Unlocking Patient-Centric Prospective Risk Adjustment

Value-based care organizations are more aggressively establishing Prospective Risk Adjustment initiatives to supplement Retrospective Risk Adjustment programs. Payers and risk-bearing providers are concerned about Risk Adjustment Factors (RAF) accuracy because of heightened regulatory focus on overpayments and concerns about changing reimbursement models. This is driving investment into Prospective Risk Adjustment programs, which attempt to capture risk adjusted HCCs at the point of care, reflecting RAF scores more accurately in retrospective audits.  

In a recent webinar co-sponsored with RISE on “Patient-Centered Prospective Risk Adjustment: A Treatment Use Case,” poll data from attendees during the session revealed that nearly 70% had implemented prospective programs and another 25% were actively seeking solutions.  

As Prospective Risk strategies mature, health plan leaders are asking how to optimize Risk Adjustment functions. At Reveleer, we hear from health plans that struggle with provider abrasion and coding capacity, and we talk to providers who are battling outdated patient data, workflow interruptions and interoperability challenges.  

Patient-centric Prospective Risk programs are the most successful at tackling these challenges. Prospective Risk programs engage providers with in-depth clinical insights pre-encounter and at the point of care. Clinical insights such as undiagnosed risk adjustable HCCs can help providers improve condition management, as well as provider and patient satisfaction.  

Surfacing new clinical insights requires a holistic picture of patient care, which requires tapping clinical data outside the health plan or provider network.  

The Power of External Data and AI

Healthcare data is fragmented, making it challenging to get a full view of patient care journeys. On average more than 50% of care happens outside of the PCPs EMR, including specialty and acute visit data.  

For a complete picture of patient care, value-based care organizations need solutions that can tap into an established, nationwide network of clinical data sources to enrich what clinicians already know about their patients. This requires robust digital retrieval capabilities that can of aggregate and harmonize data from multiple sources to create a comprehensive view of patient health records.  

AI algorithms built on Minimal Effective Alternative Treatment (MEAT) standards then analyze clinical data to highlight potential undiagnosed conditions for humans to validate faster and more efficiently than people could find them on their own.

Getting all this data to one place allows value-based care organizations to design more patient-centric Prospective Risk Adjustment programs. Broader data sets improve the accuracy of predictive models, enabling earlier intervention and prevention of adverse health events. Clinicians can access a complete picture of a patient’s health to tailor treatment plans. Better data access supports personalized treatment strategies, improving patient adherence and outcomes.

RAF accuracy with prospective insights  

Patient-centered Prospective Risk Adjustment programs can also drive efficiencies downstream into Retrospective Risk Adjustment audits.  

Health plan Risk Adjustment functions already struggle with coder capacity and audit deadlines. Conditions treated, documented, and coded at the point of care ensure RAF accuracy and reimbursement transparency when reviewed by health plan coders retrospectively. This is especially crucial as value-based care grows.  

Patient-centered Prospective Risk Adjustment programs help bridge this gap by aligning health plans and clinicians around the common ground of the patient. The key is in depth and supplemental clinical data, which requires the ability to tap outside data sources, and deliver insights to where they matter most.  

To hear a replay with our most webinar on the topic of “Patient-Centered Prospective Risk Adjustment,” with Fierce Healthcare, click here.  

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By Anthony Polizzi, Senior Director, Solutions Architect, Reveleer. Based in New York, Anthony helps customers to solve data interoperability challenges and surface new clinical insights to improve care.