Ensuring high-quality health data is critical for effective decision-making and interoperability within the European Health Data Space (EHDS). As part of the QUANTUM project, this study developed a comprehensive framework for assessing data quality and utility through a modified Delphi method. The present study builds upon the results from QUANTUM Task 1.1, which involved an extensive and systematic literature review along with individual expert consultations. This process produced a set of 54 data quality (DQ) dimensions, which were the input for the actual study. A modified Delphi method, utilizing the RAND/UCLA appropriateness method, was conducted to reach consensus among experts on the most relevant dimensions. This iterative process involved two rounds, leveraging a 9-point Likert scale and statistical measures such as the Interpercentile Range Adjusted for Symmetry (IPRAS) to evaluate agreement. The study highlights critical considerations, including representativeness of respondents, potential biases, and methodological rigour. Results reveal a refined set of 12 prioritized data quality dimensions essential for creating a robust data quality and utility labelling tool. This paper discusses the methodology, key findings, and implications for future developments in data quality assessment for the EHDS.