Learn how AI-powered targeted HCC optimization transforms retrospective risk adjustment, strengthens compliance, and elevates financial and clinical outcomes for health plans.
For health plans and providers alike, strong retrospective risk adjustment programs lay the foundation for success in value-based care. Highly efficient and accurate risk adjustment promotes fair compensation, incentivizes the delivery of high-quality care, and supports financial sustainability.
Health plans rely on technology to support their risk adjustment programs—from medical record retrieval to submissions. But not all risk adjustment software is created equal. Integrating AI into risk adjustment can power targeted HCC optimization and result in better financial, clinical, and compliance outcomes.
Being able to precisely identify and code chronic and high-risk conditions is essential to ensuring that health plans and providers receive adequate compensation to cover the anticipated cost of care. The precise identification and coding of hierarchical condition categories (HCCs) ensure that risk adjustment factor (RAF) scores accurately reflect the severity of a patient’s diagnosis. RAF scores, in turn, determine the allocation of reimbursements based on the projected healthcare needs of a patient population.
Unfortunately, risk adjustment coders spend a large portion of their time coding charts in search of missed diagnoses and supporting clinical evidence without the ability to prioritize their work. This broad, “shotgun” approach to coding results in too many conditions getting missed with many charts going unreviewed. As a result, payers are unable to capture the revenue they need to deliver improvements in cost and care quality required for value-based care success.
Using artificial intelligence (AI) and smart automation, coding teams can prioritize high-value charts and optimize HCC capture, transforming the retrospective risk adjustment process.
Targeted HCC optimization isolates known diagnoses and prioritizes the most complex member charts for coder review, ensuring superior HCC mapping and reducing errors. It lifts the burden of redundant and irrelevant tasks off coders and brings the most critical conditions to the forefront for focused and efficient review.
This streamlined approach helps coding teams capture a more comprehensive and accurate set of diagnoses with precision and speed, maximizing outcomes in three key areas:
To power targeted HCC optimization in retrospective risk adjustment, leverage technology that prioritizes the highest value charts based on diagnosis detection. This technology should also separate other charts with HCCs that are already substantiated or have no RAF changes to help coders better focus their time. There are several ways to manage this type of prioritization to maximize outcomes for health plans:
When health plans rely heavily on manual review processes, they risk errors and inconsistencies in their risk adjustment submissions. The result is suboptimal RAF scores, non-compliance with CMS guidelines, and lower reimbursement. Fortunately, the next evolution of retrospective risk technology has the power to transform risk adjustment for increased accuracy, efficiency, and compliance.
AI-powered targeted optimization helps resolve inaccurate coding, helping coding teams move faster and meet their financial and reporting objectives.
For example, the technology can identify instances in a diabetic patient’s medical record in which diabetes management was discussed but the diagnosis for diabetes for some reason was not captured at the point of care. This could be from structured or unstructured clinical data such as notes from consultations, lab results, or treatment plans.
The technology would then recommend the diagnosis for diabetes and map that to the appropriate HCC , prioritizing the highest value and most appropriate HCC—and the one with the greatest impact on clinical outcomes. It can also detect other likely chronic conditions such as COPD by looking at spirometry results and treatment plans, or hypertension by looking at mentions of hypertension management across the patient’s care journey.
Importantly, AI-powered technology should surface clinical evidence to coding teams, justifying the recommendation by referencing specific chart entries. This helps ensure coding teams can focus their time and provide quality assurance, increasing overall accuracy and compliance.
By integrating targeted HCC optimization into their retrospective risk programs, health plans and their coding teams can transform risk adjustment and achieve their value-based care goals:
Recently, Reveleer announced the launch of its next generation Risk Adjustment technology, making retrospective risk management faster and more accurate than before. On top of its comprehensive Risk Adjustment solutions which offer everything from lightning-fast record retrieval to advanced analytics, the newest iteration of Risk Adjustment introduces dynamic, targeted HCC optimization to help teams prioritize the highest value charts for risk adjustment.
Interested in how powerful AI solutions can transform risk adjustment through targeted HCC optimization? Schedule a demo to experience Reveleer’s Risk Adjustment suite for yourself.
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