phone call for all other readmissions.22
Adjudication of Outcomes
Adverse events and preventable adverse events: All cases of new or worsening symptoms or signs, along with all supporting documentation, were then presented to teams of two trained blinded physician adjudicators through application of methods established in previous studies.4,21 Each of the two adjudicators independently reviewed the information, along with the medical record, and completed a standardized form to confirm or deny the presence of any adverse events (ie, patient injury due to medical management) and to classify the type of event (eg, adverse drug event, hospital-acquired infection, procedural complication, diagnostic or management error), the severity and duration of the event, and whether the event was preventable or ameliorable. The two adjudicators then met to resolve any differences in their findings and come to consensus.
Preventable readmissions: If patients were readmitted to either study hospital, we conducted an evaluation, based on previous studies,23 to determine if and how the readmission could have been prevented including (a) a standardized patient and caregiver interview to identify possible problems with the transitions process and (b) an email questionnaire to the patient’s PCP and the inpatient teams who cared for the patient during the index admission and readmission regarding possible deficiencies with the transitions process. As with adverse event adjudications, two physician adjudicators worked independently to classify the preventability of the readmission and then met to come to consensus. Conflicts were resolved by a third adjudicator.
Analysis Plan
To evaluate the effects of the intervention on the primary outcome, the number of postdischarge adverse events per patient, we used multivariable Poisson regression, with study arm as the main predictor. A similar approach was used to evaluate the number of new or worsening postdischarge signs or symptoms and the number of preventable adverse events per patient. We used an intention-to-treat analysis: If a practice did not start the intervention when they were scheduled to, based on our randomization, we counted all patients in that practice admitted after that point as intervention patients. We adjusted for patient demographics, clinical characteristics, month, inpatient unit, and primary care practice as fixed effects. We clustered by PCP using general linear models. Intervention effects were expressed as both unadjusted and adjusted incidence rate ratios (IRRs). We also conducted a limited number of subgroup analyses, determined a priori, to determine whether the intervention was more effective in certain patient populations; we used interaction terms (intervention × subgroup) to determine the statistical significance of any effect modification.
To evaluate the effects of the intervention on nonelective readmissions and preventable readmissions, we used a similar approach, using multivariable logistic regression. Postdischarge functional status, adjusted for status prior to admission, was analyzed using multivariable linear regression and random effects by primary care practice. The general linear mixed model (GLIMMIX) procedure in the SAS 9.3 statistical package (SAS Institute) was used to carry out all analyses.
Power and Sample Size
We assumed a baseline rate of postdischarge adverse events of 0.30 per patient.21 We conservatively assumed an effect size of a change from 0.30 in the control group to 0.23 in the intervention group (a relative reduction of 22%, which was based on studies of preventability rates23 and close to the minimum clinically important difference). Based on prior studies,4,22 we assumed an intraclass correlation coefficient of 0.01 with an average cluster size of seven patients per PCP. Assuming a 10% loss to follow-up rate and an alpha of 0.05, we targeted a sample size of 1,800 patients to achieve 80% power, with one-third of the patients in the usual care arm and two-thirds in the intervention arm.
RESULTS
We enrolled 18 PCMH primary care practices to participate in the study, including 8 from Hospital A (out of 13 approached), 8 from Hospital B (out of 11), and 2 from other ACO practices (out of 9) (plus two pilot practices). Reasons for not participating included not having dedicated personnel to play the role of the responsible outpatient clinician, undergoing recent turn-over in practice leadership, and not having enough patients admitted to the two hospitals. One practice only enrolled 5 patients in the study and withdrew from participation, which left 17 practices.
Study Patients
We enrolled 1,679 patients (Figure 1). Reasons for nonenrollment included being unable to complete the screen prior to discharge, not meeting inclusion criteria or meeting exclusion criteria, being assigned to a pilot practice, and declining informed written consent. The baseline characteristics of enrolled patients are presented in Table 2. Differences between the two study arms were small. About 47% of the cohort was not reachable by phone after five attempts for the 30-day phone call, but only 69 (4.1%) were truly lost to follow-up because they were unreachable by phone and had no documentation in the electronic medical record in the 30-days after discharge.
Intervention Fidelity
The majority of patients did not receive most intervention components, even those components that were supposed to be delivered to all intervention patients (Table 3). A minority of patients were referred to visiting nurse services and to the home pharmacy program. However, 855 patients (87%) in the intervention arm received at least one intervention component.
Outcome Measures
The intervention was associated with a statistically significant reduction in several of the outcomes of interest, including the primary outcome, number of postdischarge adverse events (45% reduction), and new or worsening postdischarge signs or symptoms (22% reduction), as well as preventable postdischarge adverse events (58% reduction) (Table 4). There was a nonsignificant difference in functional status. There was no significant effect on total nonelective or on preventable readmission rates. When analyzed by type of adverse event, the intervention was associated with a reduction in adverse drug events and in procedural complications (Table 2 of Appendix 1). Of note, there was no significant difference in the proportion of patients with at least one adverse event whether the outcome was determined by phone call and medical record review (49%) or medical record review alone (51%) (P = .48).
In subgroup analyses, there was no evidence of effect modification by service, hospital, patient age, readmission risk, health literacy, or comorbidity score (Table 3 of Appendix 1). Table 4 of Appendix 1 provides examples of postdischarge adverse events seen in the usual care arm that might have been prevented in the intervention.
DISCUSSION
This intervention was associated with a reduction in postdischarge adverse events. The relative improvement in each outcome aligned with the hypothesized sensitivity to change: the smallest improvement was seen in new or worsening signs or symptoms, followed by postdischarge adverse events and then by preventable postdischarge adverse events. The intervention was not associated with a difference in readmissions. The lack of effect on hospital readmissions may have been caused by the low proportion of readmissions that are preventable, as well as low intervention fidelity and lack of resources to implement facets such as postdischarge coaching, an evidence-based intervention that was never adopted.16,23 One lesson of this study is that it may be easier to reduce postdischarge injury (still an important outcome) than readmissions.
Putting this study in context, we should note that the literature on interventions to improve care transitions is mixed.18 While there are several reports of successful interventions, there are many reports of unsuccessful ones, often using similar components. Success is often the result of adequate resources and attention to myriad details regarding implementation.24 The intervention in our study likely contributed to improvements in patient and caregiver engagement in the hospital, enhancements of communication between inpatient and outpatient clinicians, and implementation of pharmacist-led interventions to improve medication safety. Regarding the latter, several prior studies have shown the benefits of pharmacist interventions in decreasing postdischarge adverse drug events.4,25,26 Therefore, even an intervention with incomplete intervention fidelity can reduce postdischarge adverse events, especially because adverse drug events make up the majority of adverse events.1,2,21
Perhaps the biggest lesson we learned was regarding the limitations of the hospital-led ACO model to incentivize sufficient up-front investments in transitional care interventions. By design, we chose a real-world approach in which interventions were integrated with existing ACO efforts, which were paid for internally by the institution. As a result, many of the interventions had to be scaled back because of resource constraints. The ACO model theoretically incentivizes more integrated care, but this may not always be true in practice. Emerging evidence suggests that physician group–led ACOs are associated with lower costs and use compared with hospital-led ACOs, likely because of more aligned incentives in physician group–led ACOs to reduce use of inpatient care.27,28
An unresolved question is whether the ideal implementation approach is to protect the time of existing clinical personnel to carry out transitional care tasks or to hire external personnel to do these tasks. We purposely spread the intervention over several clinician types to minimize the additional burden on any one of them, minimize additional costs, and play to each clinician’s expertise, but in retrospect, this may not have been the right approach. By providing additional personnel with dedicated time, interest, and training in care transitions, the intervention may be delivered with higher quantitative and qualitative fidelity, and it could create a single point of contact for patients, which was considered highly desirable by our PFAC.
This study has several limitations. A large proportion of patients (44%) were unavailable for postdischarge phone calls. However, we were able to perform medical record review for worsening signs (eg, lab abnormalities) and symptoms (as reported by patients’ providers) in the postdischarge period and adjudicate them for adverse events for all but 69 of these patients. Because all these patients had ACO-affiliated PCPs, we would expect most of their utilization to have been within the system and, therefore, to be present in the medical record. The proportion of patients with at least one adverse event did not vary by the method of follow-up, which suggests that this issue is an unlikely source of bias. Assessment of readmission was imperfect because we do not have statewide or national data. However, our combination of administrative data for Partners readmissions plus self-report for non-Partners readmission has been shown to be fairly complete in previous studies.29 Adjudicators could not be fully blinded to intervention status due to the lack of blinding of admission date. We did not calculate a kappa value for interrater reliability of individual assessments of adverse events; rather, coming to consensus among the two adjudicators was part of the process. In only a handful of cases was a third adjudicator required. Lastly, this study was conducted at two academic medical centers and their affiliated primary care clinics, which potentially limits generalizability; however, the results are likely generalizable to other ACOs that include major academic medical centers.
CONCLUSION
In conclusion, in this real-world clinical trial, we designed, implemented, and iteratively refined a multifaceted intervention to improve care transitions within a hospital-based academic ACO. Evolution of the intervention components was the result of stakeholder input, experience with the intervention, and ACO resource constraints. The intervention reduced postdischarge adverse events. However, across the ACO network, intervention fidelity was low, and this may have contributed to the lack of effect on readmission rates. ACOs that implement interventions without hiring new personnel or protecting the time of existing personnel to conduct transitional tasks are likely to face the same challenges of low fidelity.
Acknowledgments
The authors would like to acknowledge the many people who worked on designing, implementing, and evaluating this intervention, including but not limited to: Natasha Isaac, Hilary Heyison, Jacqueline Minahan, Molly O’Reilly, Michelle Potter, Nailah Khoory, Maureen Fagan, David Bates, Laura Carr, Joseph Frolkis, Eric Weil, Jacqueline Somerville, Stephanie Ahmed, Marcy Bergeron, Jessica Smith, and Jane Millett. We would also like to thank the members of our Patient-Family Advisory Council: Maureen Fagan, Karen Spikes, Margie Hodges, Win Hodges, Aureldon Henderson, Dena Salzberg, and Kay Bander.
1. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345-349.
2. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161-167. https://doi.org/10.7326/0003-4819-138-3-200302040-00007
3. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141(7):533-536. https://doi.org/10.7326/0003-4819-141-7-200410050-00009
4. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166(5):565-571. https://doi.org/10.1001/archinte.166.5.565
5. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. https://doi.org/10.1056/nejmsa0803563
6. Accountable Care Organizations (ACOs). Centers for Medicare & Medicaid Services. 2012. Updated February 11, 2020. Accessed July 15, 2012. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO/index.html?redirect=/ACO/
7. Bates DW, Bitton A. The future of health information technology in the patient-centered medical home. Health Aff (Millwood). 2010;29(4):614-621. https://doi.org/10.1377/hlthaff.2010.0007
8. Bitton A, Martin C, Landon BE. A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med. 2010;25(6):584-592. https://doi.org/10.1007/s11606-010-1262-8
9. Brown C, Lilford R. Evaluating service delivery interventions to enhance patient safety. BMJ. 2008;337:a2764. https://doi.org/10.1136/bmj.a2764
10. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. https://doi.org/10.1097/00005650-199603000-00003
11. Burke RE, Kripalani S, Vasilevskis EE, Schnipper JL. Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102-109. https://doi.org/10.1002/jhm.1990
12. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620. https://doi.org/10.1001/jama.281.7.613
13. Gandara E, Ungar J, Lee J, Chan-Macrae M, O’Malley T, Schnipper JL. Discharge documentation of patients discharged to subacute facilities: a three-year quality improvement process across an integrated health care system. Jt Comm J Qual Patient Saf. 2010;36(6):243-251. https://doi.org/10.1016/s1553-7250(10)36039-9
14. Pippins JR, Gandhi TK, Hamann C, et al. Classifying and predicting errors of inpatient medication reconciliation. J Gen Intern Med. 2008;23(9):1414-1422. https://doi.org/10.1007/s11606-008-0687-9
15. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. https://doi.org/10.1001/archinternmed.2009.51
16. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822-1828. https://doi.org/10.1001/archinte.166.17.1822
17. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178-187. https://doi.org/10.7326/0003-4819-150-3-200902030-00007
18. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. https://doi.org/10.7326/0003-4819-155-8-201110180-00008
19. Schnipper J, Levine C. The important thing to do before leaving the hospital: many patients and families forget, which can lead to complications later. Next Avenue. October 22, 2019. Accessed September 10, 2020. https://www.nextavenue.org/before-leaving-hospital/
20. PCORI Methodology Standards: Standards for Studies of Complex Interventions. Patient-Centered Outcomes Research Institute; November 12, 2015. Updated: February 26, 2019. Accessed June 3, 2019. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Complex
21. Tsilimingras D, Schnipper J, Duke A, et al. Post-discharge adverse events among urban and rural patients of an urban community hospital: a prospective cohort study. J Gen Intern Med. 2015;30(8):1164-1171. https://doi.org/10.1007/s11606-015-3260-3
22. Kripalani S, Roumie CL, Dalal AK, 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. https://doi.org/10.7326/0003-4819-157-1-201207030-00003
23. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176(4):484-493. https://doi.org/10.1001/jamainternmed.2015.7863
24. Vasilevskis EE, Kripalani S, Ong MK, et al. Variability in implementation of interventions aimed at reducing readmissions among patients with heart failure: a survey of teaching hospitals. Acad Med. 2016;91(4):522-529. https://doi.org/10.1097/acm.0000000000000994
25. Gardella JE, Cardwell TB, Nnadi M. Improving medication safety with accurate preadmission medication lists and postdischarge education. Jt Comm J Qual Patient Saf. 2012;38(10):452-458. https://doi.org/10.1016/s1553-7250(12)38060-4
26. Kaboli PJ, Hoth AB, McClimon BJ, Schnipper JL. Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955-964. https://doi.org/10.1001/archinte.166.9.955
27. McWilliams JM, Hatfield LA, Chernew ME, Landon BE, Schwartz AL. Early performance of accountable care organizations in Medicare. N Engl J Med. 2016;374(24):2357-2366. https://doi.org/10.1056/nejmsa1600142
28. McWilliams JM, Hatfield LA, Landon BE, Hamed P, Chernew ME. Medicare spending after 3 years of the Medicare Shared Savings Program. N Engl J Med. 2018;379(12):1139-1149. https://doi.org/10.1056/nejmsa1803388
29. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211-219. https://doi.org/10.1007/s11606-009-1196-1
30. Donzé JD, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632-638. https://doi.org/10.001/jamainternmed.2013.3023
31. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016;176(4):496-502. https://doi.org/10.1001/jamainternmed.2015.8462
1. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345-349.
2. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161-167. https://doi.org/10.7326/0003-4819-138-3-200302040-00007
3. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141(7):533-536. https://doi.org/10.7326/0003-4819-141-7-200410050-00009
4. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166(5):565-571. https://doi.org/10.1001/archinte.166.5.565
5. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. https://doi.org/10.1056/nejmsa0803563
6. Accountable Care Organizations (ACOs). Centers for Medicare & Medicaid Services. 2012. Updated February 11, 2020. Accessed July 15, 2012. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ACO/index.html?redirect=/ACO/
7. Bates DW, Bitton A. The future of health information technology in the patient-centered medical home. Health Aff (Millwood). 2010;29(4):614-621. https://doi.org/10.1377/hlthaff.2010.0007
8. Bitton A, Martin C, Landon BE. A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med. 2010;25(6):584-592. https://doi.org/10.1007/s11606-010-1262-8
9. Brown C, Lilford R. Evaluating service delivery interventions to enhance patient safety. BMJ. 2008;337:a2764. https://doi.org/10.1136/bmj.a2764
10. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. https://doi.org/10.1097/00005650-199603000-00003
11. Burke RE, Kripalani S, Vasilevskis EE, Schnipper JL. Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102-109. https://doi.org/10.1002/jhm.1990
12. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620. https://doi.org/10.1001/jama.281.7.613
13. Gandara E, Ungar J, Lee J, Chan-Macrae M, O’Malley T, Schnipper JL. Discharge documentation of patients discharged to subacute facilities: a three-year quality improvement process across an integrated health care system. Jt Comm J Qual Patient Saf. 2010;36(6):243-251. https://doi.org/10.1016/s1553-7250(10)36039-9
14. Pippins JR, Gandhi TK, Hamann C, et al. Classifying and predicting errors of inpatient medication reconciliation. J Gen Intern Med. 2008;23(9):1414-1422. https://doi.org/10.1007/s11606-008-0687-9
15. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. https://doi.org/10.1001/archinternmed.2009.51
16. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822-1828. https://doi.org/10.1001/archinte.166.17.1822
17. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178-187. https://doi.org/10.7326/0003-4819-150-3-200902030-00007
18. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. https://doi.org/10.7326/0003-4819-155-8-201110180-00008
19. Schnipper J, Levine C. The important thing to do before leaving the hospital: many patients and families forget, which can lead to complications later. Next Avenue. October 22, 2019. Accessed September 10, 2020. https://www.nextavenue.org/before-leaving-hospital/
20. PCORI Methodology Standards: Standards for Studies of Complex Interventions. Patient-Centered Outcomes Research Institute; November 12, 2015. Updated: February 26, 2019. Accessed June 3, 2019. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Complex
21. Tsilimingras D, Schnipper J, Duke A, et al. Post-discharge adverse events among urban and rural patients of an urban community hospital: a prospective cohort study. J Gen Intern Med. 2015;30(8):1164-1171. https://doi.org/10.1007/s11606-015-3260-3
22. Kripalani S, Roumie CL, Dalal AK, 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. https://doi.org/10.7326/0003-4819-157-1-201207030-00003
23. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176(4):484-493. https://doi.org/10.1001/jamainternmed.2015.7863
24. Vasilevskis EE, Kripalani S, Ong MK, et al. Variability in implementation of interventions aimed at reducing readmissions among patients with heart failure: a survey of teaching hospitals. Acad Med. 2016;91(4):522-529. https://doi.org/10.1097/acm.0000000000000994
25. Gardella JE, Cardwell TB, Nnadi M. Improving medication safety with accurate preadmission medication lists and postdischarge education. Jt Comm J Qual Patient Saf. 2012;38(10):452-458. https://doi.org/10.1016/s1553-7250(12)38060-4
26. Kaboli PJ, Hoth AB, McClimon BJ, Schnipper JL. Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955-964. https://doi.org/10.1001/archinte.166.9.955
27. McWilliams JM, Hatfield LA, Chernew ME, Landon BE, Schwartz AL. Early performance of accountable care organizations in Medicare. N Engl J Med. 2016;374(24):2357-2366. https://doi.org/10.1056/nejmsa1600142
28. McWilliams JM, Hatfield LA, Landon BE, Hamed P, Chernew ME. Medicare spending after 3 years of the Medicare Shared Savings Program. N Engl J Med. 2018;379(12):1139-1149. https://doi.org/10.1056/nejmsa1803388
29. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211-219. https://doi.org/10.1007/s11606-009-1196-1
30. Donzé JD, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632-638. https://doi.org/10.001/jamainternmed.2013.3023
31. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016;176(4):496-502. https://doi.org/10.1001/jamainternmed.2015.8462
The Effects of a Multifaceted Intervention to Improve Care Transitions Within an Accountable Care Organization: Results of a Stepped-Wedge Cluster-Randomized Trial
This work is licensed under a Creative Commons Attribution 4.0 International License
The Effects of a Multifaceted Intervention to Improve Care Transitions Within an Accountable Care Organization: Results of a Stepped-Wedge Cluster-Randomized Trial
This work is licensed under a Creative Commons Attribution 4.0 International License
© 2021 Society of Hospital Medicine
Email: lsamal@bwh.harvard.edu; Telephone: 617-732-7812; Twitter: @LipikaSamalMD; @drjschnip.
Embedding a Discharge Facilitator
Recent studies have shown that a patient's discharge from the hospital is a vulnerable period for patient safety.14 With the reduction in length of stay (LOS) and the increase in patient acuity over the past decade, patients are discharged from acute care settings quicker and sicker, resulting in management of ongoing illness in a less‐monitored environment.5, 6 In addition, in teaching hospitals, residents are supervised by hospital‐based physicians who are rarely the primary care physician (PCP) for the residents' patients, which creates discontinuity of care.
One in 5 medical discharges is complicated by an adverse event believed, in part, to be due to poor communication between caregivers during this transition time.2 Discharge summaries, a key form of that communication, are not always done in a timely fashion and may lack key pieces of information.7, 8 For approximately 68% of patient discharges, the PCP will not have a discharge summary available for the patient's first follow‐up visit.911 In a survey of PCPs whose patients were in the hospital, only 23% reported direct communication with the hospital care team.12 This leaves PCPs unaware of pending test results or recommended follow‐up evaluations.10, 11, 13, 14 All of these factors are believed to contribute to adverse events, emergency department (ED) visits, and readmissions.
A recently published consensus statement on transitions of care by 6 major medical societies emphasizes the need for timely communication and transfer of information.15 These important processes are especially challenging to meet at academic medical centers, where discharge summaries and transition communication are done by residents in a hectic and challenging work environment, with multiple simultaneous and competing demands including outpatient clinic and required conferences.12 Residents have little formal training in how to write an effective discharge summary or how to systematically approach discharge planning. One study found higher error rates in discharge summaries written by residents compared with attending physicians.16 While the Accreditation Council for Graduate Medical Education (ACGME) limits the number of admissions per intern for both patient safety and educational reasons, the number of discharges per day is not limited despite the considerable amount of time required for appropriate discharge planning and communication.
Many interventions have been tried to improve the discharge process and reduce patient adverse events.17 Arranging early follow‐up appointments to reduce emergency department visits and readmissions has shown mixed results.13, 1820 Interventions that focus on specific populations, such as the elderly or patients with congestive heart failure, have been more successful.2123 Some interventions employed additional resources, such as a discharge form, transition coach, or discharge advocate, again with varying impact on results.18, 2427 A recent study by Jack et al. used nurse discharge advocates (DAs) to help with discharge planning and communication at an academic medical center.25 These DAs were independent of the care team, and focused on patient education and follow‐up plans, and reduced hospital reutilization in a selected population.
No studies have assessed the potential benefit of helping residents with the physician components of the discharge process. Prior studies have mainly focused on patient communication and follow‐up appointments, yet safe transitions also involve timely discharge summaries, physician‐to‐physician communication, physician‐to‐nurse communication, and medication reconciliation. Without support and time, these tasks can be very challenging for resident physicians with work‐hour limitations. We undertook a randomized, controlled trial to evaluate the impact on the discharge process of embedding a discharge facilitator in a resident medical team to help with the physician discharge process. We studied the effect for all the patients discharged from the resident team, rather than focusing on a select group or patients with a single diagnosis.
METHODS
Study Setting and Participants
This study was conducted on 2 of the 5 resident general medical teams on the inpatient teaching service at Massachusetts General Hospital (MGH), Boston, Massachusettsa large, 907‐bed, urban hospital. The residents' teams are regionalized and each care for approximately 20 patients on a single floor. Each of the study teams consists of a junior resident, 4 interns, and 1 to 2 attendings who rotate on the floor for 2‐week or 4‐week blocks. Attending rounds, which occur 10 AM to 12 PM weekdays, are for new patient presentations and discussion of plans. Interdisciplinary rounds occur 9:30 AM to 10 AM. Sign‐out rounds occur in the afternoon whenever all work is complete. The junior resident is responsible for all the discharge orders and communication with PCPs, and the discharge summaries for patients going to facilities. The interns are responsible for discharge summaries for patients discharged home; these summaries are not mandatory at the time of discharge. The majority of patients were admitted under the team attending(s). Patients were assigned to the teams by the admitting office, based on bed availability. All patients discharged from both resident medical teams over a 5‐month period were included in this study. Those who were not discharged from the hospital by the study teams (ie, transfers to intensive care units or deaths) were excluded. These exclusions accounted for less than 12% of all team patients. Partners Healthcare System Institutional Review Board approved all study activities.
