Can Digital Tools Predict Future Health Risks in Family Medicine Using Ontomorph?

Abstract

Family doctors are trained to prevent disease, not just treat it. Yet prevention often depends on predicting what might happen in the future. Will this patient develop diabetes? Will that patient have a heart attack? Who is at risk for cancer, and who needs screening? In recent years, digital tools have emerged that attempt to answer these questions using patient data, genomic information, and machine learning. These tools project future health risks and simulate the effects of different interventions. This narrative review examines whether such tools may help family doctors prevent and manage chronic diseases. Ontomorph is used as a case example of a platform that proposes "what-if" simulations and ten-year risk projections based on a patient's genome, laboratory results, and lifestyle data. The review also considers the practical and ethical issues of using future-risk projections in primary care, drawing on recent literature. The evidence suggests that digital risk prediction may improve case finding, support shared decision-making, and enhance preventive care. However, challenges remain, including validation, bias, patient anxiety, workflow integration, and unclear liability. Family doctors should engage with these tools critically, ensuring that they serve patient care rather than adding to the burden of practice.