The healthcare system in the United States is changing how providers get paid for care. Instead of paying for each service, value-based care pays healthcare groups based on how well patients stay healthy and how well chronic conditions are managed. An important part of this new system is Hierarchical Condition Category (HCC) coding. HCC coding shows how serious and complex patients’ illnesses are. Recent improvements in automation and artificial intelligence (AI) have made HCC scoring more accurate and faster. This affects how money flows and how population health is managed.
This article looks at how automated HCC coding affects financial results, care quality, and daily work in medical practices across the U.S. It focuses on technology tools that help revenue cycle managers, practice owners, and IT staff.
HCC coding is a way to adjust risk created by the Centers for Medicare and Medicaid Services (CMS). It groups patient diagnoses into categories that can predict future healthcare costs and care needs. Each HCC matches clusters of chronic or serious conditions like diabetes, heart failure, or cancer. These groups get Risk Adjustment Factor (RAF) scores that show how complex a patient’s health is.
Providers use HCC codes to explain patient risk clearly. This is important for Medicare Advantage plans and other value-based systems where payment depends on how complex patients are, not how many services they use. Good risk adjustment means providers get paid fairly based on real care needs.
For example, providers who care for patients with higher HCC scores get bigger capitated payments because those patients need more resources. According to UASI, a healthcare consulting group, improving RAF scores by just 0.1 can bring about $1 million more in revenue for every 10,000 patients. This shows how important risk adjustment accuracy is for financial stability.
Healthcare groups face the challenge of keeping clinical documentation complete and up to date. HCC scores must be reassessed every year because Medicare resets risk scores annually. If doctors don’t update stable or ongoing conditions, patient complexity may be underreported, which lowers payment.
The MEAT framework — Monitor, Evaluate, Assess, Treat — helps doctors and coders document diagnoses enough to support HCC coding rules. Good clinical notes make sure coders assign the most specific and serious ICD-10 codes, which link to the right HCC categories.
Studies show that missing or incomplete documentation and coding cause big revenue losses. For example, the American Academy of Professional Coders found that more than 40% of active chronic conditions are left out of medical records, causing wrong HCC coding. When healthcare groups don’t document well, they risk losing money and facing compliance problems.
Doing HCC coding by hand takes time and mistakes happen because coding rules are complex and often change. AI-powered automated coding tools are now used more to improve accuracy and speed. These systems use natural language processing (NLP) and machine learning to read large amounts of clinical documents and find relevant chronic conditions and their seriousness.
SIT MD Medical Billing Services reported a case where HCC coding accuracy rose from 82% to 97% in six months after adding AI-assisted coding software and training coders. This led to an 18% increase in risk-adjusted payments and 25% fewer claims denied. Audit risks also dropped by 40%, which helps with compliance.
Automated HCC tools help providers by:
These improvements lead to more steady cash flow and less paperwork for practices. This is important in the busy and limited-resource U.S. healthcare system.
Besides affecting money, HCC coding helps with managing population health. Correct risk grouping lets healthcare groups spot high-risk patients and use resources wisely. By knowing patient complexity and chronic illness levels, care teams can make targeted programs, lower avoidable hospital stays, and encourage preventive care.
For instance, eClinicalWorks, a health IT company, offers tools that help small independent clinics watch weekly HCC scores, track doctor performance, and find areas to improve without needing their own coding teams. This gives wider access to managing population health with data on patient risk.
Also, better risk adjustment supports value-based contracts where quality measures and money depend on managing population health results. In this way, accurate HCC coding helps match payment to patient-focused care and supports sustainable healthcare.
CMS updates the HCC model now and then to include new clinical data and cost trends. The latest CMS-HCC Version 28, released in 2024, made big changes to code mappings and weights. It removed 2,294 diagnosis codes from payment and added 268 new ones, many for rare conditions.
Because of a step called “constraining,” related HCCs like diabetes with and without complications are scored the same in Version 28. This may lower RAF scores for patients with complicated diabetes compared to old versions. CMS expects a 3.12% average drop in RAF scores for Medicare Advantage patients, which could save about $11 billion for the Medicare Trust Fund.
These changes mean healthcare providers and Medicare Advantage Organizations (MAOs) must adjust how they document and code carefully. Experts say it is important to keep checking patient health status often and use smart coding tools to keep up.
