Toward labor-sensitive research: Health data labor and targeted inclusion in precision medicine research

Data-intensive healthcare has an equity problem. As data-intensive healthcare expands, historical injustices embedded in existing datasets limit its benefits for marginalized groups. While national and international Precision Medicine Research (PMR) initiatives - like All of Us (US) and the 100k Genome Project (UK) - aim to address these gaps through targeted inclusion, such strategies carry exploitative potential. Representative data serves not only underserved communities but also public and commercial interests, raising additional ethical concerns about how data gaps are filled. This paper critically examines targeted inclusion efforts and discussions thereof in PMR ethics, which tend to overlook the risks and social conditions participants face during research participation. Drawing on the concept of 'clinical labor', we introduce the idea of health data labor - the often invisible, unequal labor involved in health data generation. This framing helps expose how the burdens and risks of PMR participation may follow existing social inequities. We argue that without addressing these inequities in the participation process, inclusionary efforts risk reinforcing injustice. We conclude by proposing 'labor-sensitive research design' - a set of practices to make PMR more equitable in both process and outcome.