A Welfare Test for Sharing Health Data

Anindya Sen, Helen Chen, Maura R. Grossman and Shu‐Feng Tsao

Review of Income and Wealth, 2025, vol. 71, issue 1

Abstract: Machine learning and artificial intelligence methods are being increasingly used in the analysis of patient level and other health data, resulting in new insights and significant societal benefits. However, researchers are often denied access to sensitive health data due to concerns on privacy and security breaches. A key reason for reluctance in data sharing by data custodians is the lack of a specific legislative or other methodological framework in balancing societal benefits against costs from potential privacy breaches. Consistent with traditional cost–benefit analysis and the use of Quality Adjusted Life Years (QALYs), this study proposes an economic test that would enable Research Ethics Boards (REBs) to assess the benefits versus risks of data sharing. Our test can also be used to assess whether REBs employ reasonable effort to protect individual privacy and avoid tort liability. Constructing an acceptable welfare test for health data sharing will also lead to a better understanding of the value of data.

Date: 2025
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