Characterizing a Framework for Assessment of the Use of FAIR Principles for Health Data in Digital Health Devices Regulation

This paper presents a structured framework to assess the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles in health datasets used in regulatory processes for digital health devices. With the FAIR Data Maturity Model from the Research Data Alliance (RDA) and the Pistoia Alliance's FAIR Maturity Matrix as foundational guides, this framework provides a scalable, adaptable approach for evaluating dataset readiness and compliance with regulatory requirements. By focusing on metadata quality, interoperability, and privacy, this study supports regulatory bodies and developers in aligning with FAIR principles, enhancing transparency, and ensuring that data meets the standards necessary for digital health device approval.