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 rehospitaliza-tions. 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, med-ically 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1478-1498 |
| Number of pages | 21 |
| Journal | Annals of Applied Statistics |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2021 |
Bibliographical note
Funding Information:Research reported in this article was partially funded through two Patient-Centered Outcomes Research Institute (PCORI) Awards (ME-1409-21219 and HSD-1603-35039). The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee. This project was also supported by the UW Health Office of Population Health, the Health Innovation Program, the UW School of Medicine and Public Health from The Wisconsin Partnership Program, and the Community-Academic Partnerships core of the University of Wisconsin Institute for Clinical and Translational Research (UW ICTR) through the National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Funding Information:
Acknowledgments. Research reported in this article was partially funded through two Patient-Centered Outcomes Research Institute (PCORI) Awards (ME-1409-21219 and HSD-1603-35039). The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee. This project was also supported by the UW Health Office of Population Health, the Health Innovation Program, the UW School of Medicine and Public Health from The Wisconsin Partnership Program, and the Community-Academic Partnerships core of the University of Wisconsin Institute for Clinical and Translational Research (UW ICTR) through the National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Publisher Copyright:
© Institute of Mathematical Statistics, 2021.
Keywords
- Data integration
- Heterogeneity of treatment effect
- Observational data
- Rehospitalization
- Subgroup identification