Faculty Bibliography

Programs from the Centers for Medicare and Medicaid Services simultaneously promote strategies to lower hospital admissions and readmissions. However, there is concern that hospitals in communities that successfully reduce admissions may be penalized, as patients that are ultimately hospitalized may be sicker and at higher risk of readmission. We therefore examined the relationship between changes from 2010 to 2013 in admission rates and thirty-day readmission rates for elderly Medicare beneficiaries. We found that communities with the greatest decline in admission rates also had the greatest decline in thirty-day readmission rates, even though hospitalized patients did grow sicker as admission rates declined. The relationship between changing admission and readmission rates persisted in models that measured observed readmission rates, risk-standardized readmission rates, and the combined rate of readmission and death. Our findings suggest that communities can reduce admission rates and readmission rates in parallel, and that federal policy incentivizing reductions in both outcomes does not create contradictory incentives.

Since its publication in 2008, SQUIRE (Standards for Quality Improvement Reporting Excellence) has contributed to the completeness and transparency of reporting of quality improvement work, providing guidance to authors and reviewers of reports on healthcare improvement work. In the interim, enormous growth has occurred in understanding factors that influence the success, and failure, of healthcare improvement efforts. Progress has been particularly strong in three areas: the understanding of the theoretical basis for improvement work; the impact of contextual factors on outcomes; and the development of methodologies for studying improvement work. Consequently, there is now a need to revise the original publication guidelines. To reflect the breadth of knowledge and experience in the field, we solicited input from a wide variety of authors, editors and improvement professionals during the guideline revision process. This Explanation and Elaboration document (E&E) is a companion to the revised SQUIRE guidelines, SQUIRE 2.0. The product of collaboration by an international and interprofessional group of authors, this document provides examples from the published literature, and an explanation of how each reflects the intent of a specific item in SQUIRE. The purpose of the guidelines is to assist authors in writing clearly, precisely and completely about systematic efforts to improve the quality, safety and value of healthcare services. Authors can explore the SQUIRE statement, this E&E and related documents in detail at http://www.squire-statement.org.

Importance: Feasibility, effectiveness, and sustainability of large-scale readmission reduction efforts are uncertain. The Greater New Haven Coalition for Safe Transitions and Readmission Reductions was funded by the Center for Medicare & Medicaid Services (CMS) to reduce readmissions among all discharged Medicare fee-for-service (FFS) patients. Objective: To evaluate whether overall Medicare FFS readmissions were reduced through an intervention applied to high-risk discharge patients. Design, Setting, and Participants: This quasi-experimental evaluation took place at an urban academic medical center. Target discharge patients were older than 64 years with Medicare FFS insurance, residing in nearby zip codes, and discharged alive to home or facility and not against medical advice or to hospice; control discharge patients were older than 54 years with the same zip codes and discharge disposition but without Medicare FFS insurance if older than 64 years. High-risk target discharge patients were selectively enrolled in the program. Interventions: Personalized transitional care, including education, medication reconciliation, follow-up telephone calls, and linkage to community resources. Measurements: We measured the 30-day unplanned same-hospital readmission rates in the baseline period (May 1, 2011, through April 30, 2012) and intervention period (October 1, 2012, through May 31, 2014). Results: We enrolled 10621 (58.3%) of 18223 target discharge patients (73.9% of discharge patients screened as high risk) and included all target discharge patients in the analysis. The mean (SD) age of the target discharge patients was 79.7 (8.8) years. The adjusted readmission rate decreased from 21.5% to 19.5% in the target population and from 21.1% to 21.0% in the control population, a relative reduction of 9.3%. The number needed to treat to avoid 1 readmission was 50. In a difference-in-differences analysis using a logistic regression model, the odds of readmission in the target population decreased significantly more than that of the control population in the intervention period (odds ratio, 0.90; 95% CI, 0.83-0.99; P = .03). In a comparative interrupted time series analysis of the difference in monthly adjusted admission rates, the target population decreased an absolute -3.09 (95% CI, -6.47 to 0.29; P = .07) relative to the control population, a similar but nonsignificant effect. Conclusions and Relevance: This large-scale readmission reduction program reduced readmissions by 9.3% among the full population targeted by the CMS despite being delivered only to high-risk patients. However, it did not achieve the goal reduction set by the CMS.

OBJECTIVE: Recalling a patient to repeat a radiology examination is an adverse and, in certain cases, preventable event. Our objectives were to assess the rate of patient recalls for all imaging performed in the outpatient setting at our institution and to characterize the underlying reasons for the recalls. MATERIALS AND METHODS: We performed a retrospective review of all repeat imaging requests for an inadequate initial imaging study between January 2012 and March 2015. RESULTS: We identified 100 recall requests (mean, 2.6 requests per month), for an overall recall rate of approximately 1 in 8046 ambulatory studies and 1 in 1684 MRI studies. Nearly all recalls (98%) involved adults. A total of 95% of the recalls were for MRI studies. The most common reason for a patient recall request was an incomplete examination, making up 24% of all requests. The other causes were inadequate coverage of the area of interest (22%), protocoling errors (20%), poor imaging quality (15%), additional imaging to clarify a finding (11%), insufficient contrast visualization (7%), and incorrect patient information (1%). CONCLUSION: We found that patient recalls for imaging in the outpatient setting at our institution are not common. When recalls did occur, they were most often related to the acquisition of MR images. Improved technologist education on MRI protocoling and enhanced communication between ordering clinicians and radiologists to clarify the purpose of imaging might reduce the need for repeat ambulatory imaging.

BACKGROUND: Hospital quality improvement efforts absorb substantial time and resources, but many innovations fail to integrate into organizational routines, undermining the potential to sustain the new practices. Despite a well-developed literature on the initial implementation of new practices, we have limited knowledge about the mechanisms by which integration occurs. METHODS: We conducted a qualitative study using a purposive sample of hospitals that participated in the State Action on Avoidable Rehospitalizations (STAAR) initiative, a collaborative to reduce hospital readmissions that encouraged members to adopt new practices. We selected hospitals where risk-standardized readmission rates (RSRR) had improved (n = 7) or deteriorated (n = 3) over the course of the first 2 years of the STAAR initiative (2010-2011 to 2011-2012) and interviewed a range of staff at each site (90 total). We recruited hospitals until reaching theoretical saturation. The constant comparative method was used to conduct coding and identification of key themes. RESULTS: When innovations were successfully integrated, participants consistently reported that a small number of key staff held the innovation in place for as long as a year while more permanent integrating mechanisms began to work. Depending on characteristics of the innovation, one of three categories of integrating mechanisms eventually took over the role of holding new practices in place. Innovations that proved intrinsically rewarding to the staff, by making their jobs easier or more gratifying, became integrated through shifts in attitudes and norms over time. Innovations for which the staff did not perceive benefits to themselves were integrated through revised performance standards if the innovation involved complex tasks and through automation if the innovation involved simple tasks. CONCLUSIONS: Hospitals have an opportunity to promote the integration of new practices by planning for the extended effort required to hold a new practice in place while integration mechanisms take hold. By understanding how integrating mechanisms correspond to innovation characteristics, hospitals may be able to foster integrating mechanisms most likely to work for particular innovations.

BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. Journal of Hospital Medicine 2015. (c) 2015 Society of Hospital Medicine.