Risk adjustment coding is a system mainly used by CMS to measure how complex a patient’s health is and to guess future healthcare costs. This model is important in value-based care, where payments depend on the risk level of patients, not just the number of services given. Chronic heart diseases often involve many health problems and different symptoms. These are usually coded within HCC categories that affect payment and care plans.
Since it was created in 2004, HCC coding has changed over time to better show patient health using diagnosis categories. In 2024, CMS introduced CMS-HCC Version 28. This update changed the categories and how they relate to real medical situations. The total number of HCC categories went from 86 to 115. There are more detailed groups, especially in heart care. For example, congestive heart failure is now split into five groups based on how severe it is and the ejection fraction. Doctors must document carefully to make sure coding is correct.
The update also reduced the number of ICD-10 diagnosis codes linked to HCCs. The codes went down from 9,797 in Version 24 to about 7,770 in Version 28. Around 2,300 codes no longer count toward risk adjustment. At the same time, 268 new codes were added. This means many conditions that used to add to risk scores might not count anymore. This could lower payments even if patients’ health stays the same.
An important number affected by these changes is the Risk Adjustment Factor (RAF) score. This score helps decide Medicare payments. RAF scores use patient information and HCC codes to estimate future healthcare use. Because many codes were removed or changed in Version 28, CMS expects a 3.12% drop in average RAF scores for Medicare Advantage patients. This change would save about $11 billion for the Medicare Trust Fund in 2024.
These changes affect healthcare groups financially, especially those treating patients with chronic heart conditions. For example, patients with atrial fibrillation or chronic systolic heart failure had big drops in their risk scores. Providers might get less money even if patient needs do not change. UnitedHealth Group said the new Version 28 model caused their early 2025 Medicare Advantage earnings to go down. This shows how the whole industry is affected.
Because of this, coding must be very accurate and documentation very detailed to avoid losing money. The detailed groups for chronic heart diseases require more careful recording. Providers and staff need to write down disease severity and related health issues clearly.
Artificial intelligence is now seen as an important tool to make medical coding more accurate and efficient. This includes HCC risk adjustment coding for heart conditions. AI systems look through electronic health records (EHRs) and find missing or poorly recorded chronic conditions. They then suggest proper HCC codes with the right details.
Companies like Navina and IMO Health have made AI tools that greatly improve the capture of chronic condition diagnoses, including those for CMS-HCC Version 28. Navina’s AI reportedly achieved a 106% year-over-year increase in capturing “V28-only” codes within the same patient groups. Providers who use the AI tool regularly saw a 45% higher increase in RAF scores compared to those who used it less. This shows how AI helps maintain and improve payments despite tough coding changes.
IMO Health points out that AI can make coding more reliable and clear by helping explain results and keep them steady. Their platform helps organizations move from CMS-HCC Version 24 to Version 28, focusing on more details and matching ICD-10-CM rules.
With AI using natural language processing (NLP), coding systems can read doctors’ notes written in free text and pull out key diagnostic information. This is very important in cardiology, where diagnosis is complex and modifiers affect coding. These challenges can cause undercoding or errors without AI help.
In value-based care models, it is very important to show a patient’s true health complexity through detailed and specific coding. This is needed not just for payment but also for quality reports and following rules. Missing or wrong coding of chronic heart diseases can cause big revenue losses and hurt patient management.
Some data shows how important AI-supported coding is. One client using AI identified nearly 579 missing chronic HCCs in 763 patient problem lists. This could have raised Medicare Advantage payments by about $1 million. Finding these conditions not only increases revenue but also helps align care with patient needs and rules.
The new CMS-HCC Version 28 requires more detailed data than before. It highlights the need to record heart failure status and control of arrhythmias carefully. Without AI, finding and updating this info across many patients takes a lot of time and is prone to mistakes.
AI tools also help by learning from past coding mistakes and billing denials. This reduces errors in the future. This learning is important because CMS often updates coding rules and payment methods.
Apart from improving coding accuracy, AI helps automate office tasks that support medical practice operations and compliance. Simbo AI is one company that uses AI to automate phone answering and front-office tasks. Though not directly involved in coding, this technology helps the practice work better and improves communication. This indirectly supports better documentation and patient involvement.
Staff often handle many jobs like scheduling, checking insurance, and answering patient questions about coverage or bills. AI-based front-office automation reduces stress on staff, shortens call wait times, and helps provide accurate information. When patients and providers communicate well, documentation gets better, which supports correct coding later.
Combining AI phone automation with electronic health records gives a complete approach to running a practice more efficiently. For example, automatic alerts about patient visits or chronic condition care can remind clinicians and staff to check or update documentation needed for risk adjustment coding.
Also, linking AI coding helpers with front-office workflows can make it easier to collect and check clinical and insurance data for heart patients. These connections help improve coding quality and make HCC coding more precise and accurate.
By using these steps, medical practices can better handle the changes in CMS-HCC Version 28 and increase the money they get from accurate risk adjustment coding.
Medical practice administrators, owners, and IT managers in the United States need to understand how AI affects risk adjustment coding for chronic heart conditions. The move to CMS-HCC Version 28 and the complexity of heart coding need tools that help make documentation more accurate, improve revenue, and keep up with changing rules.
AI can improve coding accuracy, cut down errors, and automate tasks. This helps the financial health of medical practices in value-based care. Using these technologies together with office automation, like Simbo AI’s front-office tools, gives practices two main benefits: better coding and smoother operations.
In a changing healthcare payment system, using AI in heart coding and practice workflows can help medical practices stay strong while giving care that matches patient needs and value-based goals.
Cardiology coding poses challenges such as high procedure volume and variation, modifier confusion, complex diagnoses with comorbidities, and documentation gaps, which can lead to under coding and compliance risks.
AI enhances coding by enabling real-time chart analysis, accurate modifier assignments, guideline-based code suggestions, recognizing comorbidities, and continuously learning from error patterns to improve future coding accuracy.
MediCodio is an AI-driven platform designed for cardiology billing that provides real-time CPT and ICD-10 suggestions, automated detection of procedures, modifier guidance, and risk adjustment support.
AI provides faster coding turnaround, fewer claim denials, improved charge capture, support for various cardiology procedures, and optimized revenue, enhancing operational efficiency.
Best practices include involving coders in AI training, integrating AI into existing EHR workflows, regularly auditing AI-suggested codes, aligning coding policies with AI logic, and leveraging dashboards to track performance.
Yes, AI systems like MediCodio are equipped to manage a wide range of cardiac procedures, including both invasive treatments and non-invasive tests such as EKGs.
AI analyzes the context of billing services to apply the correct modifiers, ensuring compliance and accuracy in billing for services rendered.
Yes, platforms like MediCodio are regularly updated to comply with CMS, CPT, and payer-specific guidelines, ensuring accuracy in coding.
No, AI is designed to assist coders by automating repetitive tasks, while human coders remain essential for reviewing and validating complex coding scenarios.
Yes, AI tools identify HCC codes and highlight opportunities to capture essential conditions, supporting organizations in optimizing revenue under value-based care models.
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