Why Are Rural Hospitals Closing in the U.S.? Predictors Identified Using Explainable Machine Learning

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Examines rural hospital closures in the U.S. from 2011 to 2022. Utilizes national longitudinal data and explainable machine learning (XML) to analyze 2,683 rural hospitals according to financial, operational, and demographic characteristics as well as closure risks. Discusses the utility of longitudinal data integrated into XML to observe hospital trends and create early warning systems.

Kiruthika Balakrishnan, Tesfamariam M. Abuhay, Hana E. Hinkle

BMC Health Services Research, 26, 1049

05/2026

AI · Closures of healthcare facilities and services · Healthcare business and finance · Hospitals · Statistics and data