AI Medical Coding: Definition and Examples

LJ Acallar

November 7, 2025•14 min read

What is AI Medical Coding?

AI medical coding is the automated process of suggesting the assignment of relevant medical codes to digitized medical documentation, facilitating standardized billing and smoother reimbursement processes.

For decades, health systems and clinicians have gone from manually assigning the correct medical codes to outsourcing them to medical coders to ensure proper billing, yet everything was done with paper. While essential, this repetitive process has slowed down physicians from seeing patients due to the error-prone nature of the traditional billing procedure.

With the advent of AI in healthcare, claims processing and revenue cycle management (RCM) operations are enhanced, yielding higher efficiency gains. Technology that automates coding is being increasingly widely adopted to maintain medical relevance and fidelity.

In this article, we will discuss the benefits of AI-powered medical coding over traditional methods and look at real-world applications of AI for medical coding.

An infographic from Heidi titled "AI Medical Coding," showing features for on-demand coding support (asking AI to assist with medical tasks) and human-guided accuracy (AI suggesting relevant codes from notes for clinician approval).

Why is AI Medical Coding Important in Modern Healthcare?

AI in medical coding workflows is becoming more essential in keeping modern healthcare running efficiently. Since clinicians can focus on the patient-facing aspects of care, they no longer need to complete medical charting that consumes half of their average day. Free from this cognitive burden, they can dedicate their mental energy to engaging with patients, inquiring about their well-being, and listening fully.

Since its inception, digital technology has rapidly advanced. Its transformation from being clunky and expensive to intuitive and accessible eliminates the need for clinicians to spend valuable time onboarding with multiple levels of complex interfaces.

In today’s healthcare systems, cost is no longer the major barrier to adopting digital technology that streamlines administrative tasks. The main challenges now lies in interoperability between systems and the need for secure, trustworthy infrastructure that protects patient data. The more reliable the product is without sacrificing quality, the higher the likelihood of it being adopted in the care space.

Another factor accelerating the need for AI in medical coding is the growing shortage of qualified medical coders. The demand for skilled human coders outpaces the supply and this leads to backlogs and delayed reimbursements. AI helps fill the gap by ensuring coding is compliant, accurate, and consistent, thereby easing the pressure on care teams, even in high-volume settings like hospitals.

What Does the Future of AI in Medical Coding Look Like?

Artificial intelligence moves medical coding from post-documentation manual code selection to code-ready documentation. It is expected to drive the adoption of computer-assisted coding (CAC) in workflows to further the efficiency of revenue cycles. This means fewer denials in reimbursement claims, and in turn, allows clinicians to allot more time to see patients without rushing care.

To maintain ethical oversight in practice, clinicians remain essential as they take authority on edge cases that AI cannot cover, and this contributes to the anticipation that the hybrid model in care is becoming the norm.

To better understand the future of AI in healthcare, let's explore real-world applications of AI in medical coding.

AI Medical Coding Examples

Medical Coding AI Example 1: The Ask Heidi feature aids clinicians with on-demand guidance. With a simple prompt, whether a few minutes or a couple of hours, clinicians can quickly recall and confirm proper codes as Heidi accommodates queries such as “Add billing codes” or “Remind me of the top three antidepressant medication names in AU.”
Medical Coding AI Example 2: Heidi’s coding capabilities leverage AI to support clinicians in improving end-to-end documentation structure. It does not auto-submit or finalize codes, preserving clinician judgment. Heidi strengthens audit-readiness by aligning every note with care context while keeping humans in charge.

Medical coding and AI are poised to bring about higher clinical productivity. In high-volume specialties like urology, documentation accuracy directly influences coding precision and reimbursement. Advanced Urology’s leadership saw this clearly. As Dr. Neal Patel, CTO, shares, “Heidi reduces burden on our providers and increases accuracy of our clinical encounters.”

As a busy clinician, your primary responsibility is to take care of patients, not chase codes or fix rejected claims, which is why AI medical coding only works when the quality of documentation is high. With ICD-10 billing code support built into their workflow, the team shifted time from admin to care while keeping consistent and compliant notes.

“A huge lift on our documentation burden. Our providers save 1 to 2 hours a day with Heidi.” Those reclaimed hours align with measurable outcomes that support accurate coding and faster throughput. Ultimately, clinical quality and operational efficiency are reinforced in one move, thanks to Heidi.

How Does AI Medical Coding Work?

In modern medical coding workflows, advanced AI capabilities from tools like Heidi automate the scanning of digital documentation notes to suggest relevant diagnoses and treatments. It executes this process to assist clinicians while they maintain control over the final note output. Let us take a look at how AI-powered medical coding works with the leading AI solution in healthcare, Heidi.

Step 1: Start a Session

Start a new session and begin transcribing in the Heidi app by pressing Start transcribing at the beginning of the encounter. With the consent of your patient, Heidi will capture all salient medical details while you focus on the interaction. Heidi’s ambient capabilities run unobtrusively in the background on your computer or mobile device while it transcribes medical dialogue.

