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Machine learning is getting better at predicting things. There are now algorithms that improve the detection of diabetic retinopathy, predict the onset of sepsis, and help determine a critically ill patient’s risk of dying. But a piece of wisdom from Warren Buffet comes to mind: “Predicting rain doesn’t matter. Building arks does.” Even the most impressive algorithm is relatively useless if it doesn’t allow us to build better “arks” to address the medical disorder or complications that the digital tool identifies. And building the best healthcare interventions requires that clinicians not just identify the right signs, symptoms, and biomarkers, whether they be high cholesterol levels, elevated A1c, or a lump in a woman’s breast. It requires we understand what’s happening in patients’ everyday lives outside the clinic, the so-called social determinants of health (SDOH), and then using that data to inform treatment.
A great deal has been written recently about SDOH. Health professionals are slowly beginning to realize that we cannot “remove health and illness from the social contexts in which they are produced,” according to Simukai Chigudu, Oxford Department of International Development, University of Oxford.1 That begs the questions: What social issues are mostly likely to influence our patients’ clinical course and what do we do about them? How can AI help alleviate the impact of these issues?