Artificial intelligence (AI) and value-based care (VBC) are both regarded as transformative forces within healthcare. They have the potential to supercharge precision medicine, curb skyrocketing healthcare costs, and deliver the experience and outcomes from healthcare that patients truly want. But beyond their shared promise, they also share the common issue of struggling to gain real traction in healthcare systems.

AI’s rollout in healthcare is still scattershot, with no unified strategy for targeting high-need areas or consistently assessing its impact. VBC, meanwhile, faces hurdles in the data processing that’s essential for reshaping how care is paid for, organized, and delivered. The result is that health systems, payors, and patients are stuck waiting for AI and VBC to live up to their promise. We see a way forward in leveraging the strengths of each to overcome the challenges of the other.

AI creates truly integrated care systems

A key strategic pillar of VBC is the formation of integrated health systems. These are designed to enhance value, particularly in the management of complex patient conditions, through frictionless combination of multi-specialty input, easy coordination of community services, and deployment of resources across large geographic areas. However, the variety and complexity of relevant patient data and siloing of data means that most health systems remain fragmented, with frequently negative implications for care quality. We propose that AI can be a de-facto integrator across data sources and systems.

GenAI models leveraging natural language processing (NLP) capabilities have the computational power to extract, synthesize, and interpret vast amounts of disparate and multi-modal data, including clinical notes, genotypic, and social determinants data. AI can also lighten the administrative burden associated with pulling together this data from different parts of health systems that do not normally interact with each other. Together this will create a far more holistic view of a patient’s care journey through an integrated system and identify critical junctures for intervening to improve outcomes. In this way, AI acts as a co-pilot to augment clinician decision-making and create high-value integrated systems.

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