Hospital efficiency assessment: a systematic review of DEA and hybrid DEA-machine learning approaches.

Abstract

The increasing complexity of healthcare systems and growing resource constraints have made hospital efficiency assessment central to healthcare management and policy. Data Envelopment Analysis (DEA) has been widely used to evaluate hospital efficiency due to its ability to accommodate multiple input and output structures. However, classical DEA applications provide limited support for explaining efficiency scores, generating predictions, and informing managerial decision-making. This study systematically reviews hospital efficiency studies published between 2020 and 2025 to examine the use and limitations of DEA and to identify approaches integrating DEA with machine learning (ML). To more comprehensively demonstrate the methodological diversity of DEA-machine learning integration, studies from non-healthcare fields have also been included in the comparative analysis. Following PRISMA guidelines, standalone DEA and DEA-ML studies were analysed and coded across methodological dimensions. Multiple Correspondence Analysis was applied to identify dominant methodological configurations and emerging patterns in the literature. Findings indicate that CCR and BCC models remain prevalent, with human resources and financial indicators as common inputs and service delivery measures as outputs. DEA-ML research largely relies on two-stage structures focused on classification and prediction. The results highlight methodological gaps and the potential of more explainable and decision-support-oriented DEA-ML approaches to enhance benchmarking and resource allocation in hospital efficiency management.