Should there be information you prefer not to say aloud, you can easily dictate it in the Context section, and Heidi will incorporate the details in your final note.

Step 2: Stop the Session

When the appointment ends, press Stop transcribing to conclude the recording. Heidi instantly processes everything discussed, then generates a structured clinical note that follows your preferred format and the style of your chosen template. For example, this medical billing and coding template supports accurate coding through precise identification of CPT modifiers to ensure alignment with future care interventions.

In this step, you can also search for specific clinical terms, but the need to make manual edits is minimized thanks to the accuracy of Heidi’s AI scribe. The standard of documentation is fully under your flexibility and control, so you retain the accountability in refining the note accordingly before saving or pushing it into the patient file.

Step 3: Review medical codes

Once your consultation is over, you can review the codes on the right sidebar, which displays as a panel when you click on the button that shows the total number of codes Heidi suggests. Each code is displayed as a card containing its name and ID to give you a clear overview of suggested options.

The codes are organized into tiers such as High, Medium, or Low relevance, so you can quickly assess which ones are most applicable to your note. Selecting the information icon on any code opens a detailed view of its meaning, context, and use. When full evidence is needed and to help you understand exactly why each code is recommended, clicking the transcript icon will take you directly to the relevant, highlighted section of the transcript that prompted the suggestion.

Step 4: Confirm medical codes

To confirm codes, simply check the box beside each one that applies. As you work through the list, the progress indicator changes color to show completion status. This color progression provides a quick visual cue so you can track your progress throughout the coding review process.

A red indicator means no codes have been confirmed, while orange signals that some have been selected. Green indicates that all relevant codes have been reviewed and approved. If all codes apply, there is a checkbox for you to click once, and all the confirmed codes will be modifiable, added at the bottom of your note.

Step 5: Modify medical codes

If a suggested code does not fit the context, you can easily replace it by hovering over the code, selecting Replace, and using the search bar to find a more accurate option. When a necessary code is not added to the list, simply choose Add new code, then search for the correct code to apply it to your note. If a suggested code is not relevant, hover over it and select Remove from list, then confirm your choice in the pop-up window.

You can review the full specification of any code before confirming your selection to ensure accuracy. Also note that removing a code only updates the coding list and does not alter the content of the original note.

Medical coding with AI will propel your productivity rates and increase your care capacity, all to allow a more personalized patient experience. No matter your practice’s size or setting, Heidi adapts to 200+ specialties with support for over 110 languages to help you practice medicine, not admin.

Reduce Medical Coding Errors with Heidi AI

If you work with a small care team and want to scale, Heidi’s plans will benefit you with features that set billing all into one place and minimize the need for claims scrubbing. With Heidi, not only will you get advanced document generation features, but the customization options will save your team real clinical hours that can be saved from admin and allocated back to patient care.

Heidi supports HL7 FHIR-based interoperability and can integrate alongside EHRs and other health IT systems. Built with privacy by design, here are other ways Heidi can help you in practice:

Primary Care: Heidi’s AI summarizes your SOAP notes into diagnostic and procedural codes for common conditions, and it alerts you when documentation is insufficient for code specificity.
Ancillary Care: Heidi flags mismatches between your clinical findings and documentation procedures. It reduces upcoding and undercoding as it aligns medical details with payer policies.
Emergency Care: In emergency and acute care workflows, Heidi helps reduce claim denials due to incomplete charting by flagging missing critical codes, like for trauma, drug administrations, and others.

With more than 1.8 consults transcribed weekly, Heidi delivers secure documentation that streamlines coding and billing, aligning with regional privacy standards. It is certified with ISO 27001 and fully compliant with HIPAA, GDPR, NHS, and the APPs, among other regulatory frameworks. Data never leaves clinician control, and audio from encounters is never stored.

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Frequently Asked Questions about AI Medical Coding

Can AI do medical coding?

Yes. With AI-powered tools like Heidi, medical coding can automatically assign codes from the documentation of clinicians. The hybrid approach in care workflows is growing in popularity, where clinicians validate what AI is built to assist with, so the workflow is more seamless and efficient.

How does AI contribute to enhanced accuracy in medical coding and billing?

AI capacitates clinicians in ways more beneficial than ever, especially in detecting inconsistencies between documentation and coding. All done in real-time, it minimizes human fatigue-related errors. For instance, Heidi’s AI capabilities help standardize clinicians’ documentation by adapting to the style and voice based on their final notes, ultimately saving hours of paperwork.

Will medical coding be replaced by AI?

No. Clinicians and medical coders focus on interpreting complex and ambiguous encounters, an area in care where human cognition is irreplaceable. The efficiency that AI brings to billing workflows only handles the repetitive majority of cases, but clinicians will maintain supervision. For example, the validation of AI-assisted outputs such as notes, letters, and claim forms remains under full clinician control.

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