AI can help providers achieve better outcomes in value-based care models
The momentum of value-based care is about to accelerate. The Centers for Medicare and Medicaid Services has outlined an ambitious goal: to transition all traditional Medicare beneficiaries to a VBC plan by 2030 – a notable increase from the mere 7% recorded by Bain Research in 2021.
As more health plans, providers and members join VBC schemes, substantial amounts of clinical data will need to be managed effectively to monitor patient risk and quality of care.
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 contracting 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 clinical data of both quality and risk adjustment, and how healthcare providers can use AI tools to help patients be fully engaged in their care.
Q. You argue that AI has the potential to revolutionize risk adjustment. How?
A. AI can significantly transform risk adjustment within value-based care due to its ability to scan, analyze and synthesize vast 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 healthcare organizations are evolving by developing prospective risk adjustment programs that engage providers before interacting with members. Most are limited by the member data they have in-house, making it difficult to effectively engage providers with outdated information.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 healthcare 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-oriented function to a proactive workflow that can truly impact care.
Ask. You also told me that AI can synthesize high-quality clinical data and risk adjustment for better-informed healthcare decisions and earlier interventions. Describe how AI works to achieve this.
A. AI can help align risk adjustment and quality improvement programs by giving them a unified, longitudinal view of their members 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 lymphoma, bronchiectasis, and hypertension. After scanning data from across the health ecosystem, the AI system finds evidence that suggests 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 understandable patient summaries, linked to supporting clinical documentation. In this example, when this information is presented to healthcare providers at the point of care, the healthcare provider can review the proposed diagnosis and supporting evidence and then decide which diagnoses to include and how best to continue the patient’s care.
Risk and quality programs can then align with this better, more comprehensive data from their members and work more proactively with healthcare providers to improve patient care.
Q. How can Frhealthcare providers use AI tools to help patients be fully involved in their care?
A. By skillfully using AI tools, healthcare providers can empower patients to take on a more involved role in their healthcare journey, resulting in better outcomes and a greater level of engagement in their care.
AI allows healthcare providers to analyze patient data to formulate personalized health recommendations that meet individual needs and preferences, serving as the basis for guiding patients in making informed decisions about their healthcare.
By examining longitudinal patient data, AI algorithms can predict potential health risks and complications. This allows healthcare providers to proactively involve patients in preventive measures and interventions, reducing the chance of adverse consequences.
AI tools can also analyze patients’ communication preferences and tailor outreach via 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 healthcare outcomes through more informed clinical decisions, earlier interventions and more effective treatments.
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