Using SDOH Data to Enhance Artificial Intelligence, Outcomes

Boosting Risk Identification

To understand a patient’s full health record, addressing the socioeconomic and environmental factors that impact an individual’s social determinants of health is an important step.

According to New York University researchers, machine learning can accurately predict cardiovascular disease and guide physicians to select treatment options. However, by factoring social determinants of health into the equation, providers can better serve diverse groups.

Currently, cardiovascular disease is responsible for almost a third of all deaths worldwide and disproportionally impacts those in lower socioeconomic communities. Additionally, the increase in cardiovascular disease is partly impacted by the social determinants of health.

“Cardiovascular disease is increasing, particularly in low- and middle-income countries and among communities of color in places like the United States," Rumi Chunara, the study’s senior author and associate professor of biostatistics at NYU School of Global Public Health and of computer science and engineering at NYU Tandon School of Engineering, said in a press release.

"Because these changes are happening over such a short period of time, it is well known that our changing social and environmental factors, such as increased processed foods, are driving this change, as opposed to genetic factors which would change over much longer time scales," Chunara continued.

By incorporating social determinants of health into artificial intelligence, machine learning technologies can better identify risk factors for chronic diseases. A patient’s health can be severely impacted by socioeconomic and environmental factors. Addressing these factors could significantly improve patient outcomes.

Improving Patient Outcomes

A patient’s health is heavily impacted by factors outside healthcare facilities. Whether these factors are due to income, where the patient lives, or their access to healthcare, by addressing the root cause of the problem, providers can improve patient outcomes.

“Our health isn’t taking place in the doctor’s office—it’s where we live, where we work, and where we play,” Amy Andrade, former assistant vice president of research at Meharry Medical College and founding director of Meharry Medical College’s Data Science Center, said during an episode of Healthcare Strategies, an Xtelligent Healthcare Media podcast.

“You have to step back and look at those influences. If providers and health systems don’t have access to this information, then they’re not seeing the true picture of what’s influencing health. You can’t just look at it in a siloed fashion.”

For artificial intelligence to be successful in improving patient outcomes with social determinants of health, providers need well-trained algorithms and extensive data addressing community needs.

 “You’ve got to look at your particular health system and what’s plaguing you. For instance, in urban areas it’s the prevalence of underlying chronic diseases. In other places, it may be cancer,” Andrade said.

“The second step is, you’ve got to ask yourself why. If you peel back the onion, you’ll get to the root cause. So now you have these large amounts of data, and that’s where AI comes in.”

By identifying and addressing underlying issues within a community concerning social determinants of health, providers will see improved patient outcomes.

Eliminate Health Disparities

As a provider, it’s important to understand the impact that addressing social determinants of health can have on an individual’s overall health. By identifying socioeconomic and environmental barriers patients face regarding their health, providers can take steps to eliminate these disparities.

The social determinants of health have come into the spotlight with the COVID-19 pandemic highlighting how low-income and diverse populations of people are disproportionately impacted by the virus. The impact was associated with factors such as lack of access to healthcare, close living conditions, etc.

“Around 80 percent of your health is determined by things that are not your genetics. There are things more such as what’s going on in the rest of your life, what we call social determinants of health — social, economic, gender orientation, and other markers that sometimes can lead to inequality,” said Rebecca Madsen, chief consumer officer of UnitedHealthcare, told HealthITAnalytics during an interview.

Providers such as UnitedHealthcare are trying to improve how their organizations respond to social determinants of health needs. With predictive analytics, UnitedHealthcare created an advocacy model designed to identify a patient’s needs and connect them with community resources.

“We know that so much of an individual’s health is determined by what happens outside of the doctor’s office. And we look at that as an organization, a chance to redefine a traditional insurance model, especially for the most vulnerable,” Madsen said.

By connecting individuals to proper community resources, providers can improve patient outcomes, especially for underrepresented groups. Overall, SDOH data helps providers enhance artificial intelligence and patient outcomes while improving risk identification and eliminating health disparities.