Send message to Authors

Paper short abstract

Tracing personalized and precision medicine in the twenty first century, this paper uses evolutionary concept analysis to show how shifting labels link care promises to data standards and programs. It asks who or what is included or made invisible as medicine becomes more data-driven.

Paper long abstract

Although standard medical care is already expected to be tailored to a person, personalized medicine is often presented as a novel twenty-first-century ideal. Forming part of a broader lexicon alongside precision, these interchangeable labels direct attention toward progress built around data, standards, and coordination. This is not simply a language issue; these terms carry different promises about what medicine is and who shapes the conditions making those promises plausible across policy, industry, and research.

Using evolutionary concept analysis, I trace how these terms change meaning across research and policy arenas. A preliminary review of publications shows precision become a dominant term post-2015, aligning with the US Precision Medicine Initiative and Europe’s ICPerMed. I then turn to how these agendas enter policy and funding texts, I argue that while personalization points to a broad data footprint, precision narrows the focus to standardized clinical and genomic datasets. Moreover, as these terms travel across different geographies, they function less as stable scientific concepts and more as flexible tools for policy and market-building.

This data-centric trajectory gained significant prominence during the COVID-19 pandemic, reinforcing the assumption that better healthcare requires more data and integration. Yet, "better" and "more" remain hard to measure, while standards continually lag behind expanding data production. If personalization becomes merely a data project, who or what is made invisible? Rather than offering a simple fix, this paper uses concept drift to open critical questions about inclusion, cost, and responsibility in the making of data-driven medicine.