Data analytics

SDOH: How Data Analytics and AI Impact Social Determinants

Now more than ever, Social Determinants of Health (SDoH) and their impacts are widely understood within the healthcare community. This month, our Director of SDoH and Product Development Tamara Carlton is highlighting the efforts various groups are making to track SDoH and their influence on society through analytics and AI–including the negative impact SDoH can have within the medical world.

Collecting and Analyzing SDoH Data

While data has been collected regarding SDoH and their outcomes, little to no progress has been made to utilize that data to drive positive results for people and communities. A huge opportunity for moving this effort forward lies in Artificial Intelligence (AI) technology. Simply put, AI is systems or machines that mimic human intelligence to perform tasks to better help and improve the way the world works. But how can AI be used in terms of SDoH?

As we know, factors such as education, employment, location, transportation, and more can have a huge impact on an individual’s health, making it difficult for some to receive the medical care they need. These factors are largely outside of providers’ control, regardless of health plans’ efforts to adjust. This is where AI can come in to the mix.

According to Healthcare IT News, with a combination of stats and data scientific methods, researchers and scientists can start to understand what is driving local variations in SDoH outcomes. AI tools can use these stats to improve incentives for providers to treat more patients, including those more difficult-to-manage patients with complex needs. Lisa M. Lines, a Senior Health Services Researcher, says “The idea is grounded in principles of risk adjustment. In healthcare, risk adjustment has been used for decades in managed care to provide incentives to both plans and providers to care for sicker patients.” She continues by saying, “What has only recently begun to be accepted in health systems is the outsized role of social factors in health outcomes.”

It can be broken down in to three points: boosting risk identification, improving patient outcomes, and eliminating health disparities. AI has a large role in each of these points, benefiting social determinants in a positive way.

Boosting Risk Identification

Addressing socioeconomic and environmental factors can impact an individual’s health. Using AI tools like machine learning has proven to accurately predict and recognize different life threatening diseases that are impacted by social determinants. It is important to understand tools that continue to assist in recognizing these risk identifications, as social and environmental factors continuously change worldwide.

Improving Patient Outcomes

For AI to be successful in improving patient outcomes driven by social determinants, providers need well-trained algorithms and extensive data addressing community needs. By addressing issues within communities and using AI to discover these, providers will see improved patient outcomes.

Eliminating Health Disparities

When providers realize the importance of recognizing the impact of SDoH, they can target and eliminate health disparities of their patients. “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,” says Rebecca Madsen, chief consumer officer of UnitedHealthcare.

By using AI such as predictive analytics and machine learning, SDoH studies and statistics collected by scientists and collective projects can make a positive change. While collection of data is a great tool, it is important to be able to create plans moving forward that use the correct data in the correct way to change the outcomes of SDoH.

Ready to see how MTM is leading the charge in addressing SDoH through quality transportation? Click here to learn more!


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