Intervention
We randomly assigned a discharge facilitator (DF), a master's level nurse practitioner with prior inpatient medicine experience, to 1 of the 5 resident medical teams. She had no prior experience on this specific floor. A similar resident team, on a different floor, served as the control. For the intervention team, the DF attended daily resident work rounds and interdisciplinary discharge rounds. The resident and DF collaborated in identifying patients being discharged in the next 1 to 3 days, and the DF scheduled all follow‐up appointments and tests. The DF performed medication reconciliation, wrote prescriptions and faxed them to pharmacies, and arranged all anticoagulation services. In collaboration with the resident, the DF called PCPs' offices with discharge information and faxed discharge summaries to PCPs' offices outside the Partners Healthcare System. The DF wrote part or all of the computer discharge orders and discharge summaries at the request of the resident and interns. All discharge summaries still needed to be reviewed, edited, and signed by the resident or interns. The DF also noted pending tests and studies at time of discharge, and followed up on these tests for the team. The DF met with all patients to answer any questions about their discharge plan, medications, and appointments; while residents are encouraged to do this, it is not done as consistently. She provided her business card for any questions after their discharge. Follow‐up patient calls to the DF were either answered by her or triaged to the appropriate person. The DF also communicated with the patient's nurse about the discharge plans. For all patients discharged over a weekend, the DF would arrange the follow‐up appointments on Mondays and call the patients at home.
For both teams, residents received letters at the start of their rotation notifying them of the study and asking them to complete discharge summaries within 24 hours. All residents in the program were expected to do an online discharge tutorial and attend a didactic lecture on discharge summaries. The residents on the intervention team received a 5‐minute orientation on how best to work with the DF. Residents were given the autonomy to decide how much to use the DF's services. The scheduling of follow‐up appointments on the control team was the responsibility of the team resident as per usual care. The nursing component of the discharge process, including patient discharge education, was the same on both teams. Nurses on both floors are identically trained on these aspects of care. The nurses on both teams were surveyed about perception of the discharge process prior to the intervention and after the intervention. A research assistant (RA) called patients discharged home on both teams, 1 week after discharge, to ask about satisfaction with the discharge process, to determine if the patients had any questions, and to verify patient knowledge regarding whom they should contact for problems. The RA also noted the end time of attending rounds each day and the start time of resident sign‐out.
Outcome Measures and Follow‐Up
At the time of discharge, the RA collected baseline data on all patients discharged from both teams, including the number of follow‐up appointments scheduled. Patients were tracked through electronic medical records to see if and when they attended their follow‐up appointments, whether they changed the appointment, and whether patients returned to a hospital emergency department or were readmitted to MGH or an affiliated Partners hospital within 30 days. For patients outside the MGHPartners system, the research assistant contacted primary care physician offices to document follow‐up. The remaining patient data was obtained through the MGHPartners computerized information system.
The primary outcomes of the study were length of stay, time of discharge, number of emergency department visits, hospital readmissions, numbers of discharge summaries completed in 24 hours, time from discharge to discharge summary completion, and whether the discharge summary was completed before follow‐up. Secondary outcomes were number of follow‐up PCP appointments made at time of discharge, percentage of follow‐up appointments attended and time from discharge to attending a follow‐up appointment, patient phone survey results, and nursing perception of the discharge process, as well as the percentage of attending rounds that ended on time and the time of resident sign‐out.
Statistical Analyses
Patient characteristics were compared between intervention and control teams using 2‐sample t tests or Wilcoxon rank sum tests for continuous variables, and chi‐square tests for categorical variables. Hours to discharge summary completion and hospital length of stay were summarized using median and interquartiles (IQR), and compared between the 2 teams using Wilcoxon rank sum tests. Categorical outcomes were compared using chi‐square tests. Two‐sided P values 0.05 were considered statistically significant. SAS version 9.2 (SAS Institute Inc, Cary, NC) was used for all statistical analyses.
RESULTS
Study Sample
During the 5‐month intervention (November 12, 2008 to April 14, 2009), a combined total of 999 patients were admitted to the intervention and control general medical teams. We excluded 96 patients who were not discharged but transferred to another service or intensive care units, and 24 patients who died. We also excluded 7 patients who were discharged from both teams the first day of the study, because the DF was not involved with the patients' discharge planning. That left 872 patients discharged to either home, a facility, or having left against medical advice (AMA) included in the study: 440 patients on the intervention team and 432 patients on the control team (Figure 1). Baseline patient demographic and clinical characteristics were similar across both teams with only gender being significantly different (Table 1). The mean age was 63 years (range, 1896) and the mean comorbidity score was 2.3 (range, 012). Of note, about a quarter of patients were discharged to facilities, about half were Medicare recipients, and approximately 80% had a PCP. The DF participated in the discharge process for nearly all of the intervention patients; she reported contributing approximately 50% of the content to the discharge summaries.
| Characteristics | Intervention Team | Control Team |
|---|---|---|
| n = 440 | n = 432 | |
| ||
| Mean age (SD), year | 63 (18) | 63 (18) |
| Women, n (%)* | 181 (41) | 207 (48) |
| Race, n (%) | ||
| White non‐Hispanic | 267 (61) | 243 (56) |
| Black non‐Hispanic | 24 (5) | 33 (8) |
| Hispanic | 21 (5) | 17 (4) |
| Unknown/other | 128 (29) | 139 (32) |
| Health insurance, n (%) | ||
| Medicare | 213 (48) | 226 (52) |
| Medicaid | 85 (19) | 81 (19) |
| Private | 110 (25) | 91 (21) |
| Other | 32 (7) | 34 (8) |
| PCP on admission, n (%) | 370 (84) | 356 (82) |
| Discharge disposition, n (%) | ||
| AMA | 12 (3) | 14 (3) |
| Home | 305 (69) | 315 (73) |
| Facility | 123 (28) | 103 (24) |
| Mean comorbidity index score (SD) | 2.3 (2.4) | 2.3 (2.4) |
| Diagnoses | ||
| Congestive heart failure | 30 (6%) | 27 (5%) |
| COPD/asthma | 34 (7%) | 47 (9%) |
| Cardiovascular disease | 54 (11%) | 50 (8%) |
| Alcohol/substance abuse | 29 (6%) | 34 (7%) |
| Gastrointestinal bleeds/ulcers | 38 (8%) | 41 (8%) |
| Hepatobiliary disease | 30 (6%) | 36 (7%) |
| Renal failure/kidney disease | 33 (7%) | 37 (7%) |
| Pneumonia | 36 (7%) | 22 (4%) |
| Musculoskeletal disease | 26 (5%) | 23 (5%) |
| Neurologic disease | 22 (4%) | 25 (5%) |
| Other | 163 (33%) | 172 (35%) |
Primary Outcomes
Primary outcomes from the 2 medical teams are listed in Table 2. In the intervention group, significantly more discharge summaries were completed within 24 hours compared to the control group (293 [67%] vs 207 [48%]; P < 0.0001). Since nearly all patients discharged to facilities must have a discharge summary at the time of discharge, the overall difference in completion rates came mainly from patients discharged home or having left AMA from the intervention team (177 [56%] vs 112 [34%]; P < 0.0001). For all discharge summaries, the median time to completion on the intervention team was 18.9 hours compared with 73.1 hours on the control team (P < 0.0001). More discharge summaries were completed before the first follow‐up appointment on the intervention team (393 [89%] vs 330 [76%]; P < 0.001). The DF intervention had no effect on 30‐day readmission or emergency department visits. For patients on the DF team, 88 (20%) were readmitted within 30 days of discharge, as compared with 79 (18%) on the control team (P = 0.55). Similarly, 40 (9%) of the intervention team patients, as compared with 39 (9%) of the control team patients, visited the emergency department at least once within 30 days (P = 1.0). There was no difference in length of stay (LOS) between the 2 teams (median 4.0 days for both teams, P = 0.84).
| Intervention Team | Control Team | ||
|---|---|---|---|
| Variables | n = 440 | n = 432 | P Value |
| |||
| Discharge summaries completed 24 hr, n (%) | 293 (67) | 207 (48) | <0.0001 |
| Discharges to facilities | 116 (94) | 95 (92) | 0.60 |
| Discharges to home/AMA | 177 (56) | 112 (34) | <0.0001 |
| Median hours to discharge summary completion for discharges to home/AMA (IQR) | 18.9 (0138) | 73.1 (4.3286) | <0.0001 |
| Discharge summary complete before time of follow‐up appointment. | 393 (89) | 330 (76) | <0.0001 |
| Emergency department visits in 30 days, n (%) | 40 (9) | 39 (9) | 1.0 |
| Readmissions in 30 days, n (%) | 88 (20) | 79 (18) | 0.55 |
| Median length of stay, days (IQR) | 4.0 (37) | 4.0 (28) | 0.84 |
| Discharges to facilities | 6.0 (511) | 8.0 (513) | 0.17 |
| Discharges to home/AMA | 4.0 (26) | 3.0 (26) | 0.61 |
| Discharged by noon, n (%) | 38 (9) | 42 (10) | 0.64 |
Secondary Outcomes
Table 3 shows secondary outcomes from the 2 medical teams. Among the patients discharged from the DF team, 264 (62%) had scheduled follow‐up appointments with PCPs compared to the control team 151 (36%) (P < 0.0001). (Many patients going to rehabilitation hospitals are not given PCP appointments at the time of discharge.) Despite having more scheduled appointments, patients' actual follow‐up with PCPs was similar during the 5‐month study period among both intervention and control group (234 [65%] vs 223 [63%]; P = 0.58). However, there was earlier follow‐up with the primary provider in the first 2 or 4 weeks in the intervention group. At 2 weeks, 129 (36%) patients in the intervention group saw their provider compared to 81 (23%) patients in the control group (P < 0.0002), and at 4 weeks, 159 (44%) of the intervention group was seen compared to 99 (28%) of the control group (P < 0.0001). Of note, among the 415 patients on both teams discharged with scheduled appointments, only 53 (13%) of patients did not show up for the scheduled appointment and this no‐show rate was the same on both teams.
| Variables | Intervention Team | Control Team | P Value |
|---|---|---|---|
| |||
| No. of eligible patients* | 428 | 418 | |
| Patients with follow‐up appointments to primary providers, n (%) | 264 (62) | 151 (36) | <0.0001 |
| No. of eligible patients | 359 | 354 | |
| Attended follow‐up appointment with primary provider during study, n (%) | 234 (65) | 223 (63) | 0.58 |
| Within 2 weeks of discharge | 129 (36) | 81 (23) | 0.0002 |
| Within 4 weeks of discharge | 159 (44) | 99 (28) | <0.0001 |
| No. of days round times were recorded | 100 | 99 | |
| No. of attending rounds ending by 12 PM | 45 (45%) | 31 (31%) | 0.058 |
| Mean start time of sign‐out rounds | 16:38 | 17:24 | 0.0007 |
Attending rounds ended on time (12 PM) 45% of the time in the intervention group compared to 31% in the control group (P = 0.058). Mean start time of resident sign‐out rounds was 1638 hours on the intervention team and 1724 hours on the control team (P = 0.0007).
