Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations

Hyo Kyung Lee, Rebecca Jin, Yuan Feng, Philip A. Bain, Jo Goffinet, Christine Baker, Jingshan Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper studies the post-surgery care process for total joint replacement (TJR) patients. First, factors affecting readmission risks are identified and a multivariate logistic regression model is introduced to predict a patient's readmission probability from the patient profile. Based on readmission risk and patient eligibility, different intervention processes can be carried out. Specifically, three intervention options are considered: nursing home, home care service, and self-care. A discrete-event simulation model is introduced to illustrate how intervention process moves along the 90 day post discharge phase. Finally, the models are used to identify the best intervention strategy to reduce overall readmission rate with minimal cost.

Original languageEnglish
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages568-573
Number of pages6
ISBN (Electronic)9781509067800
DOIs
Publication statusPublished - 2017 Jul 1
Externally publishedYes
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: 2017 Aug 202017 Aug 23

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2017-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/TerritoryChina
CityXi'an
Period17/8/2017/8/23

Keywords

  • intervention
  • patient-centered care
  • readmission
  • risk
  • simulation
  • Total joint replacement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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