A decision support system on postoperative care management of total joint replacement patients using clinical outcome measurement

Hanie Eskandari, Hyo Kyung Lee

Research output: Contribution to journalArticlepeer-review

Abstract

The postoperative care of total joint replacement patients is of interest to healthcare practitioners due to the high number of annual replacements and growing economic burden on the U.S. healthcare system. Minimizing postoperative care costs and maximizing patients’ functional outcomes are the key aspects of postoperative care management. In this paper, a novel analytical framework is introduced in which multiple measurements are concurrently utilized to dynamically assess the recovery progress of the patients, and furthermore, be used as a tool to devise personalized post-discharge intervention plans for the patients. A Markov decision process model is formulated to provide decision support on determining cost-effective intervention care plans that can be used as clinical guidelines in practice. The results clearly demonstrate the risk of using a single measurement method which may lead to inadequate level of intervention, and justifies the need of such modeling based approaches for devising the most appropriate care levels especially in the early recovery periods.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Robotics and Automation Letters
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Decision making
  • Health Care Management
  • Hospitals
  • Markov processes
  • Medical services
  • Probability and Statistical Methods
  • PROM
  • Robotics and Automation in Life Sciences
  • Surgery
  • Time measurement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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