We obtained patient reported outcome data by telephone within 2 to 4 weeks of discharge. Of the 620 patients discharged to home, 6 died or were readmitted to the hospital before being reached by phone. For the remaining 614 patients, we were able to contact 444 (72%). Of those, 321 (52%) agreed to participate in the phone interview. We surveyed similar proportions of intervention and control group patients (158 [52%] vs 163 [52%]) (Table 4). Both groups reported similar rates of having questions about their hospital stay after discharge (43 [27%] vs 49 [30%]; P = 0.62). The intervention group could better identify whom to call with questions (150 [95%] vs 138 [85%]; P = 0.003). The intervention group reported better understanding of their follow‐up plans (157 [99%] vs 141 [87%]; P = 0.001) and better understanding of their discharge medications (152 [96%] vs 142 [87%]; P = 0.001). More patients in the intervention group were satisfied with the discharge process (153 [97%] vs 124 [76%]; P < 0.0001).
| Intervention Team | Control Team | P Value | |
|---|---|---|---|
| |||
| Patients discharged home* | 304 | 310 | |
| Patients contacted by phone after discharge, n (%) | 213 (70) | 231 (75) | 0.24 |
| Agreed to participate in phone interview, n (%) | 158 (52) | 163 (53) | 0.94 |
| Among those agreed to participate, n (%) | |||
| Did you have questions about your hospital stay? | 43 (27) | 49 (30) | 0.62 |
| Would you know who to call if you had questions after discharge? | 150 (95) | 138 (85) | 0.003 |
| Satisfied with the discharge process? | 153 (97) | 124 (76) | <0.0001 |
| Did you understand your follow‐up plans? | 157 (99) | 141 (87) | <0.0001 |
| Did you understand your medications? | 152 (96) | 142 (87) | 0.001 |
| Did you feel safe going home? | 153 (97) | 151 (92) | 0.07 |
Compared with nurses on the control team, nurses on the intervention team more often reported paperwork being completed in a timely fashion (56% vs 29%; P = 0.041) and being less worried about the discharge plan (44% vs 57%; P = 0.027). The intervention team nurses also reported fewer issues with medications/prescriptions (61% vs 82%) and being included more often in the discharge planning (50% vs 38%). However, neither of these results reached statistical significance (P = 0.81 and 0.50, respectively).
DISCUSSION
Our study embedded a nurse practitioner on a busy resident general medical team to help with all aspects of the discharge process for which physicians are responsible. Previous studies have been limited to patients with specific diagnoses, age, or disposition plans.1825 In this study, we included all general medical patients. Our intervention improved several important quality of care elements: the timeliness of completion of discharge summaries; and increased number of early follow‐up appointments, with more patients seen within 2 and 4 weeks after discharge. Patients reported better understanding of their follow‐up plans and more satisfaction with the discharge process. While not statistically significant, there was a trend towards better communication with nurses. For residents with work‐hour limitations, there was time savings with a trend towards finishing attending rounds on time and statistically significant earlier sign‐out rounds (46 minutes earlier). This intervention had no effect on patient length of stay, readmissions, or emergency department visits in the 30 days after discharge.
Despite improving many aspects of the discharge process and communication that have previously been raised as areas of concern for patient safety, there was no improvement in readmissions rates and ED utilization which are often used as the quality indicators for effective discharge planning. Similar types of interventions on general medical patients have generally also failed to show improvement in readmission rates.1820, 25 Weinberger et al. arranged follow‐up appointments within 1 week for patients discharged from a Veterans Administrative hospital; while patients were seen more often, the intervention actually increased readmission rates.20 Fitzgerald et al. had a case manager contact patients at home and encourage follow‐up, which increased follow‐up visits, but again had no effect on readmission.19 Einstadter et al. had a nurse case manager coordinate outpatient follow‐up on a resident team and also did not effect readmission rates or ED visits.18 Jack et al. in project reengineered discharge (RED) did show a significant reduction in combined hospital utilization measures. However, their study focused on a more limited patient population, and employed both a discharge advocate to arrange follow‐up and improve patient education, and a pharmacist to make postdischarge phone calls.25
So why did readmissions rates and ED visits not change in our study? It would be reasonable to think that having earlier follow‐up appointments, better and timely physician‐to‐physician communication, and a facilitator for patient questions should improve the quality of the discharge process. In a recent study, Jha et al. found there was no association between chart‐based measures of discharge quality and readmissions rates, and only a modest association for patient‐reported measures of discharge quality and readmission rates.28 The authors suggest readmission rates are driven by many factors beyond just improved discharge safety. Perhaps readmission rates are too complex a measure to use to assess discharge process improvement. For fiscal reasons, it is understandable that hospitals, insurance companies, and the Centers for Medicare and Medicaid want to reduce readmission rates and ED utilization. Jencks et al. noted the cost of readmissions in 2004 was 17.4 billion dollars.29 However, sweeping efforts to improve the discharge process for all general medical patients may not yield significant reductions in readmissions, as this study suggests. We may need to focus aggressive intervention on smaller target populations, as prior studies on focused groups suggest.2123
There are no evidence‐based studies to suggest when optimal follow‐up should occur after discharge.26 Several medical society guidelines recommend 2 weeks. More patients on the intervention team were seen within 2 weeks, but readmission rates were not affected. The University Health System Consortium recently reported that the majority of readmissions occurred within 6 days, with the average being about 2 to 3 days.30 In this study, the median days to readmit were 12 for the intervention team and 10 for the control. It is possible that even with our improved 2‐week follow‐up, this was not early enough to reduce readmissions. Follow‐up may need to be within 13 days of discharge for highly vulnerable patients, to significantly change readmission rates. Further studies focusing on this question would be helpful.
Finally, with ACGME limitation of work hours, many residency programs are looking for ways to reduce residents' workload and increase time for education. With a significant trend towards finishing attending rounds on time, it is likely that more residents on the intervention team were able to attend the noon‐time educational conferences. We speculate that this was due to fewer interruptions during rounds because the DF was available for nurses' questions. Sign‐out rounds occurred significantly earlier, possibly because of improved resident efficiency due to the DF's help with the discharge process. While residents may lose some educational experience from not performing all discharge tasks, they gain experience working in interdisciplinary teams, have increased time for education, and reduced work hours. Since the ACGME limits the number of residents per program and increasing the residency size is not an option, a DF should be considered as a possible solution to ACGME work‐hour restrictions.
This study had several limitations. First, the intervention team had 1 specific person embedded, and therefore the results of this study may have limited generalizability. Second, the limited number of residents working with the DF could have biased the intervention, as not all residents worked equally well with the DF. However, this may represent the real‐world experience on any teaching service, given variation in working styles and learning curves of residents over their training. Third, this study was done at 1 university‐affiliated urban Academic Medical Center, making it potentially less generalizable to resident teams in community hospitals. Fourth, we were not able to capture readmissions and ED visits at institutions outside the MGHPartners Healthcare System. However, given that patients were assigned at random to either team, this factor should have impacted both teams equally. Fifth, the study occurred during Massachusetts healthcare reform which requires everyone to have health insurance. This may have affected the rates of ED visits and readmission rates, especially with a shortage of primary care physicians and office visits. Finally, this intervention was not cost‐neutral. Paying for a nurse practitioner to help residents with the work of discharge and providing patients with additional services had many advantages, but this quality improvement project did not pay for itself through shorter LOS, or decreases in ED visits or readmissions.
While readmission rates and ED utilization are important patient outcomes, especially in the current healthcare climate, what determines readmissions and ED visits is likely complex and multifactorial. This study suggests that, in the nationwide effort to reduce readmissions, solely improving the discharge process for all general medical patients may not produce the hoped‐for financial savings. Improving the discharge process, however, is something valuable in its own right. Adding a DF to a resident team does improve some quality markers of the discharge process and decreases work hours for residents.
Acknowledgements
Sara Macchiano, RN for her help with the data gathering of this study.
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- ,,,,,.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.
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- ,,.Tying up loose ends: Discharging patients with unresolved medical issues.Arch Intern Med.2007;167(12):1305–1311.
- ,,, et al.Patient safety concerns arising from test results that return after hospital discharge.Ann Intern Med.2005;143(2):121–128.
- ,,, et al.Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine.J Hosp Med.2009;4(6):364–370.
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Recent studies have shown that a patient's discharge from the hospital is a vulnerable period for patient safety.14 With the reduction in length of stay (LOS) and the increase in patient acuity over the past decade, patients are discharged from acute care settings quicker and sicker, resulting in management of ongoing illness in a less‐monitored environment.5, 6 In addition, in teaching hospitals, residents are supervised by hospital‐based physicians who are rarely the primary care physician (PCP) for the residents' patients, which creates discontinuity of care.
One in 5 medical discharges is complicated by an adverse event believed, in part, to be due to poor communication between caregivers during this transition time.2 Discharge summaries, a key form of that communication, are not always done in a timely fashion and may lack key pieces of information.7, 8 For approximately 68% of patient discharges, the PCP will not have a discharge summary available for the patient's first follow‐up visit.911 In a survey of PCPs whose patients were in the hospital, only 23% reported direct communication with the hospital care team.12 This leaves PCPs unaware of pending test results or recommended follow‐up evaluations.10, 11, 13, 14 All of these factors are believed to contribute to adverse events, emergency department (ED) visits, and readmissions.
A recently published consensus statement on transitions of care by 6 major medical societies emphasizes the need for timely communication and transfer of information.15 These important processes are especially challenging to meet at academic medical centers, where discharge summaries and transition communication are done by residents in a hectic and challenging work environment, with multiple simultaneous and competing demands including outpatient clinic and required conferences.12 Residents have little formal training in how to write an effective discharge summary or how to systematically approach discharge planning. One study found higher error rates in discharge summaries written by residents compared with attending physicians.16 While the Accreditation Council for Graduate Medical Education (ACGME) limits the number of admissions per intern for both patient safety and educational reasons, the number of discharges per day is not limited despite the considerable amount of time required for appropriate discharge planning and communication.
Many interventions have been tried to improve the discharge process and reduce patient adverse events.17 Arranging early follow‐up appointments to reduce emergency department visits and readmissions has shown mixed results.13, 1820 Interventions that focus on specific populations, such as the elderly or patients with congestive heart failure, have been more successful.2123 Some interventions employed additional resources, such as a discharge form, transition coach, or discharge advocate, again with varying impact on results.18, 2427 A recent study by Jack et al. used nurse discharge advocates (DAs) to help with discharge planning and communication at an academic medical center.25 These DAs were independent of the care team, and focused on patient education and follow‐up plans, and reduced hospital reutilization in a selected population.
No studies have assessed the potential benefit of helping residents with the physician components of the discharge process. Prior studies have mainly focused on patient communication and follow‐up appointments, yet safe transitions also involve timely discharge summaries, physician‐to‐physician communication, physician‐to‐nurse communication, and medication reconciliation. Without support and time, these tasks can be very challenging for resident physicians with work‐hour limitations. We undertook a randomized, controlled trial to evaluate the impact on the discharge process of embedding a discharge facilitator in a resident medical team to help with the physician discharge process. We studied the effect for all the patients discharged from the resident team, rather than focusing on a select group or patients with a single diagnosis.
METHODS
Study Setting and Participants
This study was conducted on 2 of the 5 resident general medical teams on the inpatient teaching service at Massachusetts General Hospital (MGH), Boston, Massachusettsa large, 907‐bed, urban hospital. The residents' teams are regionalized and each care for approximately 20 patients on a single floor. Each of the study teams consists of a junior resident, 4 interns, and 1 to 2 attendings who rotate on the floor for 2‐week or 4‐week blocks. Attending rounds, which occur 10 AM to 12 PM weekdays, are for new patient presentations and discussion of plans. Interdisciplinary rounds occur 9:30 AM to 10 AM. Sign‐out rounds occur in the afternoon whenever all work is complete. The junior resident is responsible for all the discharge orders and communication with PCPs, and the discharge summaries for patients going to facilities. The interns are responsible for discharge summaries for patients discharged home; these summaries are not mandatory at the time of discharge. The majority of patients were admitted under the team attending(s). Patients were assigned to the teams by the admitting office, based on bed availability. All patients discharged from both resident medical teams over a 5‐month period were included in this study. Those who were not discharged from the hospital by the study teams (ie, transfers to intensive care units or deaths) were excluded. These exclusions accounted for less than 12% of all team patients. Partners Healthcare System Institutional Review Board approved all study activities.
