Medical coding means changing patient diagnoses, procedures, and services into standard codes like ICD-10-CM, CPT, and SNOMED CT. These codes are important for billing, payments, reporting, and analysis in U.S. healthcare. Getting the codes right is very important to get proper payments and follow rules like HIPAA and Medicare. But medical coding is hard because medical terms are complex, coding rules change often, and codes must fit the specific situation.
For hospital leaders, clinic owners, and IT managers in the U.S., making sure coding is correct while keeping costs down is always a challenge. Using AI to help with coding can cut down on manual work, lower mistakes, and improve how money flows. But picking the right AI tool means knowing what each tool can and cannot do.
General LLMs like GPT-3.5 and GPT-4 can understand and write natural language well. They learned from many kinds of books, articles, and websites, so they can create text for many topics and languages. But they don’t have the deep medical knowledge needed for exact medical coding.
A 2023 study by Mount Sinai showed that basic LLMs got only about 34% exact matches for ICD-10 codes and 50% for CPT codes. This happened because of several problems:
Dr. Jingqi Wang from IMO Health said that without specific medical knowledge, these models “struggle with medical coding accuracy and often make wrong or fake codes.” This happens even with big models and lots of training because they lack medical rules and guidelines.
Also, general LLMs can’t always tell real facts from made-up information or follow the many coding rules needed to meet U.S. laws and financial rules in healthcare.
To work better, medical coding AI needs to use special medical knowledge and data. IMO Health, a company that works with clinical terms and AI coding, showed that adding deep clinical knowledge to LLMs makes coding much more accurate.
Systems like ICD-10-CM, CPT, SNOMED CT, LOINC, and RxNorm are the base for medical coding in the U.S. IMO Health’s terminology covers millions of clinical ideas in 24 areas and has about 20% more synonyms than the Unified Medical Language System (UMLS). This helps the AI match terms with the right codes.
When these terminologies are part of the AI, it better understands medical vocabulary used in U.S. healthcare. This leads to much higher exact coding accuracy—IMO Health reports up to 92% accuracy for ICD-10-CM, while generic LLMs get only 34-55%.
IMO Health adds special editorial guidelines and mapping rules developed over many years. Experts with more than 440 years of combined experience contribute to this knowledge base.
This knowledge limits AI from making up wrong codes by only allowing codes that follow industry and insurer rules. It also helps create extra codes and scores for risk adjustment and value-based care payment methods used in the U.S.
Fine-tuning LLMs with carefully labeled clinical data helps them understand medical language better for coding. IMO Health’s AI experts use prompt engineering to add 22 coding rules directly into the AI’s instructions. This guides the AI to follow the rules.
Retrieval-augmented generation (RAG) lets the AI check trusted terminology databases in real time. Instead of just guessing codes, the AI selects from verified options. This lowers mistakes, makes AI decisions clearer, and reduces costs because the AI is used only when needed.
By using LLMs only for complex cases and coding simple ones directly, accuracy improves from 82.9% to 90%, and computing costs go down.
Good, standard data is needed for AI to learn and work well in medical coding. IMO Health follows United States Core Data for Interoperability (USCDI) version 4 and will through at least 2028 to meet U.S. rules.
Data is handled to protect patient privacy following HIPAA, providing clean and safe data for AI training and testing.
AI is improving not just coding accuracy but also daily healthcare office work and money cycle processes. IT leaders and practice managers find that AI automation can cut down on administrative work and make daily tasks easier.
Companies like Simbo AI focus on automating patient calls and answering services. This supports backend coding automation by managing patient communication and front-office tasks.
Simbo AI uses conversational AI to handle patient calls, schedule appointments, and answer basic questions. This reduces the work for staff and keeps communication steady and on time without needing humans.
Good front-office automation helps avoid appointment mistakes and supports patient satisfaction, which indirectly helps with accurate coding by making sure visits and procedures are recorded and scheduled correctly.
When AI handles patient contacts and coding well, doctors and coders can focus on care and complicated cases. Combining domain-specific AI with workflow automation that links to Electronic Health Records (EHRs) and practice software reduces documentation mistakes that hurt coding.
AI automation lowers operational costs by managing routine communication and data work. Automating simple coding and front-office tasks helps healthcare groups use staff better and speeds up claim submissions and payments.
Automated workflows include built-in compliance checks and standard documentation steps during patient registration and visits. This reduces human mistakes and missing information that can complicate coding and helps meet U.S. rules.
Healthcare leaders thinking about AI for coding and automation should consider these points:
Using AI in medical coding means understanding that large general LLMs alone do not meet U.S. accuracy and compliance needs. Adding clinical terms, editorial rules, and fine-tuning is necessary.
Healthcare groups that pick AI coding solutions should focus on clear explanations, good data, and following rules to improve revenue and coding quality. Combining coding AI with front-office automation like Simbo AI’s phone systems offers a practical way to make healthcare work better, faster, and cheaper.
Balancing technology with skilled human checks can help U.S. medical practices improve coding accuracy, get proper payments, and keep good patient care records as healthcare keeps changing.
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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