How AI and machine learning can help predict SDOH needs

In his March HIMSS22 session entitled " Using Explainable AI to Mitigate SDOH Contributors to Risk," Dr. Jim Walton, president and CEO of Genesis Physicians Group, will describe how his organization, along with Medical Home Network, avoided potential pitfalls of applying AI in underrepresented populations and trained machine learning models on the population and data sources to fairly and efficiently identify high-risk members. A. Recently, population-health-management strategies have begun to incorporate evaluations for patients' social needs connected to SDOH, as well as interventions addressing these needs. Care managers and social workers, working within accountable care organizations, and physician provider networks now incorporate a short series of SDOH interview questions with patients identified as high-risk for poor healthcare outcomes or unnecessary future healthcare expenditures. The care-management team members work to connect patients and their families to community-based organizations that offer solutions for many of the social needs identified. Now, we see the evolution of care-management services that are more agile, where interventional staff are just as likely to identify and respond to the social needs that many patients express as they are the clinical disease management.