Intervention
We randomly assigned a discharge facilitator (DF), a master's level nurse practitioner with prior inpatient medicine experience, to 1 of the 5 resident medical teams. She had no prior experience on this specific floor. A similar resident team, on a different floor, served as the control. For the intervention team, the DF attended daily resident work rounds and interdisciplinary discharge rounds. The resident and DF collaborated in identifying patients being discharged in the next 1 to 3 days, and the DF scheduled all follow‐up appointments and tests. The DF performed medication reconciliation, wrote prescriptions and faxed them to pharmacies, and arranged all anticoagulation services. In collaboration with the resident, the DF called PCPs' offices with discharge information and faxed discharge summaries to PCPs' offices outside the Partners Healthcare System. The DF wrote part or all of the computer discharge orders and discharge summaries at the request of the resident and interns. All discharge summaries still needed to be reviewed, edited, and signed by the resident or interns. The DF also noted pending tests and studies at time of discharge, and followed up on these tests for the team. The DF met with all patients to answer any questions about their discharge plan, medications, and appointments; while residents are encouraged to do this, it is not done as consistently. She provided her business card for any questions after their discharge. Follow‐up patient calls to the DF were either answered by her or triaged to the appropriate person. The DF also communicated with the patient's nurse about the discharge plans. For all patients discharged over a weekend, the DF would arrange the follow‐up appointments on Mondays and call the patients at home.
For both teams, residents received letters at the start of their rotation notifying them of the study and asking them to complete discharge summaries within 24 hours. All residents in the program were expected to do an online discharge tutorial and attend a didactic lecture on discharge summaries. The residents on the intervention team received a 5‐minute orientation on how best to work with the DF. Residents were given the autonomy to decide how much to use the DF's services. The scheduling of follow‐up appointments on the control team was the responsibility of the team resident as per usual care. The nursing component of the discharge process, including patient discharge education, was the same on both teams. Nurses on both floors are identically trained on these aspects of care. The nurses on both teams were surveyed about perception of the discharge process prior to the intervention and after the intervention. A research assistant (RA) called patients discharged home on both teams, 1 week after discharge, to ask about satisfaction with the discharge process, to determine if the patients had any questions, and to verify patient knowledge regarding whom they should contact for problems. The RA also noted the end time of attending rounds each day and the start time of resident sign‐out.
Outcome Measures and Follow‐Up
At the time of discharge, the RA collected baseline data on all patients discharged from both teams, including the number of follow‐up appointments scheduled. Patients were tracked through electronic medical records to see if and when they attended their follow‐up appointments, whether they changed the appointment, and whether patients returned to a hospital emergency department or were readmitted to MGH or an affiliated Partners hospital within 30 days. For patients outside the MGHPartners system, the research assistant contacted primary care physician offices to document follow‐up. The remaining patient data was obtained through the MGHPartners computerized information system.
The primary outcomes of the study were length of stay, time of discharge, number of emergency department visits, hospital readmissions, numbers of discharge summaries completed in 24 hours, time from discharge to discharge summary completion, and whether the discharge summary was completed before follow‐up. Secondary outcomes were number of follow‐up PCP appointments made at time of discharge, percentage of follow‐up appointments attended and time from discharge to attending a follow‐up appointment, patient phone survey results, and nursing perception of the discharge process, as well as the percentage of attending rounds that ended on time and the time of resident sign‐out.
Statistical Analyses
Patient characteristics were compared between intervention and control teams using 2‐sample t tests or Wilcoxon rank sum tests for continuous variables, and chi‐square tests for categorical variables. Hours to discharge summary completion and hospital length of stay were summarized using median and interquartiles (IQR), and compared between the 2 teams using Wilcoxon rank sum tests. Categorical outcomes were compared using chi‐square tests. Two‐sided P values 0.05 were considered statistically significant. SAS version 9.2 (SAS Institute Inc, Cary, NC) was used for all statistical analyses.
RESULTS
Study Sample
During the 5‐month intervention (November 12, 2008 to April 14, 2009), a combined total of 999 patients were admitted to the intervention and control general medical teams. We excluded 96 patients who were not discharged but transferred to another service or intensive care units, and 24 patients who died. We also excluded 7 patients who were discharged from both teams the first day of the study, because the DF was not involved with the patients' discharge planning. That left 872 patients discharged to either home, a facility, or having left against medical advice (AMA) included in the study: 440 patients on the intervention team and 432 patients on the control team (Figure 1). Baseline patient demographic and clinical characteristics were similar across both teams with only gender being significantly different (Table 1). The mean age was 63 years (range, 1896) and the mean comorbidity score was 2.3 (range, 012). Of note, about a quarter of patients were discharged to facilities, about half were Medicare recipients, and approximately 80% had a PCP. The DF participated in the discharge process for nearly all of the intervention patients; she reported contributing approximately 50% of the content to the discharge summaries.
| Characteristics | Intervention Team | Control Team |
|---|---|---|
| n = 440 | n = 432 | |
| ||
| Mean age (SD), year | 63 (18) | 63 (18) |
| Women, n (%)* | 181 (41) | 207 (48) |
| Race, n (%) | ||
| White non‐Hispanic | 267 (61) | 243 (56) |
| Black non‐Hispanic | 24 (5) | 33 (8) |
| Hispanic | 21 (5) | 17 (4) |
| Unknown/other | 128 (29) | 139 (32) |
| Health insurance, n (%) | ||
| Medicare | 213 (48) | 226 (52) |
| Medicaid | 85 (19) | 81 (19) |
| Private | 110 (25) | 91 (21) |
| Other | 32 (7) | 34 (8) |
| PCP on admission, n (%) | 370 (84) | 356 (82) |
| Discharge disposition, n (%) | ||
| AMA | 12 (3) | 14 (3) |
| Home | 305 (69) | 315 (73) |
| Facility | 123 (28) | 103 (24) |
| Mean comorbidity index score (SD) | 2.3 (2.4) | 2.3 (2.4) |
| Diagnoses | ||
| Congestive heart failure | 30 (6%) | 27 (5%) |
| COPD/asthma | 34 (7%) | 47 (9%) |
| Cardiovascular disease | 54 (11%) | 50 (8%) |
| Alcohol/substance abuse | 29 (6%) | 34 (7%) |
| Gastrointestinal bleeds/ulcers | 38 (8%) | 41 (8%) |
| Hepatobiliary disease | 30 (6%) | 36 (7%) |
| Renal failure/kidney disease | 33 (7%) | 37 (7%) |
| Pneumonia | 36 (7%) | 22 (4%) |
| Musculoskeletal disease | 26 (5%) | 23 (5%) |
| Neurologic disease | 22 (4%) | 25 (5%) |
| Other | 163 (33%) | 172 (35%) |
Primary Outcomes
Primary outcomes from the 2 medical teams are listed in Table 2. In the intervention group, significantly more discharge summaries were completed within 24 hours compared to the control group (293 [67%] vs 207 [48%]; P < 0.0001). Since nearly all patients discharged to facilities must have a discharge summary at the time of discharge, the overall difference in completion rates came mainly from patients discharged home or having left AMA from the intervention team (177 [56%] vs 112 [34%]; P < 0.0001). For all discharge summaries, the median time to completion on the intervention team was 18.9 hours compared with 73.1 hours on the control team (P < 0.0001). More discharge summaries were completed before the first follow‐up appointment on the intervention team (393 [89%] vs 330 [76%]; P < 0.001). The DF intervention had no effect on 30‐day readmission or emergency department visits. For patients on the DF team, 88 (20%) were readmitted within 30 days of discharge, as compared with 79 (18%) on the control team (P = 0.55). Similarly, 40 (9%) of the intervention team patients, as compared with 39 (9%) of the control team patients, visited the emergency department at least once within 30 days (P = 1.0). There was no difference in length of stay (LOS) between the 2 teams (median 4.0 days for both teams, P = 0.84).
| Intervention Team | Control Team | ||
|---|---|---|---|
| Variables | n = 440 | n = 432 | P Value |
| |||
| Discharge summaries completed 24 hr, n (%) | 293 (67) | 207 (48) | <0.0001 |
| Discharges to facilities | 116 (94) | 95 (92) | 0.60 |
| Discharges to home/AMA | 177 (56) | 112 (34) | <0.0001 |
| Median hours to discharge summary completion for discharges to home/AMA (IQR) | 18.9 (0138) | 73.1 (4.3286) | <0.0001 |
| Discharge summary complete before time of follow‐up appointment. | 393 (89) | 330 (76) | <0.0001 |
| Emergency department visits in 30 days, n (%) | 40 (9) | 39 (9) | 1.0 |
| Readmissions in 30 days, n (%) | 88 (20) | 79 (18) | 0.55 |
| Median length of stay, days (IQR) | 4.0 (37) | 4.0 (28) | 0.84 |
| Discharges to facilities | 6.0 (511) | 8.0 (513) | 0.17 |
| Discharges to home/AMA | 4.0 (26) | 3.0 (26) | 0.61 |
| Discharged by noon, n (%) | 38 (9) | 42 (10) | 0.64 |
Secondary Outcomes
Table 3 shows secondary outcomes from the 2 medical teams. Among the patients discharged from the DF team, 264 (62%) had scheduled follow‐up appointments with PCPs compared to the control team 151 (36%) (P < 0.0001). (Many patients going to rehabilitation hospitals are not given PCP appointments at the time of discharge.) Despite having more scheduled appointments, patients' actual follow‐up with PCPs was similar during the 5‐month study period among both intervention and control group (234 [65%] vs 223 [63%]; P = 0.58). However, there was earlier follow‐up with the primary provider in the first 2 or 4 weeks in the intervention group. At 2 weeks, 129 (36%) patients in the intervention group saw their provider compared to 81 (23%) patients in the control group (P < 0.0002), and at 4 weeks, 159 (44%) of the intervention group was seen compared to 99 (28%) of the control group (P < 0.0001). Of note, among the 415 patients on both teams discharged with scheduled appointments, only 53 (13%) of patients did not show up for the scheduled appointment and this no‐show rate was the same on both teams.
| Variables | Intervention Team | Control Team | P Value |
|---|---|---|---|
| |||
| No. of eligible patients* | 428 | 418 | |
| Patients with follow‐up appointments to primary providers, n (%) | 264 (62) | 151 (36) | <0.0001 |
| No. of eligible patients | 359 | 354 | |
| Attended follow‐up appointment with primary provider during study, n (%) | 234 (65) | 223 (63) | 0.58 |
| Within 2 weeks of discharge | 129 (36) | 81 (23) | 0.0002 |
| Within 4 weeks of discharge | 159 (44) | 99 (28) | <0.0001 |
| No. of days round times were recorded | 100 | 99 | |
| No. of attending rounds ending by 12 PM | 45 (45%) | 31 (31%) | 0.058 |
| Mean start time of sign‐out rounds | 16:38 | 17:24 | 0.0007 |
Attending rounds ended on time (12 PM) 45% of the time in the intervention group compared to 31% in the control group (P = 0.058). Mean start time of resident sign‐out rounds was 1638 hours on the intervention team and 1724 hours on the control team (P = 0.0007).
We obtained patient reported outcome data by telephone within 2 to 4 weeks of discharge. Of the 620 patients discharged to home, 6 died or were readmitted to the hospital before being reached by phone. For the remaining 614 patients, we were able to contact 444 (72%). Of those, 321 (52%) agreed to participate in the phone interview. We surveyed similar proportions of intervention and control group patients (158 [52%] vs 163 [52%]) (Table 4). Both groups reported similar rates of having questions about their hospital stay after discharge (43 [27%] vs 49 [30%]; P = 0.62). The intervention group could better identify whom to call with questions (150 [95%] vs 138 [85%]; P = 0.003). The intervention group reported better understanding of their follow‐up plans (157 [99%] vs 141 [87%]; P = 0.001) and better understanding of their discharge medications (152 [96%] vs 142 [87%]; P = 0.001). More patients in the intervention group were satisfied with the discharge process (153 [97%] vs 124 [76%]; P < 0.0001).
| Intervention Team | Control Team | P Value | |
|---|---|---|---|
| |||
| Patients discharged home* | 304 | 310 | |
| Patients contacted by phone after discharge, n (%) | 213 (70) | 231 (75) | 0.24 |
| Agreed to participate in phone interview, n (%) | 158 (52) | 163 (53) | 0.94 |
| Among those agreed to participate, n (%) | |||
| Did you have questions about your hospital stay? | 43 (27) | 49 (30) | 0.62 |
| Would you know who to call if you had questions after discharge? | 150 (95) | 138 (85) | 0.003 |
| Satisfied with the discharge process? | 153 (97) | 124 (76) | <0.0001 |
| Did you understand your follow‐up plans? | 157 (99) | 141 (87) | <0.0001 |
| Did you understand your medications? | 152 (96) | 142 (87) | 0.001 |
| Did you feel safe going home? | 153 (97) | 151 (92) | 0.07 |
Compared with nurses on the control team, nurses on the intervention team more often reported paperwork being completed in a timely fashion (56% vs 29%; P = 0.041) and being less worried about the discharge plan (44% vs 57%; P = 0.027). The intervention team nurses also reported fewer issues with medications/prescriptions (61% vs 82%) and being included more often in the discharge planning (50% vs 38%). However, neither of these results reached statistical significance (P = 0.81 and 0.50, respectively).
