DIAGNOSIS-GROUP-SPECIFIC TRANSITIONAL CARE PROGRAM RECOMMENDATIONS FOR 30-DAY REHOSPITALIZATION REDUCTION

• 成果类型: Article
• 署名作者: Yu, Menggang; Kuang, Chensheng; Huling, Jared D.; Smith, Maureen
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1473
• 发表日期: 2021
• 页码: 1478-1498
• 关键词: hospital readmissions; variable selection; subgroup identification; strategies; DESIGN; rates

摘要

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.