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<p>Healthcare systems face increasing demands and cost pressures, driving a focus on efficiency improvements, particularly in hospitals. This study examines the efficiency of 35 urology departments in Austrian publicly funded hospitals (including public and private non-profit) using slack-based Data Envelopment Analysis (DEA) on 2017–2019 data. We analyse efficiency drivers via second-stage regression, focusing on processes, structures, and the external environment, to understand performance differences by ownership. Initial DEA results suggest higher efficiency in private non-profit hospitals. However, second-stage regression reveals that these seemingly more efficient private units benefit from a more advantageous external environment, characterized by fewer unplanned, night-time or weekend admissions. Crucially, when controlling for these environmental factors, the efficiency advantage of private hospitals diminishes or even reverses. To rigorously assess the impact of methodological choices, specifically the inclusion of environmental controls that capture differences in service provision roles, we conduct a meta-regression across 768 model specifications. This result demonstrates the significant influence of accounting for these contextual factors on the relative efficiency assessment of public versus private non-profit units, potentially explaining mixed results in prior literature. The findings are in line with both theoretical and empirical work regarding the shortcomings and potential unintended consequences of financial incentives in hospital financing. They underscore the critical need for hospital efficiency evaluations and funding models to account for the heterogeneous service provision roles and patient populations served by different hospital types. Failing to control for environmental factors can lead to biased conclusions favouring hospitals operating in less challenging contexts. The detailed department-level analysis and the robust methodological framework, highlighting the sensitivity to modelling choices, offer valuable insights for health policy aimed at equitable resource allocation and targeted efficiency improvements across the diverse landscape of publicly funded hospitals.</p>

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Context matters: a DEA and regression-based analysis of efficiency in Austrian public and private non-profit departments for urology

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Martin Zuba

摘要

Healthcare systems face increasing demands and cost pressures, driving a focus on efficiency improvements, particularly in hospitals. This study examines the efficiency of 35 urology departments in Austrian publicly funded hospitals (including public and private non-profit) using slack-based Data Envelopment Analysis (DEA) on 2017–2019 data. We analyse efficiency drivers via second-stage regression, focusing on processes, structures, and the external environment, to understand performance differences by ownership. Initial DEA results suggest higher efficiency in private non-profit hospitals. However, second-stage regression reveals that these seemingly more efficient private units benefit from a more advantageous external environment, characterized by fewer unplanned, night-time or weekend admissions. Crucially, when controlling for these environmental factors, the efficiency advantage of private hospitals diminishes or even reverses. To rigorously assess the impact of methodological choices, specifically the inclusion of environmental controls that capture differences in service provision roles, we conduct a meta-regression across 768 model specifications. This result demonstrates the significant influence of accounting for these contextual factors on the relative efficiency assessment of public versus private non-profit units, potentially explaining mixed results in prior literature. The findings are in line with both theoretical and empirical work regarding the shortcomings and potential unintended consequences of financial incentives in hospital financing. They underscore the critical need for hospital efficiency evaluations and funding models to account for the heterogeneous service provision roles and patient populations served by different hospital types. Failing to control for environmental factors can lead to biased conclusions favouring hospitals operating in less challenging contexts. The detailed department-level analysis and the robust methodological framework, highlighting the sensitivity to modelling choices, offer valuable insights for health policy aimed at equitable resource allocation and targeted efficiency improvements across the diverse landscape of publicly funded hospitals.