The reason? The algorithm was trained on insurance claims data, and thus predicted which patients were likely to have higher care costs. Because systemic barriers to care — rooted largely in SDOH — have historically resulted in lower healthcare spending for Black patients, the algorithm ended up deprioritizing Black patients, perpetuating the existing racial bias in healthcare.
Risk scores like the ones in the study are pervasive in healthcare, and are commonly used by value-based care providers to determine which patients should be prioritized to prevent adverse outcomes. But as the study shows, failing to account for SDOH can leave these algorithms prone to bias against the most vulnerable and marginalized patients.
Bringing SDOH awareness to value-based care
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Value-based care providers shouldn’t have to choose between improving their overall patient outcomes and taking action to reduce health disparities. They can do both. But it will take more than just traditional predictive analytics.
In 2020, I co-authored a study in the American Journal of Managed Care that showed that machine learning, trained only on SDOH data, could predict patients’ risk for healthcare utilization to a high degree of accuracy. This is the way forward for value-based care.
The difference between the machine learning approach and the predictive analytics approach is that it’s focused on the root causes of poor health outcomes (SDOH), rather than their end result (higher care costs, as reflected by the insurance claims data). This enables healthcare providers to proactively address the SDOH factors driving patients’ risk, and by extension, the disparities we see in outcomes among socioeconomically disadvantaged groups.
Using public data from the Census and other government agencies, machine learning can predict which communities are most vulnerable to poor health outcomes, as well as the SDOH risk factors driving their vulnerability. This enables value-based health systems to strategically invest in community benefit programs, such as food delivery services or ridesharing services, that address the barriers to health in a given community.
Machine learning can also identify the SDOH risk faced by individuals. Through a process known as similarity clustering, machine learning matches patients with thousands of similar patients whose outcomes are already known, which can predict their risk and identify what their leading SDOH risk factors are. When you add in clinical data from the provider’s EHR, machine learning can provide a holistic view of patient risk that goes far beyond what’s possible with predictive analytics.