DISCUSSION
Our study embedded a nurse practitioner on a busy resident general medical team to help with all aspects of the discharge process for which physicians are responsible. Previous studies have been limited to patients with specific diagnoses, age, or disposition plans.1825 In this study, we included all general medical patients. Our intervention improved several important quality of care elements: the timeliness of completion of discharge summaries; and increased number of early follow‐up appointments, with more patients seen within 2 and 4 weeks after discharge. Patients reported better understanding of their follow‐up plans and more satisfaction with the discharge process. While not statistically significant, there was a trend towards better communication with nurses. For residents with work‐hour limitations, there was time savings with a trend towards finishing attending rounds on time and statistically significant earlier sign‐out rounds (46 minutes earlier). This intervention had no effect on patient length of stay, readmissions, or emergency department visits in the 30 days after discharge.
Despite improving many aspects of the discharge process and communication that have previously been raised as areas of concern for patient safety, there was no improvement in readmissions rates and ED utilization which are often used as the quality indicators for effective discharge planning. Similar types of interventions on general medical patients have generally also failed to show improvement in readmission rates.1820, 25 Weinberger et al. arranged follow‐up appointments within 1 week for patients discharged from a Veterans Administrative hospital; while patients were seen more often, the intervention actually increased readmission rates.20 Fitzgerald et al. had a case manager contact patients at home and encourage follow‐up, which increased follow‐up visits, but again had no effect on readmission.19 Einstadter et al. had a nurse case manager coordinate outpatient follow‐up on a resident team and also did not effect readmission rates or ED visits.18 Jack et al. in project reengineered discharge (RED) did show a significant reduction in combined hospital utilization measures. However, their study focused on a more limited patient population, and employed both a discharge advocate to arrange follow‐up and improve patient education, and a pharmacist to make postdischarge phone calls.25
So why did readmissions rates and ED visits not change in our study? It would be reasonable to think that having earlier follow‐up appointments, better and timely physician‐to‐physician communication, and a facilitator for patient questions should improve the quality of the discharge process. In a recent study, Jha et al. found there was no association between chart‐based measures of discharge quality and readmissions rates, and only a modest association for patient‐reported measures of discharge quality and readmission rates.28 The authors suggest readmission rates are driven by many factors beyond just improved discharge safety. Perhaps readmission rates are too complex a measure to use to assess discharge process improvement. For fiscal reasons, it is understandable that hospitals, insurance companies, and the Centers for Medicare and Medicaid want to reduce readmission rates and ED utilization. Jencks et al. noted the cost of readmissions in 2004 was 17.4 billion dollars.29 However, sweeping efforts to improve the discharge process for all general medical patients may not yield significant reductions in readmissions, as this study suggests. We may need to focus aggressive intervention on smaller target populations, as prior studies on focused groups suggest.2123
There are no evidence‐based studies to suggest when optimal follow‐up should occur after discharge.26 Several medical society guidelines recommend 2 weeks. More patients on the intervention team were seen within 2 weeks, but readmission rates were not affected. The University Health System Consortium recently reported that the majority of readmissions occurred within 6 days, with the average being about 2 to 3 days.30 In this study, the median days to readmit were 12 for the intervention team and 10 for the control. It is possible that even with our improved 2‐week follow‐up, this was not early enough to reduce readmissions. Follow‐up may need to be within 13 days of discharge for highly vulnerable patients, to significantly change readmission rates. Further studies focusing on this question would be helpful.
Finally, with ACGME limitation of work hours, many residency programs are looking for ways to reduce residents' workload and increase time for education. With a significant trend towards finishing attending rounds on time, it is likely that more residents on the intervention team were able to attend the noon‐time educational conferences. We speculate that this was due to fewer interruptions during rounds because the DF was available for nurses' questions. Sign‐out rounds occurred significantly earlier, possibly because of improved resident efficiency due to the DF's help with the discharge process. While residents may lose some educational experience from not performing all discharge tasks, they gain experience working in interdisciplinary teams, have increased time for education, and reduced work hours. Since the ACGME limits the number of residents per program and increasing the residency size is not an option, a DF should be considered as a possible solution to ACGME work‐hour restrictions.
This study had several limitations. First, the intervention team had 1 specific person embedded, and therefore the results of this study may have limited generalizability. Second, the limited number of residents working with the DF could have biased the intervention, as not all residents worked equally well with the DF. However, this may represent the real‐world experience on any teaching service, given variation in working styles and learning curves of residents over their training. Third, this study was done at 1 university‐affiliated urban Academic Medical Center, making it potentially less generalizable to resident teams in community hospitals. Fourth, we were not able to capture readmissions and ED visits at institutions outside the MGHPartners Healthcare System. However, given that patients were assigned at random to either team, this factor should have impacted both teams equally. Fifth, the study occurred during Massachusetts healthcare reform which requires everyone to have health insurance. This may have affected the rates of ED visits and readmission rates, especially with a shortage of primary care physicians and office visits. Finally, this intervention was not cost‐neutral. Paying for a nurse practitioner to help residents with the work of discharge and providing patients with additional services had many advantages, but this quality improvement project did not pay for itself through shorter LOS, or decreases in ED visits or readmissions.
While readmission rates and ED utilization are important patient outcomes, especially in the current healthcare climate, what determines readmissions and ED visits is likely complex and multifactorial. This study suggests that, in the nationwide effort to reduce readmissions, solely improving the discharge process for all general medical patients may not produce the hoped‐for financial savings. Improving the discharge process, however, is something valuable in its own right. Adding a DF to a resident team does improve some quality markers of the discharge process and decreases work hours for residents.
Acknowledgements
Sara Macchiano, RN for her help with the data gathering of this study.
Recent studies have shown that a patient's discharge from the hospital is a vulnerable period for patient safety.14 With the reduction in length of stay (LOS) and the increase in patient acuity over the past decade, patients are discharged from acute care settings quicker and sicker, resulting in management of ongoing illness in a less‐monitored environment.5, 6 In addition, in teaching hospitals, residents are supervised by hospital‐based physicians who are rarely the primary care physician (PCP) for the residents' patients, which creates discontinuity of care.
One in 5 medical discharges is complicated by an adverse event believed, in part, to be due to poor communication between caregivers during this transition time.2 Discharge summaries, a key form of that communication, are not always done in a timely fashion and may lack key pieces of information.7, 8 For approximately 68% of patient discharges, the PCP will not have a discharge summary available for the patient's first follow‐up visit.911 In a survey of PCPs whose patients were in the hospital, only 23% reported direct communication with the hospital care team.12 This leaves PCPs unaware of pending test results or recommended follow‐up evaluations.10, 11, 13, 14 All of these factors are believed to contribute to adverse events, emergency department (ED) visits, and readmissions.
A recently published consensus statement on transitions of care by 6 major medical societies emphasizes the need for timely communication and transfer of information.15 These important processes are especially challenging to meet at academic medical centers, where discharge summaries and transition communication are done by residents in a hectic and challenging work environment, with multiple simultaneous and competing demands including outpatient clinic and required conferences.12 Residents have little formal training in how to write an effective discharge summary or how to systematically approach discharge planning. One study found higher error rates in discharge summaries written by residents compared with attending physicians.16 While the Accreditation Council for Graduate Medical Education (ACGME) limits the number of admissions per intern for both patient safety and educational reasons, the number of discharges per day is not limited despite the considerable amount of time required for appropriate discharge planning and communication.
Many interventions have been tried to improve the discharge process and reduce patient adverse events.17 Arranging early follow‐up appointments to reduce emergency department visits and readmissions has shown mixed results.13, 1820 Interventions that focus on specific populations, such as the elderly or patients with congestive heart failure, have been more successful.2123 Some interventions employed additional resources, such as a discharge form, transition coach, or discharge advocate, again with varying impact on results.18, 2427 A recent study by Jack et al. used nurse discharge advocates (DAs) to help with discharge planning and communication at an academic medical center.25 These DAs were independent of the care team, and focused on patient education and follow‐up plans, and reduced hospital reutilization in a selected population.
No studies have assessed the potential benefit of helping residents with the physician components of the discharge process. Prior studies have mainly focused on patient communication and follow‐up appointments, yet safe transitions also involve timely discharge summaries, physician‐to‐physician communication, physician‐to‐nurse communication, and medication reconciliation. Without support and time, these tasks can be very challenging for resident physicians with work‐hour limitations. We undertook a randomized, controlled trial to evaluate the impact on the discharge process of embedding a discharge facilitator in a resident medical team to help with the physician discharge process. We studied the effect for all the patients discharged from the resident team, rather than focusing on a select group or patients with a single diagnosis.
METHODS
Study Setting and Participants
This study was conducted on 2 of the 5 resident general medical teams on the inpatient teaching service at Massachusetts General Hospital (MGH), Boston, Massachusettsa large, 907‐bed, urban hospital. The residents' teams are regionalized and each care for approximately 20 patients on a single floor. Each of the study teams consists of a junior resident, 4 interns, and 1 to 2 attendings who rotate on the floor for 2‐week or 4‐week blocks. Attending rounds, which occur 10 AM to 12 PM weekdays, are for new patient presentations and discussion of plans. Interdisciplinary rounds occur 9:30 AM to 10 AM. Sign‐out rounds occur in the afternoon whenever all work is complete. The junior resident is responsible for all the discharge orders and communication with PCPs, and the discharge summaries for patients going to facilities. The interns are responsible for discharge summaries for patients discharged home; these summaries are not mandatory at the time of discharge. The majority of patients were admitted under the team attending(s). Patients were assigned to the teams by the admitting office, based on bed availability. All patients discharged from both resident medical teams over a 5‐month period were included in this study. Those who were not discharged from the hospital by the study teams (ie, transfers to intensive care units or deaths) were excluded. These exclusions accounted for less than 12% of all team patients. Partners Healthcare System Institutional Review Board approved all study activities.
Intervention
We randomly assigned a discharge facilitator (DF), a master's level nurse practitioner with prior inpatient medicine experience, to 1 of the 5 resident medical teams. She had no prior experience on this specific floor. A similar resident team, on a different floor, served as the control. For the intervention team, the DF attended daily resident work rounds and interdisciplinary discharge rounds. The resident and DF collaborated in identifying patients being discharged in the next 1 to 3 days, and the DF scheduled all follow‐up appointments and tests. The DF performed medication reconciliation, wrote prescriptions and faxed them to pharmacies, and arranged all anticoagulation services. In collaboration with the resident, the DF called PCPs' offices with discharge information and faxed discharge summaries to PCPs' offices outside the Partners Healthcare System. The DF wrote part or all of the computer discharge orders and discharge summaries at the request of the resident and interns. All discharge summaries still needed to be reviewed, edited, and signed by the resident or interns. The DF also noted pending tests and studies at time of discharge, and followed up on these tests for the team. The DF met with all patients to answer any questions about their discharge plan, medications, and appointments; while residents are encouraged to do this, it is not done as consistently. She provided her business card for any questions after their discharge. Follow‐up patient calls to the DF were either answered by her or triaged to the appropriate person. The DF also communicated with the patient's nurse about the discharge plans. For all patients discharged over a weekend, the DF would arrange the follow‐up appointments on Mondays and call the patients at home.
