HCC optimization made smarter with AI

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.

The impact of missing high-risk conditions

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.

The power of AI in targeted HCC optimization

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:

• Regulatory impact: Targeted HCC optimization yields the most accurate HCC capture, reducing errors in retrospective risk adjustment to ensure compliance.

• Financial impact: Prioritizing high-value chases helps ensure plans receive the maximum reimbursement for the population they cover.

• Clinical impact: When HCCs are captured for the highest risk populations, plans can ensure they have data and resources to target quality improvement initiatives.

Methods of HCC value optimization

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:

• Chart optimization: This method automatically deprioritizes charts with no RAF uplift potential using diagnosis detection technology. That way, coding teams can focus resources on charts with the highest potential financial and clinical impact.

• HCC-MAO-004 suppression: When an HCC has already been captured in administrative data, or in a previously submitted MAO-004 submission, AI-powered risk adjustment technology can automatically capture and deprioritize it to reduce unnecessary and duplicative code review.

• Dynamic validation: When a coder validates a specific ICD code within an HCC category, AI-powered technology can suppress all other ICD codes mapping to the same or lower HCC, saving time and reducing errors. This also ensures coders focus on more critical diagnoses and documentation gaps.

Use cases: enhancing accuracy, efficiency, and compliance through targeted HCC optimization

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.

Top benefits of HCC optimization for health plans

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:

• Enhanced efficiency, lower administrative burden: Targeted HCC optimization powered by AI drastically increases coder productivity, supporting coder teams with relevant insights and automating redundant tasks through intelligent processing. Coders can focus on charts needing human intervention and quickly validate AI-recommended diagnoses to produce optimal results in short timeframes.

• Fewer coding errors: Health plans can improve the accuracy of their retrospective risk projects and submissions with the support of AI-powered technology. Targeted HCC optimization means that the most relevant diagnoses are captured in advance of submission deadlines, increasing coder coverage. Coders can also validate, approve, or override AI-generated recommendations, ensuring full control over the coding process.

• Compliance: By increasing the accuracy of coded diagnoses, health plans can gain peace of mind when it comes to the accuracy of their retrospective risk submissions. Plus, automated workflows help plans meet strict reporting deadlines without compromising accuracy.

• Financial performance: Health plans leveraging AI-powered targeted value optimization reap financial benefits through optimized reimbursements and compliance. By prioritizing high-value charts with RAF potential, they maximize revenue capture through enhanced reimbursement rates.

• Clinical outcomes: Finally, more accurate HCCs lead to better patient care outcomes. Coding teams can surface potential undiagnosed conditions which can help target quality improvement initiatives.  

Getting started: transforming retrospective risk adjustment

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.