Article Highlights

  • AI is transforming medical coding. Artificial intelligence can now participate directly in the coding process, but human coders remain essential for verifying context, applying complex guidelines, and ensuring coding integrity.
  • The coder’s role is evolving from doer to validator. As AI tools generate suggested codes, coders must audit and validate the output, elevating the coder’s professional impact.
  • Training must evolve to match this transformation. With AI tools integrated into the coding process, coders require training that improves their skills in critical thinking.

“AI is not a trend; it is a transformation.”

This line from a recent podcast has stayed with me. The episode wasn’t about health information management or medical coding, but the statement couldn’t be more relevant.

Artificial intelligence isn’t a passing phase, it’s a fundamental shift in how we work, learn, and think. If AI is here to stay, what does that mean for the day-to-day realities of medical coding? How will it reshape skill attainment for both emerging coders and seasoned professionals? And as a person deeply involved in shaping the future of medical record coding skills training, how must I adapt my own approach to ensure efficient and effective transfer of skills to the next cohort of professionals?

Academic teaching of medical coding has always had a structured approach. In ICD-10-CM diagnosis coding, for example, learners must first understand the classification system, then master its rules and guidelines (all while recognizing that both are constantly evolving), and only then start to solidify their training by being challenged to code progressively more complex cases.

How is AI Changing the Coding Process?

Today, AI is an active participant in the coding process. In the context of diagnosis coding, it can review documentation, identify clinical concepts, and suggest ICD-10-CM codes, particularly when the documentation is explicit and complete. Yet, the technology remains imperfect. AI can misread ambiguity, ignore context, or miss critical coding guidelines that human coders instinctively review.

As a result, the coder’s role is shifting from assigning codes to auditing and validating AI’s work. The key question for any AI generated code is no longer only “Is this the correct code?” but “Did the AI understand the documentation accurately and apply the rules correctly to arrive at that code?”

How is AI Changing Coding Skills Training?

As the coder’s role shifts, skills training for coders will also need to shift if we are to prepare coders for the changes in the work they will be doing. Practice with AI tools and approaches need to be part of the skills training. This will integrate critical thinking, the ability to challenge what the AI-engine is reporting, to allow a coder to effectively evaluate the AI output, ensuring it accurately reflects the documentation and complies with applicable definitions and guidelines. For example, instead of testing whether the correct codes are selected in an assessment question, the coder might be presented with AI output for the question. It is then up to the coder to determine whether some, all or any of the codes are correct and the rationale for these decisions.

(Critical thinking is a skill like any other and it can be learned. Libman Education’s Practical Coding Experience platform provides carefully selected and progressively more difficult real-world cases where coders apply critical thinking to arrive at the best answer.)

A Final Word on the Impact of AI on Coders and Coding

Ultimately, AI will not diminish the importance of medical coders—it will redefine and elevate it. As automation takes on repetitive tasks, human expertise will be vital in applying complex and sometimes seemingly conflicting rules while maintaining coding integrity. Integrating AI-generated examples into skills training helps coders learn to question, verify, and refine what AI produces. By doing so, organizations gain the best of both worlds, efficiency from technology and assurance from the judgement of a skilled coder.

An Example of AI getting it wrong
Let’s look at an example of this dynamic in action, reviewing an AI-generated code set for an ophthalmology case. The 68-year-old patient with a hypermature cataract in the right eye—also had hypertension and type 2 diabetes.

– The AI output generated the correct code for the hypermature cataract by following the index guidance. Under the main term Cataract, subterm hypermature the cross-reference stated to see cataract, senile, morgagnian type, and the coding was correct as an age-related morgagnian-type cataract.
– The output also included a code for the hypertension and type 2 diabetes without complication.
– The diabetes code selection was problematic, as the AI engine coded diabetes without complication, and since the patient had a cataract, diabetes should not be coded as without complication. Under the main term Cataract, diabetes the cross reference is to see Diabetes, cataract. When referencing the main term ‘Diabetes,’ the first subterm is ‘with,’ and under this the subterm ‘cataract’ is noted and the code for ‘diabetic cataract’ is given. Getting to the correct code involves following the “with” guideline, since the cataract is a subterm under ‘with,” a cataract in a patient with diabetes’ defaults to ‘diabetic cataract’ unless the provider explicitly states the diabetes and the cataract are unrelated. Absent this documentation, in this case the code for diabetic cataract should be assigned and the diabetes without complication code is incorrect.

The AI-generated codes had followed one rule but overlooked another.


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