30-day Hospital Readmission Prediction Models: Design, Performance and Generalizability
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Date
2016-03-18
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Johns Hopkins University
Abstract
Following the introduction of the Hospital Readmissions Reduction Program (HRRP) in 2012, there has been a push in research and quality improvement efforts to reduce 30-day hospital readmissions. While the needle has moved slightly downward for the high-risk conditions targeted, the majority of hospitals have received some penalty in 2015, totaling over $400 million. Having prediction models for avoidable readmissions would help providers in allocating resources and designing interventions for high-risk individuals. This systematic review searched for peer-reviewed efforts to predict 30-day readmissions published since 1990. In total, 428 articles were assessed for inclusion / exclusion criteria, resulting in 38 articles surviving all criteria. These articles were coded for several factors influencing study design including research setting, data sources, cohort size and characteristics. Further, methodologies were assessed for models implemented, input variable types, validation procedures, and model output and performance. Most studies used electronic medical or administrative records, while a few studies integrated additional data sources such as patient registries and direct patient follow-up. Cohorts varied, with congestive heart failure being the most frequently studied and, surprisingly, only one study developing a combined model for all three conditions originally included in the HRRP. The vast majority of studies used multivariate logistic regression to predict 30-day readmission outcomes, with varied performance. A few efforts were made to include novel statistical methods for readmission prediction, but their ability to improve performance was inconclusive. Unexpectedly, only one study integrated a prediction model into a clinical workflow. The low number of integration efforts could be a result of the difficulty in generating highly accurate models. As the HRRP expands to more conditions, and 30-day readmission gains traction as a quality metric, it is imperative that hospitals are fully informed when deciding which readmission prediction models to implement and when to use them. Several studies suggested model generalizability as a limitation and there were also several key pieces of information missing from some studies. To help assess model generalizability and ensure consistent reporting, this review proposes a modified checklist for 30-day readmission prediction efforts.
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Readmission, Prediction, Systematic Review, 30-day Readmission, Hospital Readmission