Do the EHDS, GDPR and AI Act Protect Against the Risk of Discrimination in DQUL-Based Health Technology Assessment?

Abstract

This article examines whether the European Health Data Space (EHDS), the General Data Protection Regulation (GDPR), and the Artificial Intelligence Act adequately prevent discriminatory outcomes in DQUL-based health technology assessment. Focusing on the Data Quality and Utility Label (DQUL) and its interaction with Quality-Adjusted Life Year (QALY) cost-effectiveness models, the article argues that regulatory compliance does not eliminate the risk of structural disadvantage. It introduces the concept of numerical discrimination to describe inequality arising from threshold-based evaluative architectures that systematically undervalue underrepresented populations. Through doctrinal analysis of EU equality, data protection, and AI governance law, the article demonstrates that current frameworks struggle to capture distributive harms arising from hierarchical data governance and proposes reforms that integrate distributive impact assessment and population representativeness into health data architecture.