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

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

Alvaro J. Riascos, Natalia Serna
Copyright: © 2018 |Volume: 8 |Issue: 2 |Pages: 21
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781522544678|DOI: 10.4018/IJKDB.2018070103
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MLA

Riascos, Alvaro J., and Natalia Serna. "Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System." IJKDB vol.8, no.2 2018: pp.44-64. http://doi.org/10.4018/IJKDB.2018070103

APA

Riascos, A. J. & Serna, N. (2018). Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(2), 44-64. http://doi.org/10.4018/IJKDB.2018070103

Chicago

Riascos, Alvaro J., and Natalia Serna. "Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.2: 44-64. http://doi.org/10.4018/IJKDB.2018070103

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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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