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: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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. © 2017 IEEE.

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
Volume	2017-August
ISBN (Print)	9781509067800
DOIs	https://doi.org/10.1109/COASE.2017.8256165
Publication status	Published - 1 Jul 2017
Externally published	Yes
Event	13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: 20 Aug 2017 → 23 Aug 2017

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
Place	China
City	Xi'an
Period	20/08/17 → 23/08/17

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

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

Research Keywords

intervention
patient-centered care
readmission
risk
simulation
Total joint replacement

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10.1109/COASE.2017.8256165

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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 (Vol. 2017-August, 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

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

@inproceedings{18d8065990cb4ebfbc31a5bffe5cb46e,

title = "Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations",

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. {\textcopyright} 2017 IEEE.",

keywords = "intervention, patient-centered care, readmission, risk, simulation, Total joint replacement",

author = "Lee, \{Hyo Kyung\} and Rebecca Jin and Yuan Feng and Bain, \{Philip A.\} and Jo Goffinet and Christine Baker and Jingshan Li",

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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. vol. 2017-August, 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, 20/08/17. 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. Vol. 2017-August IEEE Computer Society, 2017. p. 568-573 (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Lee, Hyo Kyung

AU - Jin, Rebecca

AU - Feng, Yuan

AU - Bain, Philip A.

AU - Goffinet, Jo

AU - Baker, Christine

AU - Li, Jingshan

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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. © 2017 IEEE.

AB - 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. © 2017 IEEE.

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KW - risk

KW - simulation

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ER -

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. Vol. 2017-August. IEEE Computer Society. 2017. p. 568-573. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/COASE.2017.8256165

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

Abstract

Publication series

Conference

Bibliographical note

Funding

Research Keywords

Access Output

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