JMIR Publications

ABSTRACT

AI and digital health tools are arriving faster than the ideas needed to guide preparation for this new data environment. This Viewpoint proposes the Health Data Gravity Framework (HDGF) with four interconnected aims: to provide a new way of understanding how healthcare systems are structured; to predict their trajectory as data mass redistributes from institutions to individuals; to define the changing role of primary care—both the discipline and the professionals within it—in the system that results; and to guide preparation for patients, professionals, educators, and policymakers. Inspired by general relativity, the HDGF proposes that whoever holds and controls health data shapes the healthcare landscape around themselves—pulling system resources inward not by force, but by the natural slope of the field. The model centers on three interacting ideas: data mass (the volume and quality of data an entity holds), the accessibility A(e) and usability U(e) of each entity—determining where and how data mass exerts its pull, and a policy field that raises or lowers the whole landscape. We describe four archetypal system configurations—Pre-Gravity, Institutional Gravity, Distributed Gravity, and Sovereign Gravity—through which data accessibility and usability may expand from institutions toward patients; a formal representation of these dynamics is provided in Multimedia Appendix 1 for readers who wish to explore it. In the system the HDGF predicts, primary care evolves from Data Prospector through Resource Orchestrator to Holistic Companion—a professional who holds both the reach of the patient’s data field and the irreducibly human domains that lie beyond what digital systems can fully capture: sensitive privacy, emotional complexity, and the social contexts that shape health. These human domains do not become less important as AI advances—they become more so, because they are precisely what AI cannot replace.