Interventions to Reduce 30-Day Rehospitalization: A Systematic Review

Hansen, Luke O.; Young, Robert S.; Hinami, Keiki; Leung, Alicia; Williams, Mark

doi:10.7326/0003-4819-155-8-201110180-00008

Cited by 1,035 publications

(1,025 citation statements)

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“…12 Impact of this project may have been tempered by the need to translate external QI content to the local setting. Additionally, in contrast to experimental studies that are limited in scope and timing and often scaled to a research budget, BOOST sites were encouraged to implement Project BOOST in the clinical setting even if no new funds were available to support the effort.…”

Section: Discussionmentioning

confidence: 99%

Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization

Hansen

Greenwald

Budnitz³

et al. 2013

Journal of Hospital Medicine

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Section: Discussionmentioning

confidence: 99%

Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization

Hansen

Greenwald

Budnitz³

et al. 2013

Journal of Hospital Medicine

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238

186

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“…Thus, a key finding of our study is that patients' readmission risk can be accurately predicted with the AMI READMITS score on the first day of hospitalization, enabling targeted early intervention to maximize the potential benefit of readmission reduction interventions 5, 8. This approach can be implemented by clinicians at bedside, or by hospitals and health systems by integrating the AMI READMITS score directly into the EHR, or by extracting EHR data in near real‐time, as our group has previously done for heart failure 10…”

Section: Discussionmentioning

confidence: 95%

“…Since 2012, hospitals have been subject to financial penalties for excessive 30‐day readmissions among patients hospitalized for AMI under the Hospital Readmissions Reduction Program, implemented by the Centers for Medicare and Medicaid Services (CMS). Although federal readmission penalties have incentivized readmissions reduction intervention strategies (known as transitional care interventions), these interventions are resource intensive, are most effective when implemented well before discharge, and have been only modestly successful when applied indiscriminately to all inpatients 5, 6, 7, 8…”

mentioning

confidence: 99%

“…However, a recent systematic review of AMI‐specific readmission models found that current models have only modest predictive ability and with uncertain generalizability because of methodological limitations 12. Furthermore, few existing AMI‐specific models have the potential to provide actionable data early during a patient's hospital course, which is the optimal time to initiate interventions to maximize effectiveness 5, 8, 12…”

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confidence: 99%

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Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

Nguyen

Makam

Clark

et al. 2018

JAHA

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BackgroundReadmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor‐to‐modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high‐risk patients as early as possible during hospitalization.Methods and ResultsWe used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all‐cause nonelective 30‐day readmissions, using stepwise backward selection and 5‐fold cross‐validation. Of 826 patients hospitalized with AMI, 13% had a 30‐day readmission. The first‐day AMI model (the AMI “READMITS” score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism‐corrected C‐statistic of 0.73 (95% confidence interval, 0.71–0.74) and was well calibrated. The full‐stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism‐corrected C‐statistic of 0.75 (95% confidence interval, 0.74–0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models.ConclusionsThe parsimonious AMI READMITS score enables early prospective identification of high‐risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full‐stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification.

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“…5,30 It is not well understood if the follow-up appointment needs to be within a specific time frame, especially if the patient is already being "touched" once by the system through the posthospitalization call. General consensus falls within 7 days for patients at moderate to high risk for readmissions.…”

Section: After Hospital Dischargementioning

confidence: 99%

A primary care physician's ideal transitions of care—where's the evidence?

Tang

2013

Journal of Hospital Medicine

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Reducing hospital readmissions is a national healthcare priority. Most of the interventions to reduce hospital readmission have been concentrated in the inpatient setting. However, there is increasing attention placed on the role of primary care physicians (PCPs) in improving the transition from hospital to home. In this article, a primary care physician's perspective of how inpatient and outpatient providers can partner to create the ideal care transition is described. Seven steps that occur during the hospitalization are highlighted: communicate with the PCP on admission, involve the PCP early regarding discharge planning, notify the PCP on hospital discharge, complete the discharge summary at time of discharge, schedule follow‐up appointments by discharge, ensure prescriptions are available at the patient's pharmacy, and educate the patient about self‐management. Another 7 are described as the role of the PCP and clinic staff: call the patient within 72 hours of discharge, ensure follow‐up appointments with the PCP, coordinate care, repeat above until medically stable, create access for patients with new symptoms, track readmission rates, and track and review frequently admitted patients. Insights are offered on how the changing financial landscape can help support elements of this idealized transition‐of‐care program. Journal of Hospital Medicine 2013;8:472–477. © 2013 Society of Hospital Medicine

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Interventions to Reduce 30-Day Rehospitalization: A Systematic Review

Abstract: None.

Cited by 1,035 publications

References 50 publications

Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization

Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

A primary care physician's ideal transitions of care—where's the evidence?

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