Data sharing for precision medicine: Policy lessons and future directions

Abstract

Data sharing is a precondition of precision medicine. Numerous organizations have produced abundant guidance on data sharing. Despite such efforts, data are not being shared to a degree that can trigger the expected data-driven revolution in precision medicine. We set out to explore why. Here we report the results of a comprehensive analysis of data-sharing guidelines issued over the past two decades by multiple organizations. We found that the guidelines overlap on a restricted set of policy themes. However, we observed substantial fragmentation in the policy landscape across specific organizations and data types. This may have contributed to the current stalemate in data sharing. To move toward a more efficient data-sharing ecosystem for precision medicine, policy makers should explore innovative ways to cope with central policy themes such as privacy, consent, and data quality; focus guidance on interoperability, attribution, and public engagement; and promote data-sharing policies that can be adapted to multiple data types.