An Analytical Framework for TJR Readmission Prediction and Cost-Effective Intervention

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis.

Original languageEnglish
Article number8419240
Pages (from-to)1760-1772
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number4
DOIs
Publication statusPublished - 2019 Jul
Externally publishedYes

Keywords

  • class imbalance
  • cost imbalance
  • intervention
  • machine learning, cost-effectiveness
  • risk prediction
  • Total joint replacement (TJR), readmission

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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