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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:BACKGROUND:In 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. METHODS:PubMed, 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. RESULTS:Twenty-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). CONCLUSIONS:The 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 REGISTRATION:The review is registered in Prospero (CRD42018105100).

Project description:Importance:Public reporting of hospitals' 30-day risk-standardized readmission rates following heart failure hospitalization and the financial penalization of hospitals with higher rates have been associated with a reduction in 30-day readmissions but have raised concerns regarding the potential for unintended consequences. Objective:To examine the association of the Hospital Readmissions Reduction Program (HRRP) with readmission and mortality outcomes among patients hospitalized with heart failure within a prospective clinical registry that allows for detailed risk adjustment. Design, Setting, and Participants:Interrupted time-series and survival analyses of index heart failure hospitalizations were conducted from January 1, 2006, to December 31, 2014. This study included 115?245 fee-for-service Medicare beneficiaries across 416 US hospital sites participating in the American Heart Association Get With The Guidelines-Heart Failure registry. Data analysis took place from January 1, 2017, to June 8, 2017. Exposures:Time intervals related to the HRRP were before the HRRP implementation (January 1, 2006, to March 31, 2010), during the HRRP implementation (April 1, 2010, to September 30, 2012), and after the HRRP penalties went into effect (October 1, 2012, to December 31, 2014). Main Outcomes and Measures:Risk-adjusted 30-day and 1-year all-cause readmission and mortality rates. Results:The mean (SD) age of the study population (n = 115?245) was 80.5 (8.4) years, 62 927 (54.6%) were women, and 91 996 (81.3%) were white and 11 037 (9.7%) were black. The 30-day risk-adjusted readmission rate declined from 20.0% before the HRRP implementation to 18.4% in the HRRP penalties phase (hazard ratio (HR) after vs before the HRRP implementation, 0.91; 95% CI, 0.87-0.95; P?<?.001). In contrast, the 30-day risk-adjusted mortality rate increased from 7.2% before the HRRP implementation to 8.6% in the HRRP penalties phase (HR after vs before the HRRP implementation, 1.18; 95% CI, 1.10-1.27; P?<?.001). The 1-year risk-adjusted readmission and mortality rates followed a similar pattern as the 30-day outcomes. The 1-year risk-adjusted readmission rate declined from 57.2% to 56.3% (HR, 0.92; 95% CI, 0.89-0.96; P?<?.001), and the 1-year risk-adjusted mortality rate increased from 31.3% to 36.3% (HR, 1.10; 95% CI, 1.06-1.14; P?<?.001) after vs before the HRRP implementation. Conclusions and Relevance:Among fee-for-service Medicare beneficiaries discharged after heart failure hospitalizations, implementation of the HRRP was temporally associated with a reduction in 30-day and 1-year readmissions but an increase in 30-day and 1-year mortality. If confirmed, this finding may require reconsideration of the HRRP in heart failure.

Project description:BACKGROUND:There are 32 teaching hospitals in France, including 30 University hospitals and two Regional teaching hospitals. Teaching hospitals have three roles: health care provision, training of healthcare professionals, and medical research. These roles lead to frequent interactions with pharmaceutical and medical device companies, inevitably raising risks of conflicts of interests. Therefore, policies to manage conflict of interests (COI) are crucial. This study aims to examine COI policies in French teaching hospitals. METHODS:All French teaching hospitals (n = 32) were included in this study. All hospitals websites were screened for institutional COI policies and curriculum on COI, using standardized keyword searches. More data were collected through a questionnaire addressed to each chief executive officer (CEO) of the teaching hospital. We used predefined criteria (n = 20) inspired by similar surveys on COI policies in French, US and Canadian medical schools, with some additions to reflect the local hospital context. A global score for each hospital, ranging from 0 to 60 (higher scores denoting stronger policies) was calculated by summing points obtained for each criterion. RESULTS:All 32 hospitals had websites; 21 hospitals listed policies or regulations on their websites or provided them on request. In December 2017, 17 (53.1%) had rules and regulations for some items only, four of which (12.5%) have considered implementing a policy, and only two (6.3%) have begun implementation. 15 (46.9%) had no evidence of COI policies and a null score. The maximum score was 24 out of 60. CONCLUSION:This is the first systematic assessment of COI policies in teaching hospitals in France. Such policies are needed to protect patients, clinicians and students from undue commercial influence. Despite public and political pressure for better management of COI, few teaching hospitals have implemented comprehensive and protective policies, and some hospitals lacked policies altogether. These results highlight the need for greater attention to management of COI within teaching hospitals. One potential solution would be to integrate COI policies into hospital accreditation procedures, in order to ensure a baseline of management at all teaching hospitals.

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.

Similar Datasets

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