Key result
A machine learning-based analytical framework was developed to predict 90-day hospital readmission risk for total joint replacement patients and support cost-effective intervention policies.
Why the study?
Does a machine learning-based readmission risk prediction model and cost analysis framework improve cost-effective intervention selection for TJR patients?
Does a machine learning-based readmission risk prediction model and cost analysis framework improve cost-effective intervention selection for TJR patients?
An analytical framework using machine learning can predict 90-day readmission risk for TJR patients and support cost-effective intervention policies.
Answers are grounded in this paper and cite their sources.
ML framework may guide TJR readmission policies; leaves open prospective validation before clinical adoption.
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.
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Lee et al. (2018) studied Total joint replacement (TJR). Machine learning-based readmission risk prediction model and cost analysis framework was evaluated on Hospital readmission within 90 days post-discharge. A machine learning-based analytical framework was developed to predict 90-day hospital readmission risk for total joint replacement patients and support cost-effective intervention policies.
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