Partners in healthcare technology also work to fix accuracy problems in HCC coding. The SSI Group and RCxRules teamed up to create the HCC Coding Edit Suite. This helps big health systems improve revenue cycle work under risk-based contracts.
This partnership answers studies that show missing or wrong HCC codes cause big payment losses. By automating how these gaps are found for coder review, healthcare groups get better RAF score matches with real patient health. Also, automated and compliant coding lowers regulatory penalty risks.
Stephen Gorman, CEO of RCxRules, says that combining automation with useful data improves financial chances and supports providers in Medicare Shared Savings, Medicare Advantage, and Managed Medicaid programs.
Artificial intelligence and automated workflow tools are important for healthcare leaders who want to improve revenue cycles and clinical work. AI systems do more than assign codes; they mix multiple data sources, clinical language systems, and billing rules that change.
Advanced systems use:
These AI features help make coding standard, reduce manual work, and ensure that coding follows rules and is accurate. For example, IMO Health showed that using clinical vocabularies with large language models (LLMs) raised ICD-10-CM coding accuracy to 92%, a big jump from 34-55% with general models. This lowers false positives, boosts coder trust, and improves payment results.
Automation also helps update workflows automatically to match CMS’s regular changes in HCC rules, like the changes in CMS-HCC Version 28. EHR systems can send alerts to remind clinicians to capture needed documentation, making yearly updates easier to keep risk scores right.
With these technologies, healthcare IT managers can make revenue cycles smoother and less prone to mistakes while helping clinicians meet documentation needs without too much extra work.
As value-based care grows quickly, getting risk adjustment right is more important than ever for U.S. medical practices. Using automated HCC scoring tools helps with:
One example is a primary care network that used AI HCC software and coder retraining. It got an 18% boost in reimbursement and cut audit risks by 40%. This shows real financial gains from using advanced coding tools and workflows.
Automated Hierarchical Condition Category scoring is an important part of revenue cycle and population health management in U.S. healthcare. Its accuracy directly affects provider payments and how well value-based care works.
By using automated coding software, AI, and updated coding models like CMS-HCC Version 28, medical practice leaders, owners, and IT staff can improve finances, follow rules, and manage patient care better. Using both technology and good clinical documentation is becoming the usual way to succeed as healthcare changes.
General-purpose LLMs struggle with accuracy, often producing errors without domain-specific support. They lack the specialized training on clinical terminology required for precise medical coding, leading to imprecise or even falsified code generation.
Incorporating structured, domain-specific clinical terminology enhances LLMs by providing rich, standardized vocabularies and mapping logic, which significantly improves coding precision, reliability, and reduces errors compared to out-of-the-box LLMs.
IMO Health’s knowledge layer combines advanced clinical terminologies, editorial guidelines, mapping logic, and AI tools to fine-tune LLMs, producing highly accurate, explainable, and trustworthy medical coding outputs that align with clinical practice.
The IMO Health AI solution achieves up to 92% accuracy on ICD-10-CM coding, outperforming standard LLMs that reach only about 55% accuracy, demonstrating considerable improvement in medical coding precision.
IMO Health utilizes advanced prompt engineering, retrieval augmented generation (RAG), fine-tuning with curated datasets, and AI agent orchestration to improve coding accuracy, reduce hallucinations, and increase explainability.
RAG enables LLMs to retrieve relevant clinical codes from IMO Health’s terminology APIs, reducing hallucinations and errors by narrowing code generation to selecting pre-existing candidates, thus boosting accuracy and lowering computational costs.
AI agents built on LLMs call upon IMO Health’s tools and APIs for terminology normalization and guidelines, transforming coding from a black-box output to an explainable process with clear rationale, increasing coder trust and acceptance.
IMO Health maintains a curated, comprehensive clinical terminology with updated mappings and editorial guidelines driven by decades of expert clinical informatics experience, ensuring clean, standardized data for reliable AI model training and usage.
By pre-processing and covering most diagnoses through terminology alone and selectively engaging LLMs for complex cases, the solution optimizes resource use, improves overall accuracy by over 7%, and significantly lowers operational costs in medical coding workflows.
Incorporating HCC scores into AI coding automates accurate risk adjustment coding critical to value-based care reimbursements, streamlining workflows, increasing revenue capture, and enhancing population health analytics without manual efforts.
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