Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System

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Author(s): Alvaro J. Riascos (University of los Andes and Quantil, Bogotá, Colombia)and Natalia Serna (University of Wisconsin-Madison, Madison, USA)
Copyright: 2022
Pages: 18
Source title: Research Anthology on Machine Learning Techniques, Methods, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6291-1.ch074

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in which the decision of whether to intervene on a patient depends on a simple decision model and the prediction of the patient risk of an annual length-of-stay using machine learning techniques. These results show that the prevention program achieves significant cost savings relative to several base scenarios for program efficacies greater than or equal to 40% and intervention costs per patient of 100,000 to 700,000 Colombian pesos (i.e., approximately 14% to 100% of the average cost per patient in Colombia statuary health care system). This article also shows how tree-based methods outperform linear regressions when predicting an annual length-of-stay and the final model achieves a lower out-of-sample error compared to those of the Heritage Health Prize.

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Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System

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