For both teams, residents received letters at the start of their rotation notifying them of the study and asking them to complete discharge summaries within 24 hours. All residents in the program were expected to do an online discharge tutorial and attend a didactic lecture on discharge summaries. The residents on the intervention team received a 5‐minute orientation on how best to work with the DF. Residents were given the autonomy to decide how much to use the DF's services. The scheduling of follow‐up appointments on the control team was the responsibility of the team resident as per usual care. The nursing component of the discharge process, including patient discharge education, was the same on both teams. Nurses on both floors are identically trained on these aspects of care. The nurses on both teams were surveyed about perception of the discharge process prior to the intervention and after the intervention. A research assistant (RA) called patients discharged home on both teams, 1 week after discharge, to ask about satisfaction with the discharge process, to determine if the patients had any questions, and to verify patient knowledge regarding whom they should contact for problems. The RA also noted the end time of attending rounds each day and the start time of resident sign‐out.
Outcome Measures and Follow‐Up
At the time of discharge, the RA collected baseline data on all patients discharged from both teams, including the number of follow‐up appointments scheduled. Patients were tracked through electronic medical records to see if and when they attended their follow‐up appointments, whether they changed the appointment, and whether patients returned to a hospital emergency department or were readmitted to MGH or an affiliated Partners hospital within 30 days. For patients outside the MGHPartners system, the research assistant contacted primary care physician offices to document follow‐up. The remaining patient data was obtained through the MGHPartners computerized information system.
The primary outcomes of the study were length of stay, time of discharge, number of emergency department visits, hospital readmissions, numbers of discharge summaries completed in 24 hours, time from discharge to discharge summary completion, and whether the discharge summary was completed before follow‐up. Secondary outcomes were number of follow‐up PCP appointments made at time of discharge, percentage of follow‐up appointments attended and time from discharge to attending a follow‐up appointment, patient phone survey results, and nursing perception of the discharge process, as well as the percentage of attending rounds that ended on time and the time of resident sign‐out.
Statistical Analyses
Patient characteristics were compared between intervention and control teams using 2‐sample t tests or Wilcoxon rank sum tests for continuous variables, and chi‐square tests for categorical variables. Hours to discharge summary completion and hospital length of stay were summarized using median and interquartiles (IQR), and compared between the 2 teams using Wilcoxon rank sum tests. Categorical outcomes were compared using chi‐square tests. Two‐sided P values 0.05 were considered statistically significant. SAS version 9.2 (SAS Institute Inc, Cary, NC) was used for all statistical analyses.
RESULTS
Study Sample
During the 5‐month intervention (November 12, 2008 to April 14, 2009), a combined total of 999 patients were admitted to the intervention and control general medical teams. We excluded 96 patients who were not discharged but transferred to another service or intensive care units, and 24 patients who died. We also excluded 7 patients who were discharged from both teams the first day of the study, because the DF was not involved with the patients' discharge planning. That left 872 patients discharged to either home, a facility, or having left against medical advice (AMA) included in the study: 440 patients on the intervention team and 432 patients on the control team (Figure 1). Baseline patient demographic and clinical characteristics were similar across both teams with only gender being significantly different (Table 1). The mean age was 63 years (range, 1896) and the mean comorbidity score was 2.3 (range, 012). Of note, about a quarter of patients were discharged to facilities, about half were Medicare recipients, and approximately 80% had a PCP. The DF participated in the discharge process for nearly all of the intervention patients; she reported contributing approximately 50% of the content to the discharge summaries.
| Characteristics | Intervention Team | Control Team |
|---|---|---|
| n = 440 | n = 432 | |
| ||
| Mean age (SD), year | 63 (18) | 63 (18) |
| Women, n (%)* | 181 (41) | 207 (48) |
| Race, n (%) | ||
| White non‐Hispanic | 267 (61) | 243 (56) |
| Black non‐Hispanic | 24 (5) | 33 (8) |
| Hispanic | 21 (5) | 17 (4) |
| Unknown/other | 128 (29) | 139 (32) |
| Health insurance, n (%) | ||
| Medicare | 213 (48) | 226 (52) |
| Medicaid | 85 (19) | 81 (19) |
| Private | 110 (25) | 91 (21) |
| Other | 32 (7) | 34 (8) |
| PCP on admission, n (%) | 370 (84) | 356 (82) |
| Discharge disposition, n (%) | ||
| AMA | 12 (3) | 14 (3) |
| Home | 305 (69) | 315 (73) |
| Facility | 123 (28) | 103 (24) |
| Mean comorbidity index score (SD) | 2.3 (2.4) | 2.3 (2.4) |
| Diagnoses | ||
| Congestive heart failure | 30 (6%) | 27 (5%) |
| COPD/asthma | 34 (7%) | 47 (9%) |
| Cardiovascular disease | 54 (11%) | 50 (8%) |
| Alcohol/substance abuse | 29 (6%) | 34 (7%) |
| Gastrointestinal bleeds/ulcers | 38 (8%) | 41 (8%) |
| Hepatobiliary disease | 30 (6%) | 36 (7%) |
| Renal failure/kidney disease | 33 (7%) | 37 (7%) |
| Pneumonia | 36 (7%) | 22 (4%) |
| Musculoskeletal disease | 26 (5%) | 23 (5%) |
| Neurologic disease | 22 (4%) | 25 (5%) |
| Other | 163 (33%) | 172 (35%) |
Primary Outcomes
Primary outcomes from the 2 medical teams are listed in Table 2. In the intervention group, significantly more discharge summaries were completed within 24 hours compared to the control group (293 [67%] vs 207 [48%]; P < 0.0001). Since nearly all patients discharged to facilities must have a discharge summary at the time of discharge, the overall difference in completion rates came mainly from patients discharged home or having left AMA from the intervention team (177 [56%] vs 112 [34%]; P < 0.0001). For all discharge summaries, the median time to completion on the intervention team was 18.9 hours compared with 73.1 hours on the control team (P < 0.0001). More discharge summaries were completed before the first follow‐up appointment on the intervention team (393 [89%] vs 330 [76%]; P < 0.001). The DF intervention had no effect on 30‐day readmission or emergency department visits. For patients on the DF team, 88 (20%) were readmitted within 30 days of discharge, as compared with 79 (18%) on the control team (P = 0.55). Similarly, 40 (9%) of the intervention team patients, as compared with 39 (9%) of the control team patients, visited the emergency department at least once within 30 days (P = 1.0). There was no difference in length of stay (LOS) between the 2 teams (median 4.0 days for both teams, P = 0.84).
| Intervention Team | Control Team | ||
|---|---|---|---|
| Variables | n = 440 | n = 432 | P Value |
| |||
| Discharge summaries completed 24 hr, n (%) | 293 (67) | 207 (48) | <0.0001 |
| Discharges to facilities | 116 (94) | 95 (92) | 0.60 |
| Discharges to home/AMA | 177 (56) | 112 (34) | <0.0001 |
| Median hours to discharge summary completion for discharges to home/AMA (IQR) | 18.9 (0138) | 73.1 (4.3286) | <0.0001 |
| Discharge summary complete before time of follow‐up appointment. | 393 (89) | 330 (76) | <0.0001 |
| Emergency department visits in 30 days, n (%) | 40 (9) | 39 (9) | 1.0 |
| Readmissions in 30 days, n (%) | 88 (20) | 79 (18) | 0.55 |
| Median length of stay, days (IQR) | 4.0 (37) | 4.0 (28) | 0.84 |
| Discharges to facilities | 6.0 (511) | 8.0 (513) | 0.17 |
| Discharges to home/AMA | 4.0 (26) | 3.0 (26) | 0.61 |
| Discharged by noon, n (%) | 38 (9) | 42 (10) | 0.64 |
Secondary Outcomes
Table 3 shows secondary outcomes from the 2 medical teams. Among the patients discharged from the DF team, 264 (62%) had scheduled follow‐up appointments with PCPs compared to the control team 151 (36%) (P < 0.0001). (Many patients going to rehabilitation hospitals are not given PCP appointments at the time of discharge.) Despite having more scheduled appointments, patients' actual follow‐up with PCPs was similar during the 5‐month study period among both intervention and control group (234 [65%] vs 223 [63%]; P = 0.58). However, there was earlier follow‐up with the primary provider in the first 2 or 4 weeks in the intervention group. At 2 weeks, 129 (36%) patients in the intervention group saw their provider compared to 81 (23%) patients in the control group (P < 0.0002), and at 4 weeks, 159 (44%) of the intervention group was seen compared to 99 (28%) of the control group (P < 0.0001). Of note, among the 415 patients on both teams discharged with scheduled appointments, only 53 (13%) of patients did not show up for the scheduled appointment and this no‐show rate was the same on both teams.
| Variables | Intervention Team | Control Team | P Value |
|---|---|---|---|
| |||
| No. of eligible patients* | 428 | 418 | |
| Patients with follow‐up appointments to primary providers, n (%) | 264 (62) | 151 (36) | <0.0001 |
| No. of eligible patients | 359 | 354 | |
| Attended follow‐up appointment with primary provider during study, n (%) | 234 (65) | 223 (63) | 0.58 |
| Within 2 weeks of discharge | 129 (36) | 81 (23) | 0.0002 |
| Within 4 weeks of discharge | 159 (44) | 99 (28) | <0.0001 |
| No. of days round times were recorded | 100 | 99 | |
| No. of attending rounds ending by 12 PM | 45 (45%) | 31 (31%) | 0.058 |
| Mean start time of sign‐out rounds | 16:38 | 17:24 | 0.0007 |
Attending rounds ended on time (12 PM) 45% of the time in the intervention group compared to 31% in the control group (P = 0.058). Mean start time of resident sign‐out rounds was 1638 hours on the intervention team and 1724 hours on the control team (P = 0.0007).
We obtained patient reported outcome data by telephone within 2 to 4 weeks of discharge. Of the 620 patients discharged to home, 6 died or were readmitted to the hospital before being reached by phone. For the remaining 614 patients, we were able to contact 444 (72%). Of those, 321 (52%) agreed to participate in the phone interview. We surveyed similar proportions of intervention and control group patients (158 [52%] vs 163 [52%]) (Table 4). Both groups reported similar rates of having questions about their hospital stay after discharge (43 [27%] vs 49 [30%]; P = 0.62). The intervention group could better identify whom to call with questions (150 [95%] vs 138 [85%]; P = 0.003). The intervention group reported better understanding of their follow‐up plans (157 [99%] vs 141 [87%]; P = 0.001) and better understanding of their discharge medications (152 [96%] vs 142 [87%]; P = 0.001). More patients in the intervention group were satisfied with the discharge process (153 [97%] vs 124 [76%]; P < 0.0001).
| Intervention Team | Control Team | P Value | |
|---|---|---|---|
| |||
| Patients discharged home* | 304 | 310 | |
| Patients contacted by phone after discharge, n (%) | 213 (70) | 231 (75) | 0.24 |
| Agreed to participate in phone interview, n (%) | 158 (52) | 163 (53) | 0.94 |
| Among those agreed to participate, n (%) | |||
| Did you have questions about your hospital stay? | 43 (27) | 49 (30) | 0.62 |
| Would you know who to call if you had questions after discharge? | 150 (95) | 138 (85) | 0.003 |
| Satisfied with the discharge process? | 153 (97) | 124 (76) | <0.0001 |
| Did you understand your follow‐up plans? | 157 (99) | 141 (87) | <0.0001 |
| Did you understand your medications? | 152 (96) | 142 (87) | 0.001 |
| Did you feel safe going home? | 153 (97) | 151 (92) | 0.07 |
Compared with nurses on the control team, nurses on the intervention team more often reported paperwork being completed in a timely fashion (56% vs 29%; P = 0.041) and being less worried about the discharge plan (44% vs 57%; P = 0.027). The intervention team nurses also reported fewer issues with medications/prescriptions (61% vs 82%) and being included more often in the discharge planning (50% vs 38%). However, neither of these results reached statistical significance (P = 0.81 and 0.50, respectively).
