This paper introduces an optimization model to support decision making for selection of appropriate incentive-based interventions to reduce readmissions of patients with chronic obstructive pulmonary disease (COPD). Such a model can be used to identify the optimal incentive policies to encourage patients taking appropriate interventions so that the readmission risk can be minimized under an incentive budget constraint. Closed formulas are derived under various budget scenarios and managerial insights are discussed to guide intervention implementation. The results of this work provide a quantitative tool to support hospitals planning of intervention activities.
|Title of host publication
|2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
|IEEE Computer Society
|Number of pages
|Published - 2017 Jul 1
|13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: 2017 Aug 20 → 2017 Aug 23
|IEEE International Conference on Automation Science and Engineering
|13th IEEE Conference on Automation Science and Engineering, CASE 2017
|17/8/20 → 17/8/23
Bibliographical noteFunding Information:
This work is supported in part by NSF Grant No. CMMI-1536987.
ACKNOWLEDGEMENT The authors thank to the help of Jesus Chavez Mees, Henry Rose, and Bo Zhang of University of Wisconsin-Madison and the support from staffs of St. Mary’s Hospital.
© 2017 IEEE.
- Chronic obstructive pulmonary disease (COPD)
- optimal policy
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering