TY - GEN
T1 - Modeling and analysis of postoperative intervention process for total joint replacement patients using simulations
AU - Lee, Hyo Kyung
AU - Jin, Rebecca
AU - Feng, Yuan
AU - Bain, Philip A.
AU - Goffinet, Jo
AU - Baker, Christine
AU - Li, Jingshan
N1 - 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. hlee555@wisc.edu, rcjin@wisc.edu, fengyuan1216@gmail.com, jingshan.li@wisc.edu. 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. philip.bain@ssmhealth.com. J. Goffinet and Christine Baker are with St. Mary’s Hospital, Madison, WI 53715, USA. Jo.Goffinet@ssmhealth.com, christine.baker@ssmhealth.com.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
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.
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.
KW - intervention
KW - patient-centered care
KW - readmission
KW - risk
KW - simulation
KW - Total joint replacement
UR - http://www.scopus.com/inward/record.url?scp=85044923190&partnerID=8YFLogxK
U2 - 10.1109/COASE.2017.8256165
DO - 10.1109/COASE.2017.8256165
M3 - Conference contribution
AN - SCOPUS:85044923190
T3 - IEEE International Conference on Automation Science and Engineering
SP - 568
EP - 573
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Y2 - 20 August 2017 through 23 August 2017
ER -