Monitoring total hip arthroplasty outcomes with a comparison of risk-adjustment frameworks

Yifeng Yu, Harriet Nembhard, Andrea Sillner, Naleef Fareed

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

Abstract

Unplanned readmission after total hip arthroplasty (THA) has become an increasingly serious problem in the U.S., especially after the Centers for Medicare and Medicaid Services (CMS) carried out the penalty program for readmission in 2015. Thus, it is important to accurately identify high-risk patients and monitor the surgical outcomes of the medical team. In this study, we used modern machine learning algorithms to conduct patient risk stratification. We compared random forest with decision tree and the most commonly-used risk-adjustment method, logistic regression, using the THA patient-level data records from an academic medical center during 2011-2015. The results indicate that random forest outperforms logistic regression and decision tree in accurately identifying high-risk patients. Thus, this study provides new opportunities for medical decision support. Such informed medical decision making may help clinicians obtain insights into targeting medical interventions, providing patient-centered care, and reducing unplanned readmissions.

Original languageEnglish (US)
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
PublisherInstitute of Industrial Engineers
Pages555-560
Number of pages6
ISBN (Electronic)9780983762461
StatePublished - Jan 1 2017
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: May 20 2017May 23 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Other

Other67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
CountryUnited States
CityPittsburgh
Period5/20/175/23/17

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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  • Cite this

    Yu, Y., Nembhard, H., Sillner, A., & Fareed, N. (2017). Monitoring total hip arthroplasty outcomes with a comparison of risk-adjustment frameworks. In H. B. Nembhard, K. Coperich, & E. Cudney (Eds.), 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 (pp. 555-560). (67th Annual Conference and Expo of the Institute of Industrial Engineers 2017). Institute of Industrial Engineers.