Healthcare only accounts for a small percentage of a person’s overall wellbeing and health, but external factors such as socioeconomic status and access to food are difficult to capture and leverage within the healthcare system. Machine learning models may help bridge that gap. Research from Regenstrief Institute and Indiana University Richard M. Fairbanks School of Public Health shows that using the most basic raw data created the best risk prediction models for primary care doctors to identify patients who may need some help.
“There is so much data available on social determinants of health, but the challenge is turning it into something healthcare providers can use,” said project leader Joshua R. Vest, Ph.D., MPH, Regenstrief research scientist and IU Fairbanks School of Public Health professor. “Results from our analysis suggest the simplest approach to creating a prediction model may be the most effective.”