AI Medical Coding Still Needs Human Expertise
Key Takeaways
AI-generated coding results require human validation to confirm that codes accurately reflect the documentation, applicable coding guidelines, and services provided.
AI validation helps identify coding errors, documentation gaps, and system configuration issues while providing feedback that can improve the platform’s performance over time.
Validation should begin before implementation through workflow review, provider testing, and comparison of AI results with coder-assigned codes, then continue after go-live as documentation practices and coding requirements change.
Experienced coding professionals are essential because AI may misinterpret medical history, future orders, service timing, or information documented in different sections of the medical record.
Healthcare organizations remain responsible for the accuracy and compliance of submitted claims, even when coding is generated by an AI platform or outside vendor.
Ongoing review, prebill edit correction, and coding support for excluded encounters can help reduce denials, repayment risks, missed revenue opportunities, and compliance concerns.
Artificial intelligence is becoming a larger part of medical coding. Healthcare organizations are exploring AI tools to improve efficiency and help coding teams manage growing workloads. These tools can offer value, but their results still require careful review.
Medical coding depends on more than finding words in a clinical note and matching them to a code. Coders must understand the full context of the encounter. They review the documentation, evaluate coding guidelines, and determine whether the selected codes accurately represent the services provided.
AI validation provides the oversight needed to help healthcare organizations use coding technology with greater confidence.
What Is AI Validation?
AI validation is a comprehensive review of AI generated coding results to determine accuracy and completeness. Ensource works with the healthcare organization and its AI vendor to confirm that the technology accurately captures documented services.
When an error is identified, the validator investigates how the AI interpreted the information and reached its decision. The findings are then shared with the healthcare organization and the AI vendor so the platform can be adjusted and future results can improve.
This process helps strengthen coding accuracy over time. It can also reveal areas where documentation practices or system configuration may be limiting the technology’s performance.
Why AI Coding Requires Validation
Every healthcare organization has different services and workflows. Provider documentation also varies by specialty and practice setting. Payer requirements can add another layer of complexity.
Because of these differences, AI coding requires organization specific configuration. A platform may need to learn where information is stored in the electronic health record and which coding rules apply to a particular service.
Organizations should also look closely at how AI accuracy is measured. A reported accuracy rate may only represent the claims the technology can process. As Jenny Cox, President of RCM Solutions at Professional, explains, “AI vendors may promote accuracy rates of 80 to 85 percent. However, if approximately 30 percent of claims cannot pass through the AI engine, that accuracy rate may represent only about 70 percent of the organization’s total claims.” The remaining encounters still require support from experienced coding professionals. Healthcare organizations also need to understand which services are excluded and how those claims will move through the coding process.
AI can misunderstand the timing or meaning of information within a note. For example, a provider may order a laboratory test for a future visit. An AI platform could interpret the order as a service performed during the current encounter and assign an additional charge.
The technology may also select a diagnosis from a patient’s medical history when the code should come from the current assessment. It may miss a more specific diagnosis documented in another section of the note.
Experienced coders recognize these distinctions. Their clinical and coding knowledge allows them to evaluate the entire record rather than relying on a single phrase or field.
Supporting the Full Implementation Process
AI validation can begin before an organization goes live with a coding platform.
During the early stages, Ensource works with the healthcare organization and the AI vendor to understand the coding environment. This may include reviewing provider workflows and determining which encounters are appropriate for AI coding.
Testing often begins with a limited number of providers. In one current implementation, the organization began with 20 providers. Ensource receives a daily report that compares the AI results with the work completed by the coding team.
Each difference is reviewed to determine what caused the error. The team then provides feedback that can be used to improve the platform.
Ensource can also complete a pre go live audit and provide recommendations for enhancements. During the initial go live period, the team can review 100 percent of AI coded encounters. This allows concerns to be identified before the organization increases the volume of records processed through the technology.
Ongoing Review Builds Trust
Validation should continue after implementation.
AI performance can change as providers adjust their documentation or new services are introduced. Coding requirements also evolve. Regular review helps identify new patterns before they lead to a larger volume of inaccurate claims.
Ongoing support may include reviewing a percentage of AI generated claims to ensure continued accuracy. Ensource can also correct prebill edits and provide full coding support for services that are excluded from the AI engine.
This can be especially valuable for organizations with limited internal resources. Many coding departments are already managing staffing shortages and heavy workloads. They may not have the capacity to investigate why an AI platform selected the wrong code or determine how the system should be corrected. Cox emphasizes that active oversight must continue throughout the process, explaining, “Everyone in the industry talks about having a human in the loop, but being looped in is not enough. You need human expertise at every step of the process because AI still requires significant oversight.” Ensource provides experienced coding professionals who can complete that work and communicate directly with the organization and its technology vendor. The team also analyzes clinical documentation alongside AI coding results to identify documentation concerns that may be affecting performance.
Providers Remain Responsible for Submitted Claims
Healthcare organizations remain responsible for the claims they submit, even when an AI platform or outside vendor generates the coding.
An inaccurate code can lead to a denial or repayment obligation. It can also create compliance concerns during an audit.
AI does not remove the need for quality assurance. Organizations need a process for confirming that automated coding reflects the documentation and follows applicable guidelines.
A strong validation program creates an additional layer of protection. It also gives leadership clearer insight into how well the technology is performing.
Experience That Supports Better AI Results
Ensource’s team includes seasoned coding and auditing professionals with experience working alongside coding technology and natural language processing tools dating back to 2003.
That experience matters because successful AI implementation depends on more than technical configuration. The platform needs input from professionals who understand medical coding and documentation.
Ensource helps healthcare organizations evaluate AI results and strengthen the processes surrounding the technology. The goal is to improve accuracy while supporting compliance and building confidence in the claims being submitted.
As AI becomes more common in medical coding, human expertise will continue to play a central role. Validation provides the oversight needed to turn an AI platform into a more reliable part of the coding workflow.
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