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Abstract
The healthcare policy reforms implemented in Portugal in recent years significantly impacted the quality of health care provided to Portuguese citizens. Since public hospitals play a pivotal role as secondary care providers within the National Health Service (NHS), it is essential to assess their quality. However, a generalized assessment of this multidimensional concept is insufficient to differentiate hospitals across different quality dimensions, limiting the efficiency of benchmarking processes. The conjoint use of the Electre Tri-nC method with the Multiple Criteria Hierarchy Process (MCHP) allowed us to assess quality in Portuguese public hospitals by encompassing dimension-based analysis and a holistic perspective. Electre Tri-nC is a multiple criteria sorting method that categorizes each alternative (in this case, a public hospital in Portugal) into pre-defined and ordered categories representing quality levels. The hospitals are assessed based on four non-elementary criteria (Timeliness, Effectiveness, Safety and Efficiency) structured hierarchically in logical subsets of elementary criteria, allowing MCHP to evaluate each hospital based on specific criteria subsets or the comprehensive level. The sample size is 25 hospitals, and the analysis period includes the years from 2019 to 2022. The results evidence a general downward trend in quality, culminating in poor quality levels in most public hospitals in 2022, particularly in the Effectiveness dimension. A robustness analysis was conducted to examine the resilience and reliability of the model. By establishing a framework for assessing quality assessment across different critical dimensions, this work provides an enhanced hospital benchmarking process for healthcare policymakers. This process can serve as a reference for implementing new hospital reforms based on replicating the successful practices adopted by the healthcare facilities that demonstrate superior quality results in specific dimensions or even allocating resources more efficiently across the entire network by identifying dimensions in which hospitals are performing poorly.
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