To be fair, some predictive analytics tools have attempted to account for SDOH factors. But these attempts often fail to provide value for clinicians because they stop at providing a risk score — without any context of why the patient is at risk or what can be done to reduce their risk. To be useful, we need more than predictive analytics. We need prescriptive analytics.
Prescriptive analytics differs from predictive analytics in that it provides actionable recommendations for how care teams can intervene to address patients’ risk factors, whether they be clinical or SDOH-related. To be clear, these recommendations are not meant to make decisions for clinicians, but they can ease their cognitive burden and give them direction on how to approach a patient’s care differently.
Regardless of whether value-based care becomes the law of the land, the transition to performance-based payment models will no doubt continue — and for good reason. As a nation we spend more on healthcare than any other country while achieving worse outcomes. It’s about time we incentivize both reducing costs and improving outcomes.
But we must also ensure that outcomes are improved equitably. Prescriptive intelligence, combining machine learning and SDOH data, can empower care teams to address the root causes of health inequities while still achieving the goals of value-based care.
About the author: Dr. John Frownfelter is the chief medical officer at Jvion.Back to HCB News
