Robustness and Normative Sensitivity of Ranking-Based Decision Artifacts: A TOPSIS Evaluation of an EU Composite Health Index

Résumé fourni par la source

Digital transformation in healthcare increasingly relies on data-driven governance mechanisms that translate complex health system information into actionable policy and managerial decisions. Composite performance indices serve as digital infrastructures supporting benchmarking, reform prioritization, and resource allocation across healthcare systems. Despite their influence, the reliability of such analytics-based decision tools remains underexplored. This study develops an EU Composite Health Index (CHI) using World Health Organization indicators and evaluates it with the TOPSIS multi-criteria decision method. Using panel data for EU countries (2015–2019), we examine ranking stability, sensitivity to alternative weighting assumptions, and robustness under Monte Carlo perturbations. Results show stable overall performance structures but substantial positional changes among mid-ranked systems under different normative priorities. The findings indicate that data-driven performance analytics shape healthcare transformation while remaining value-contingent. The study contributes to health informatics by showing how digital evaluation systems influence policy interpretation, resource allocation, and strategic decision-making in healthcare.

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