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Variability in Implementation of Interventions Aimed at Reducing Readmissions Among Patients With Heart Failure: A Survey of Teaching Hospitals.

ABSTRACT: To highlight teaching hospitals' efforts to reduce readmissions by describing interventions implemented to improve care transitions for heart failure (HF) patients and the variability in implemented HF-specific and care transition interventions.In 2012, the authors surveyed a network of 17 teaching hospitals to capture information about the number, type, stage of implementation, and structure of 4 HF-specific and 21 care transition (predischarge, bridging, and postdischarge) interventions implemented to reduce readmissions among patients with HF. The authors summarized data using descriptive statistics, including the mean number of interventions implemented and the frequency and stage of specific interventions, and descriptive plots of the structure of two common interventions (multidisciplinary rounds and follow-up telephone calls).Sixteen hospitals (94%) responded. The number and stage of implementation of the HF-specific and care transition interventions implemented varied across institutions. The mean number of interventions at an advanced stage of implementation (i.e., implemented for ? 75% of HF patients on the cardiology service or on all services) was 10.9 (standard deviation = 4.3). Overall, predischarge interventions were more common than bridging or postdischarge interventions. There was variability in the personnel involved in multidisciplinary rounds and in the processes/content of follow-up telephone calls.Teaching hospitals have implemented a wide range of interventions aimed at reducing hospital readmissions, but there is substantial variability in the types, stages, and structure of their interventions. This heterogeneity highlights the need for collaborative efforts to improve understanding of intervention effectiveness.

SUBMITTER: Vasilevskis EE

PROVIDER: S-EPMC4811742 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Variability in Implementation of Interventions Aimed at Reducing Readmissions Among Patients With Heart Failure: A Survey of Teaching Hospitals.

Vasilevskis Eduard E EE Kripalani Sunil S Ong Michael K MK Rosenthal J Thomas JT Longnecker David E DE Harmon Brian B Hohmann Samuel F SF Wright Kelly K Black Jeanne T JT

Academic medicine : journal of the Association of American Medical Colleges 20160401 4

<h4>Purpose</h4>To highlight teaching hospitals' efforts to reduce readmissions by describing interventions implemented to improve care transitions for heart failure (HF) patients and the variability in implemented HF-specific and care transition interventions.<h4>Method</h4>In 2012, the authors surveyed a network of 17 teaching hospitals to capture information about the number, type, stage of implementation, and structure of 4 HF-specific and 21 care transition (predischarge, bridging, and post ...[more]

PMID: 26579793

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Project description:BackgroundReduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort.ObjectivesWe hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF.MethodsAfter establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF.ResultsThe HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001).ConclusionsIn an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.

Project description:BackgroundSeveral studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.MethodsWe construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.FindingsFor the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.ConclusionsClassifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

Project description:BACKGROUND: Many patients encounter a variety of problems after discharge from hospital and many discharge (planning and support) interventions have been developed and studied. These primary studies have already been synthesized in several literature reviews with conflicting conclusions. We therefore set out a systematic review of the reviews examining discharge interventions. The objective was to synthesize the evidence presented in literature on the effectiveness of interventions aimed to reduce post-discharge problems in adults discharged home from an acute general care hospital. METHODS: A comprehensive search of seventeen literature databases and twenty-five websites was performed for the period 1994-2004 to find relevant reviews. A three-stage inclusion process consisting of initial sifting, checking full-text papers on inclusion criteria, and methodological assessment, was performed independently by two reviewers. Data on effects were synthesized by use of narrative and tabular methods. RESULTS: Fifteen systematic reviews met our inclusion criteria. All reviews had to deal with considerable heterogeneity in interventions, populations and outcomes, making synthesizing and pooling difficult. Although a statistical significant effect was occasionally found, most review authors reached no firm conclusions that the discharge interventions they studied were effective. We found limited evidence that some interventions may improve knowledge of patients, may help in keeping patients at home or may reduce readmissions to hospital. Interventions that combine discharge planning and discharge support tend to lead to the greatest effects. There is little evidence that discharge interventions have an impact on length of stay, discharge destination or dependency at discharge. We found no evidence that discharge interventions have a positive impact on the physical status of patients after discharge, on health care use after discharge, or on costs. CONCLUSION: Based on fifteen high quality systematic reviews, there is some evidence that some interventions may have a positive impact, particularly those with educational components and those that combine pre-discharge and post-discharge interventions. However, on the whole there is only limited summarized evidence that discharge planning and discharge support interventions have a positive impact on patient status at hospital discharge, on patient functioning after discharge, on health care use after discharge, or on costs.

Project description:BackgroundIn the last decade, there is an increasing focus on detecting and compiling lists of low-value nursing procedures. However, less is known about effective de-implementation strategies for these procedures. Therefore, the aim of this systematic review was to summarize the evidence of effective strategies to de-implement low-value nursing procedures.MethodsPubMed, Embase, Emcare, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, and Google Scholar were searched till January 2020. Additionally, reference lists and citations of the included studies were searched. Studies were included that described de-implementation of low-value nursing procedures, i.e., procedures, test, or drug orders by nurses or nurse practitioners. PRISMA guideline was followed, and the 'Cochrane Effective Practice and Organisation of Care' (EPOC) taxonomy was used to categorize de-implementation strategies. A meta-analysis was performed for the volume of low-value nursing procedures in controlled studies, and Mantel-Haenszel risk ratios (95% CI) were calculated using a random effects model.ResultsTwenty-seven studies were included in this review. Studies used a (cluster) randomized design (n = 10), controlled before-after design (n = 5), and an uncontrolled before-after design (n = 12). Low-value nursing procedures performed by nurses and/or nurse specialists that were found in this study were restraint use (n = 20), inappropriate antibiotic prescribing (n = 3), indwelling or unnecessary urinary catheters use (n = 2), ordering unnecessary liver function tests (n = 1), and unnecessary antipsychotic prescribing (n = 1). Fourteen studies showed a significant reduction in low-value nursing procedures. Thirteen of these 14 studies included an educational component within their de-implementation strategy. Twelve controlled studies were included in the meta-analysis. Subgroup analyses for study design showed no statistically significant subgroup effect for the volume of low-value nursing procedures (p = 0.20).ConclusionsThe majority of the studies with a positive significant effect used a de-implementation strategy with an educational component. Unfortunately, no conclusions can be drawn about which strategy is most effective for reducing low-value nursing care due to a high level of heterogeneity and a lack of studies. We recommend that future studies better report the effects of de-implementation strategies and perform a process evaluation to determine to which extent the strategy has been used.Trial registrationThe review is registered in Prospero (CRD42018105100).

Dataset Information

Variability in Implementation of Interventions Aimed at Reducing Readmissions Among Patients With Heart Failure: A Survey of Teaching Hospitals.

Publications

Variability in Implementation of Interventions Aimed at Reducing Readmissions Among Patients With Heart Failure: A Survey of Teaching Hospitals.

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