System: A Systems-Level View of Health

Health data is currently stored as isolated facts—a blood pressure reading here, a postcode there—with no links between them, even though 80% of health outcomes are shaped by social and environmental factors outside the clinic. This matters because the current approach treats health as a collection of independent pieces: organs, diseases, genes. The relationships between those pieces—the cause-and-effect connections that actually determine whether someone gets sick or stays well—are invisible. Without seeing those links, doctors, researchers, and policymakers cannot understand a patient or population as a whole system. System is building an open, living model of health—a large-scale graph that automatically links millions of determinants, interventions, and outcomes by mining evidence from scientific literature, expert databases, and real-world data. The graph updates itself as new evidence emerges and is exposed through APIs for researchers and developers. If successful, this could transform how health data is organised and used. Instead of siloed records, researchers could trace how a change in housing policy might affect diabetes rates, or how air quality interacts with a genetic predisposition. The infrastructure would quietly underpin better clinical decisions, more targeted public health interventions, and more realistic policy modelling—without requiring any change in how patients experience a doctor’s visit.