Optimal Intervention Policies for Total Joint Replacement Postoperative Care Process

Hyo Kyung Lee, Jingshan Li, Philip A. Bain, Christine Baker

Research output: Contribution to journalArticlepeer-review

Abstract

Problem definition: Successful outcomes of total joint replacement (TJR) depend not only on the surgery but also on the patients' postsurgical behaviors; inadequate postsurgical performance can lead to complications and/or readmissions. To prevent such events, patients go through postoperative intervention processes in which they receive physical therapy and/or some form of rehabilitative services. Although the costs of providing such interventions and readmission penalties are substantial, no methods exist that enable healthcare professionals to determine the optimal timing and target group of the interventions. Academic/practical relevance: To address these gaps, this paper investigates the decision problem faced by healthcare professionals: When should we provide interventions to which group of patients to minimize the total expense? We formulate the postdischarge intervention process as a finite-horizon discrete-time Markov decision process. Methodology: We dynamically model the post-TJR intervention process by (1) directly incorporating the readmission risk and penalty, and (2) considering the varying effectiveness of interventions depending on where the patient is located (care facility type). Results: Through a case study at a community hospital, the applicability of the model is illustrated and a number of structural properties, including the existence of a control-limit type policy, are proved. Based on the data obtained from the collaborating hospital, the optimal policy suggests to start intervening skilled nursing facility (SNF) patients after the postacute recovery period. Managerial implications: The optimal policy of the model shows that the common practice of focusing intervention resources on immediate postoperative periods needs further justification, and more attention should be focused on the presurgical or surgical phase to devise interventions that are more tailored toward preventing early readmission causes.

Original languageEnglish
Pages (from-to)617-631
Number of pages15
JournalManufacturing and Service Operations Management
Volume24
Issue number1
DOIs
Publication statusPublished - 2022 Jan
Externally publishedYes

Keywords

  • Healthcare management
  • Operations strategy
  • Service operations
  • Stochastic methods

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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