DISCUSSION
Our study embedded a nurse practitioner on a busy resident general medical team to help with all aspects of the discharge process for which physicians are responsible. Previous studies have been limited to patients with specific diagnoses, age, or disposition plans.1825 In this study, we included all general medical patients. Our intervention improved several important quality of care elements: the timeliness of completion of discharge summaries; and increased number of early follow‐up appointments, with more patients seen within 2 and 4 weeks after discharge. Patients reported better understanding of their follow‐up plans and more satisfaction with the discharge process. While not statistically significant, there was a trend towards better communication with nurses. For residents with work‐hour limitations, there was time savings with a trend towards finishing attending rounds on time and statistically significant earlier sign‐out rounds (46 minutes earlier). This intervention had no effect on patient length of stay, readmissions, or emergency department visits in the 30 days after discharge.
Despite improving many aspects of the discharge process and communication that have previously been raised as areas of concern for patient safety, there was no improvement in readmissions rates and ED utilization which are often used as the quality indicators for effective discharge planning. Similar types of interventions on general medical patients have generally also failed to show improvement in readmission rates.1820, 25 Weinberger et al. arranged follow‐up appointments within 1 week for patients discharged from a Veterans Administrative hospital; while patients were seen more often, the intervention actually increased readmission rates.20 Fitzgerald et al. had a case manager contact patients at home and encourage follow‐up, which increased follow‐up visits, but again had no effect on readmission.19 Einstadter et al. had a nurse case manager coordinate outpatient follow‐up on a resident team and also did not effect readmission rates or ED visits.18 Jack et al. in project reengineered discharge (RED) did show a significant reduction in combined hospital utilization measures. However, their study focused on a more limited patient population, and employed both a discharge advocate to arrange follow‐up and improve patient education, and a pharmacist to make postdischarge phone calls.25
So why did readmissions rates and ED visits not change in our study? It would be reasonable to think that having earlier follow‐up appointments, better and timely physician‐to‐physician communication, and a facilitator for patient questions should improve the quality of the discharge process. In a recent study, Jha et al. found there was no association between chart‐based measures of discharge quality and readmissions rates, and only a modest association for patient‐reported measures of discharge quality and readmission rates.28 The authors suggest readmission rates are driven by many factors beyond just improved discharge safety. Perhaps readmission rates are too complex a measure to use to assess discharge process improvement. For fiscal reasons, it is understandable that hospitals, insurance companies, and the Centers for Medicare and Medicaid want to reduce readmission rates and ED utilization. Jencks et al. noted the cost of readmissions in 2004 was 17.4 billion dollars.29 However, sweeping efforts to improve the discharge process for all general medical patients may not yield significant reductions in readmissions, as this study suggests. We may need to focus aggressive intervention on smaller target populations, as prior studies on focused groups suggest.2123
There are no evidence‐based studies to suggest when optimal follow‐up should occur after discharge.26 Several medical society guidelines recommend 2 weeks. More patients on the intervention team were seen within 2 weeks, but readmission rates were not affected. The University Health System Consortium recently reported that the majority of readmissions occurred within 6 days, with the average being about 2 to 3 days.30 In this study, the median days to readmit were 12 for the intervention team and 10 for the control. It is possible that even with our improved 2‐week follow‐up, this was not early enough to reduce readmissions. Follow‐up may need to be within 13 days of discharge for highly vulnerable patients, to significantly change readmission rates. Further studies focusing on this question would be helpful.
Finally, with ACGME limitation of work hours, many residency programs are looking for ways to reduce residents' workload and increase time for education. With a significant trend towards finishing attending rounds on time, it is likely that more residents on the intervention team were able to attend the noon‐time educational conferences. We speculate that this was due to fewer interruptions during rounds because the DF was available for nurses' questions. Sign‐out rounds occurred significantly earlier, possibly because of improved resident efficiency due to the DF's help with the discharge process. While residents may lose some educational experience from not performing all discharge tasks, they gain experience working in interdisciplinary teams, have increased time for education, and reduced work hours. Since the ACGME limits the number of residents per program and increasing the residency size is not an option, a DF should be considered as a possible solution to ACGME work‐hour restrictions.
This study had several limitations. First, the intervention team had 1 specific person embedded, and therefore the results of this study may have limited generalizability. Second, the limited number of residents working with the DF could have biased the intervention, as not all residents worked equally well with the DF. However, this may represent the real‐world experience on any teaching service, given variation in working styles and learning curves of residents over their training. Third, this study was done at 1 university‐affiliated urban Academic Medical Center, making it potentially less generalizable to resident teams in community hospitals. Fourth, we were not able to capture readmissions and ED visits at institutions outside the MGHPartners Healthcare System. However, given that patients were assigned at random to either team, this factor should have impacted both teams equally. Fifth, the study occurred during Massachusetts healthcare reform which requires everyone to have health insurance. This may have affected the rates of ED visits and readmission rates, especially with a shortage of primary care physicians and office visits. Finally, this intervention was not cost‐neutral. Paying for a nurse practitioner to help residents with the work of discharge and providing patients with additional services had many advantages, but this quality improvement project did not pay for itself through shorter LOS, or decreases in ED visits or readmissions.
While readmission rates and ED utilization are important patient outcomes, especially in the current healthcare climate, what determines readmissions and ED visits is likely complex and multifactorial. This study suggests that, in the nationwide effort to reduce readmissions, solely improving the discharge process for all general medical patients may not produce the hoped‐for financial savings. Improving the discharge process, however, is something valuable in its own right. Adding a DF to a resident team does improve some quality markers of the discharge process and decreases work hours for residents.
Acknowledgements
Sara Macchiano, RN for her help with the data gathering of this study.
- ,,,,.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317–323.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161–167.
- ,,,.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18(8):646–651.
- ,,,.Posthospital medication discrepancies: Prevalence and contributing factors.Arch Intern Med.2005;165(16):1842–1847.
- ,,, et al.Prospective payment system and impairment at discharge. The ‘quicker‐and‐sicker’ story revisited.JAMA.1990;264(15):1980–1983.
- .The incidence of adverse medical outcomes under prospective payment.Econometrica. 1995;63:29–50.
- ,,.Content of a discharge summary from a medical ward: Views of general practitioners and hospital doctors.J R Coll Physicians Lond.1995;29(4):307–310.
- ,.Quality assessment of a discharge summary system.Can Med Assoc J.1995;152(9):1437–1442.
- ,,.Dissemination of discharge summaries. Not reaching follow‐up physicians.Can Fam Physician.2002;48:737–742.
- ,,,.Effect of discharge summary availability during post‐discharge visits on hospital readmission.J Gen Intern Med.2002;17(3):186–192.
- ,,,,,.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.Association of communication between hospital‐based physicians and primary care providers with patient outcomes.J Gen Intern Med.2009;24(3):381–386.
- ,,.Tying up loose ends: Discharging patients with unresolved medical issues.Arch Intern Med.2007;167(12):1305–1311.
- ,,, et al.Patient safety concerns arising from test results that return after hospital discharge.Ann Intern Med.2005;143(2):121–128.
- ,,, et al.Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine.J Hosp Med.2009;4(6):364–370.
- ,,,.Prospective audit of discharge summary errors.Br J Surg.1996;83(6):788–790.
- ,.Lost in transition: Challenges and opportunities for improving the quality of transitional care.Ann Intern Med.2004;141(7):533–536.
- ,,.Effect of a nurse case manager on postdischarge follow‐up.J Gen Intern Med.1996;11(11):684–688.
- ,,,,.A case manager intervention to reduce readmissions.Arch Intern Med.1994;154(15):1721–1729.
- ,,.Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.N Engl J Med.1996;334(22):1441–1447.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: A meta‐analysis.JAMA.2004;291(11):1358–1367.
- ,,,,,.Transitional care of older adults hospitalized with heart failure: A randomized, controlled trial.J Am Geriatr Soc.2004;52(5):675–684.
- ,,,,,.Preparing patients and caregivers to participate in care delivered across settings: The Care Transitions Intervention.J Am Geriatr Soc.2004;52(11):1817–1825.
- ,,,.The care transitions intervention: Results of a randomized controlled trial.Arch Intern Med.2006;166(17):1822–1828.
- ,,, et al.A reengineered hospital discharge program to decrease rehospitalization: A randomized trial.Ann Intern Med.2009;150(3):178–187.
- ,,,.Redefining and redesigning hospital discharge to enhance patient care: A randomized controlled study.J Gen Intern Med.2008;23(8):1228–1233.
- ,,, et al.Effect of a nurse team coordinator on outcomes for hospitalized medicine patients.Am J Med.2005;118(10):1148–1153.
- ,,.Public reporting of discharge planning and rates of readmissions.N Engl J Med.2009;361(27):2637–2645.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):1418–1428.
- Consortium UHS. Reducing Readmissions SC22009. Available at: https://www.uhc.edu/1244.htm
- ,,,,.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317–323.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161–167.
- ,,,.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18(8):646–651.
- ,,,.Posthospital medication discrepancies: Prevalence and contributing factors.Arch Intern Med.2005;165(16):1842–1847.
- ,,, et al.Prospective payment system and impairment at discharge. The ‘quicker‐and‐sicker’ story revisited.JAMA.1990;264(15):1980–1983.
- .The incidence of adverse medical outcomes under prospective payment.Econometrica. 1995;63:29–50.
- ,,.Content of a discharge summary from a medical ward: Views of general practitioners and hospital doctors.J R Coll Physicians Lond.1995;29(4):307–310.
- ,.Quality assessment of a discharge summary system.Can Med Assoc J.1995;152(9):1437–1442.
- ,,.Dissemination of discharge summaries. Not reaching follow‐up physicians.Can Fam Physician.2002;48:737–742.
- ,,,.Effect of discharge summary availability during post‐discharge visits on hospital readmission.J Gen Intern Med.2002;17(3):186–192.
- ,,,,,.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.Association of communication between hospital‐based physicians and primary care providers with patient outcomes.J Gen Intern Med.2009;24(3):381–386.
- ,,.Tying up loose ends: Discharging patients with unresolved medical issues.Arch Intern Med.2007;167(12):1305–1311.
- ,,, et al.Patient safety concerns arising from test results that return after hospital discharge.Ann Intern Med.2005;143(2):121–128.
- ,,, et al.Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine.J Hosp Med.2009;4(6):364–370.
- ,,,.Prospective audit of discharge summary errors.Br J Surg.1996;83(6):788–790.
- ,.Lost in transition: Challenges and opportunities for improving the quality of transitional care.Ann Intern Med.2004;141(7):533–536.
- ,,.Effect of a nurse case manager on postdischarge follow‐up.J Gen Intern Med.1996;11(11):684–688.
- ,,,,.A case manager intervention to reduce readmissions.Arch Intern Med.1994;154(15):1721–1729.
- ,,.Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.N Engl J Med.1996;334(22):1441–1447.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: A meta‐analysis.JAMA.2004;291(11):1358–1367.
- ,,,,,.Transitional care of older adults hospitalized with heart failure: A randomized, controlled trial.J Am Geriatr Soc.2004;52(5):675–684.
- ,,,,,.Preparing patients and caregivers to participate in care delivered across settings: The Care Transitions Intervention.J Am Geriatr Soc.2004;52(11):1817–1825.
- ,,,.The care transitions intervention: Results of a randomized controlled trial.Arch Intern Med.2006;166(17):1822–1828.
- ,,, et al.A reengineered hospital discharge program to decrease rehospitalization: A randomized trial.Ann Intern Med.2009;150(3):178–187.
- ,,,.Redefining and redesigning hospital discharge to enhance patient care: A randomized controlled study.J Gen Intern Med.2008;23(8):1228–1233.
- ,,, et al.Effect of a nurse team coordinator on outcomes for hospitalized medicine patients.Am J Med.2005;118(10):1148–1153.
- ,,.Public reporting of discharge planning and rates of readmissions.N Engl J Med.2009;361(27):2637–2645.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):1418–1428.
- Consortium UHS. Reducing Readmissions SC22009. Available at: https://www.uhc.edu/1244.htm
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