Machine Learning Uses Social Determinants Data to Predict Utilization

A machine learning algorithm accurately predicted inpatient and emergency department (ED) utilization using only publicly available social determinants of health (SDOH) data, showing that it’s possible to determine patients’ risk of utilization without interacting with the patient or collecting information beyond age, gender, race, and address. That’s the major finding of a study recently published in the American Journal of Managed Care. By now, the healthcare industry is well aware of the connection between the conditions in which someone lives and works and her physical health. The researchers note that sociodemographic status, racial and ethnic disparities, and individual behaviors directly correlate with an increase in the prevalence and incidence of chronic diseases. Healthcare organizations have made a concentrated effort to reduce health inequalities and address social needs, but this can be challenging.