Managing Indicator Vulnerabilities in Healthcare: Mixed-Method Evaluation, Anti-Gaming Design, and Adaptive Metric Governance
Abstract Healthcare quality measurement routinely relies on structure (resources), process (actions), and outcome (results) indicators, often combined into broader “quality indicator” systems. While foundational, each indicator type is vulnerable to confounding, weak causal interpretability, data artifacts, and behavioral distortion when linked to accountability or incentives. Building on Donabedian’s framework for evaluating quality and modern guidance on measure evaluation and lifecycle management, this essay proposes a practical governance approach: (1) mixed-method evaluation that triangulates quantitative signals with theory-driven and qualitative inquiry; (2) anti-gaming measure design that anticipates Goodhart/Campbell effects and reduces manipulability; and (3) periodic reassessment that treats measures as adaptive social instruments requiring continuous validation, recalibration, and retirement. The central thesis is that credible quality assessment is not achieved by “bet...