A machine learning algorithm accurately predicts inpatient and emergency department utilization using only publicly available social determinants of health (SDOH) data. Health IT Analytics brought this interesting information to us in their article, “Machine Learning Uses Social Determinants Data to Predict Utilization.”
The ability to determine a patient’s risk of utilization without interacting with the patient or collecting information beyond age, gender, race and address – is now possible.
By now, the healthcare industry is well aware of the connection between the conditions in which someone lives and works and her physical health. Socio-demographic status, racial and ethnic disparities and individual behaviors directly correlate with an increase in the prevalence and incidence of chronic diseases. The analytics have proven it.
The social determinant most associated with risk was air quality, which had a relative value more than twice that of income, which was the second determinant most associated with risk. Both air quality and income were more important to the decision-making ability of the model than age, gender or ethnicity.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
