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

doi:10.1109/COASE.2017.8256165

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 proceeding › Conference contribution

2 Citations (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 language	English
Title of host publication	2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Publisher	IEEE Computer Society
Pages	568-573
Number of pages	6
ISBN (Electronic)	9781509067800
DOIs	https://doi.org/10.1109/COASE.2017.8256165
Publication status	Published - 2017 Jul 1
Externally published	Yes
Event	13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: 2017 Aug 20 → 2017 Aug 23

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2017-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/Territory	China
City	Xi'an
Period	17/8/20 → 17/8/23

Bibliographical note

Funding Information:
This work is supported in part by NSF Grant No. CMMI-1536987.

Funding Information:
This work is supported in part by NSF Grant No. CMMI-1536987. H.K. Lee, R. Jin, Y. Feng and J. Li are with Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA. [email protected], [email protected], [email protected], [email protected]. Y. Feng is also with Department of Automation, Tsinghua Unviersity, Beijing 100084, China. P.A. Bain is with Dean Health System, Madison, WI 53562, USA. [email protected]. J. Goffinet and Christine Baker are with St. Mary’s Hospital, Madison, WI 53715, USA. [email protected], [email protected].

Publisher Copyright:
© 2017 IEEE.

Keywords

intervention
patient-centered care
readmission
risk
simulation
Total joint replacement

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/COASE.2017.8256165

Cite this

Lee, H. K., Jin, R., Feng, Y., Bain, P. A., Goffinet, J., Baker, C., & Li, J. (2017). Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (pp. 568-573). (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256165

Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations. / Lee, Hyo Kyung; Jin, Rebecca; Feng, Yuan et al.
2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE Computer Society, 2017. p. 568-573 (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, HK, Jin, R, Feng, Y, Bain, PA, Goffinet, J, Baker, C & Li, J 2017, Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations. in 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE International Conference on Automation Science and Engineering, vol. 2017-August, IEEE Computer Society, pp. 568-573, 13th IEEE Conference on Automation Science and Engineering, CASE 2017, Xi'an, China, 17/8/20. https://doi.org/10.1109/COASE.2017.8256165

Lee HK, Jin R, Feng Y, Bain PA, Goffinet J, Baker C et al. Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE Computer Society. 2017. p. 568-573. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/COASE.2017.8256165

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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.",

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

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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