Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jul 9, 2026
Open Peer Review Period: Jul 10, 2026 - Sep 4, 2026
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Guide to Healthcare Payers Data-Driven Risk Mitigation Strategies: Illustrative Tutorial on Mitigating Social Risk Factors
ABSTRACT
Healthcare payers are strategically positioned at the junction of population health, quality of care, cost, and big patient data while navigating the risky business of healthcare. Payers possess the incentives, resources, and capabilities to implement data-driven solutions and leverage advancements in artificial intelligence to improve health and financial outcomes. Rapid technological progress often fails to translate into impactful, successful realworld results. Yet, there is limited guidance for researchers and innovators seeking to develop artificial intelligence and machine learning interventions that are aligned with the operations of the fertile healthcare payer landscape. This tutorial presents a generalizable framework for the development of data-driven solutions as healthcare payer risk mitigation strategies. Using unmet social needs as an illustrative example, we demonstrate how a data-driven approach can mitigate the impact of social risk factors on healthcare payer operations while promoting health equity. This tutorial is a bridging resource to ultimately foster collaboration by facilitating the alignment of technology development and intervention design with healthcare payer processes while also providing value to policymakers, clinicians, and payers interested in the convergence of social determinants of health and data-driven risk mitigation strategies.
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