Diagnosis-group-specific transitional care program recommendations for 30-day rehospitalization reduction

Abstract

Thirty-day rehospitalization rate is a well-studied and important measure reflecting the overall performance of health systems. Recently, transitional care (TC) programs have been initiated to reduce avoidable rehospitalizations. These programs typically ask nurses to follow-up with patients after the hospitalization to manage issues and reduce the risk of rehospitalizations during health care transitions. As rehospitalization is a complex process that depends on many factors, it is unlikely that these interventions are effective for all patients across a diverse population. In this paper we consider individualized intervention or treatment recommendation rules (ITRs) aimed at maximizing overall treatment effectiveness. We investigate our approach in a setting where patients are divided into two diagnosis related groups, medically complicated and uncomplicated. As the treatment effects can greatly vary between the two groups, we allow our recommendation rules to be group specific. In particular, our approach can accommodate scale differences in treatment effects and utilize a tuning parameter to drive the similarity of the estimated ITRs between groups. Computation is achieved by transforming our problem into a form solvable by existing software, and a wrapper R package is developed for our proposed treatment recommendation framework. We conduct extensive evaluation through both simulation studies and analysis of a TC program.