Through research the healthcare community has learned that non-clinical influences drive up to 80 percent of clinical outcomes. This means that treatments, therapies, and medications prescribed by providers aren’t the primary driver of a patient’s health – instead it’s issues like access to healthy foods, transportation, and other factors that are the primary drivers of successful patient outcomes.
These factors, collectively known as social determinants of health, are an increasingly frequent topic of conversation for healthcare professionals. But this conversation is just the start of a much bigger discussion about how innovative solutions like machine learning and artificial intelligence could be a driving force in improving overall population health.
“We use AI to identify, within a population, individuals who have mission critical gaps in compliance with their treatment plans,” shares Mary Jane Konstantin, Senior Vice President and Head of Business at HGS Population Health Management Solutions, on the HIMSS Innovation That Sticks podcast. “So as an example [there’s] an asthmatic who’s not consistently taking the right medications – we can glean that information from claims data [or] pharmacy data and reach out to that person to understand first of all, why we’re seeing a pattern of noncompliance and address their particular issues.”
While addressing the usage of AI in overcoming obstacles as a result of SDOH, Konstantin also talks about the importance of taking an individualized approach in examining population health and the need for pairing the right technologies with the right patients.
“The most important component is to remember that [we’re] working with individuals and we need to treat them as individuals and not make broad brush assumptions about what they’re going to need and what the specific opportunities are to improve their living condition and health status,” says Konstantin.
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