Leveraging Community Health Workers and Social Determinants of Health for Predicting Emergency Department Readmissions

As the capabilities of machine learning have developed, more researchers and health care providers are begin-ning to consider applications for health informatics to improve health care outcomes and address issues of health equity. The Centers for Medicare and Medicaid Services considers 30-day readmission rates to the Emergency Department (ED) to be an “outcome of care” measure. Such measures show how well a hospital is doing in preventing complications, educating patients about their care needs, and helping patients make a smooth transition from the hospital to home or other care facilities. While certain readmissions are medically necessary, hospitals usually aim to decrease the rate of 30-day ED readmissions by decreasing the number of avoidable unplanned revisits. This work is an evidential study that demonstrates the positive impact of integrating Community Health Workers (CHWs) and Social Determinants of Health in decreasing the 30-day unplanned hospital ED readmissions at Sinai Health Systems. Using data from the Sinai Urban Health Institute, we compare predicting the readmissions of patients with and without data pertaining to Social Determinants of Health (SDoH), characterize the improvement in predictions and discuss lessons learned in the process. We show that when patients are simply engaged by CHWs, regardless of the content of those conversations, we can increase the predictive accuracy of our classifier by 5%. We use this result to make recommendations for improving patient care and discuss limitations and future work. Importantly our work points directly to the human connection between patients and CHWs as an important feature in the readmission rate.