Data-driven identification of outpatient-suitable procedures: a machine learning approach

Abstract

Policymakers worldwide are encouraging a shift from inpatient to outpatient care to improve the efficiency of health systems. One of the first steps in such efforts typically involves allowing providers, often hospitals, to perform a designated list of procedures on an outpatient basis. However, determining which procedures are suitable for the hospital outpatient setting remains challenging and has traditionally relied on expert judgment and established practices. Our study advances this approach by employing supervised machine learning techniques to identify patterns in physician and expert decisions. We present a comprehensive classification of hospital procedures as either inpatient- or outpatient-suitable and use some methods of explainable AI methods to identify the main factors influencing these assessments. Our model achieves high accuracy (92%) and a robust area under the receiver operating characteristic curve (> 95%), assigning outpatient suitability scores to the entire German procedure catalog. These scores can easily be used with aggregate hospital data from different countries. To validate our approach, we applied the model to a surgical procedures shortlist from the OECD. Our findings provide decision-makers with a data-driven framework for developing targeted strategies to incentivize the provision of outpatient care.