The momentum of value-based care is poised to accelerate. The Centers for Medicare and Medicaid Services has outlined an ambitious objective: to transition all traditional Medicare beneficiaries into a VBC arrangement by 2030 – a notable increase from the mere 7% recorded in 2021 by Bain research.
As more health plans, providers and members enter VBC arrangements, substantial volumes of clinical data will need to be managed effectively to oversee patient risk and care quality.
Jay Ackerman, president and CEO of Reveleer, a quality improvement and risk adjustment technology and services company, has deep knowledge of the healthcare landscape, VBC contract models and the technologies behind the scenes. We interviewed him to discuss the potential of artificial intelligence to revolutionize risk adjustment, how AI can synthesize both quality and risk adjustment clinical data, and how providers can use AI tools to help patients fully engage in their care.
Q. You contend AI has the potential to revolutionize risk adjustment. How?
A. AI can significantly transform risk adjustment within value-based care because of its ability to scan, analyze and synthesize massive amounts of data into clinical insights that can improve patient care.
Traditionally, risk adjustment in value-based care has functioned as an audit mechanism, ensuring accurate reimbursement for health plans based on the risk profile of their members.
However, some value-based care organizations are evolving by developing prospective risk adjustment programs that engage providers before member interactions. Most are limited by the member data they have in-house, making it difficult to effectively engage providers with outdated information.
Integrated with external, clinical data sources such as health exchanges, pharmacies and out-of-network specialists, AI can create a complete picture of a patient’s health. When these insights are pushed to providers at the point of care, risk adjustment shifts from a retrospective, audit-centric function into a proactive workflow that can really influence care.
Q. You also told me AI can synthesize both quality and risk adjustment clinical data for better-informed healthcare decisions and earlier interventions. Please describe how AI works to accomplish this.
A. AI can help to align risk adjustment and quality improvement programs by giving them a unified, longitudinal view of their member and presenting clinical insights to providers at the point of care.
For example, AI analyzes data for a patient with known diagnoses of non-Hodgkin’s lymphoma, bronchiectasis and hypertension. After scanning data from across the health ecosystem, the AI system finds evidence to suggest the patient could have three new potential diagnoses: congestive heart failure, aortic atherosclerosis and stage three chronic kidney disease.
AI can then translate this information into digestible patient summaries linked to supporting clinical documentation. If this information is presented to providers at the point of care, the provider in this example can review the suggested diagnosis and supporting evidence, then decide which diagnoses to add and how best to proceed with the patient’s care.
Risk and quality programs then can align around this better, more comprehensive data across their members and work with providers more proactively to improve patient care.
Q. How can p roviders use AI tools to help patients fully engage in their care?
A. By proficiently harnessing AI tools, providers can empower patients to assume a more engaged role in their healthcare journey, resulting in enhanced outcomes and heightened levels of involvement in their care.
With AI, providers can analyze patient data to formulate personalized health recommendations that align with individual needs and preferences, serving as a foundation for guiding patients in making informed decisions regarding their healthcare.
By scrutinizing longitudinal patient data, AI algorithms can predict potential health risks and complications. This enables providers to proactively involve patients in preventive measures and interventions, reducing the likelihood of adverse outcomes.
AI tools also can analyze patients’ communication preferences and customize outreach through email, text messages or phone calls, ensuring effective, timely communication and cultivating a more robust patient-provider relationship.
Health plan members benefit from improved access and care outcomes through better-informed clinical decisions, earlier intervention and more effective treatment.
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