Hospitals Sharing Patient Data and Biospecimens with Commercial Entities: Evidence-Based Translation to Improved Practice

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PROJECT SUMMARY ABSTRACT Background: Widely sharing patient data and biospecimens can enable life-saving advances in translational science. Hospitals and commercial entities focusing on precision medicine and other genetic and artificial intelligence technologies have increasingly partnered to share these data resources. To ensure equitable access to scientific advances, datasets must include patients reflecting the demographic distribution of disease. However, our previous research demonstrates that many patients are uncomfortable with hospitals sharing their data with industry and that individuals who identify as Black or Hispanic are consistently more likely to report discomfort. We need to measure diverse patient responses to actual hospital data- sharing practices with the power to differentiate preferences across race, ethnicity, and other relevant scales to identify promising areas for compromise with an equitable impact. Approach: The goal of this proposal is to identify areas for compromise between patients and hospitals to improve data-sharing practices with industry in ways that are respectful of individual patient autonomy and equitable in impact across diverse communities. Research Aims: To achieve this goal, our project has three specific aims: (1) Explore hospital data-sharing policies and practices with commercial entities, and their industry and patient relationships, (2) Characterize the kinds of hospitals that have been approached by, are interested in, and/or have shared patient data with industry and measure the kinds of patient impact that might drive change to specific data-sharing practices, and (3) Examine the acceptability of hospital data-sharing practices, preferences amongst acceptable practices, and anticipated impact of unacceptable practices generally as well as by race and ethnicity specifically. Impact: Patient discomfort with hospital data sharing practices that patients find unacceptable – and its association with race and ethnicity – is a significant problem for increasing the accessibility and generalizability of translational science advances. Though our mixed methods ELSI approach to discovering areas for compromise between hospitals and patients, while centering those historically excluded, this proposal will have a major impact on improving data-sharing practices to facilitate diversity in data used to support high-impact translational science.