My Journey Before and After AI in Medical Coding

Medical coding is the process of translating healthcare diagnoses, procedures, services, and equipment into standardized numeric or alphanumeric codes. These codes are crucial—not just for billing insurance claims, but also for organizing patient records in a consistent, searchable format. Every code links to a dollar value, enabling insurance companies to identify and reimburse services accurately. Over time, this coded data becomes an invaluable resource, helping healthcare organizations analyze trends, optimize operations, and ultimately improve care delivery.

In my own experience, whenever we bring a new client onboard, we typically analyze six months of historical billing data. This helps us identify frequently used procedures, spot patterns, and develop SOPs tailored to that specialty or facility. These insights then inform the rules we use to implement AI systems for that client.

Historically, medical coders have relied heavily on three key resources: CPT (Current Procedural Terminology), ICD (International Classification of Diseases), and HCPCS (Healthcare Common Procedure Coding System) codebooks. CPT and HCPCS codes are used to document the procedures and services performed on patients, while ICD codes describe the diagnoses that justify those services. Accurate use of these codes is not only essential for smooth claims processing—it also helps track broader public health issues like pandemics or regional epidemics.

When I first entered the field, medical coding was a completely manual process. Physicians used paper charts, which were later scanned into TIFF images or PDFs. Coders like me would review these documents and manually assign CPT and ICD codes using physical codebooks. Eventually, scanned documents were integrated into early software systems, but we were still reading charts manually and referencing hardcopy books for every code.

As electronic health records (EHRs) became standard, platforms like Alife, Coderyte, and Cerner emerged, allowing coders to log in and work directly within digital environments. Eventually, software tools like 3M were embedded into these platforms to streamline the search process and improve coding efficiency. Even then, though, everything hinged on the human coder’s interpretation. We’d scan the chart, determine the right codes, and enter them manually—often facing fatigue, complexity, and the risk of inconsistencies. Two coders could easily produce different results from the same chart depending on how they interpreted it.

Fast forward to today, and the landscape looks completely different.

Six months ago, I joined RapidClaims, where AI is transforming how medical coding is done. At first, I was skeptical—everything was new, and I wasn’t sure how AI could interpret charts the way a human could. But I quickly learned that AI tools, especially those powered by Natural Language Processing (NLP), are incredibly effective at analyzing clinical documentation. Our team of coders now plays a collaborative role: we help identify which sections of a chart are relevant and where key details live. That input is then used by our data science team to build algorithms that extract and assign codes.

Once algorithms are built, we run extensive testing—checking hundreds of charts to ensure the AI is generating accurate codes. This process is repeated and refined until we hit a benchmark of 98% accuracy. Only then is it considered ready for live coding.

The benefits are huge. What used to take hours can now be done in minutes. The AI can process charts in bulk, and coders now act as reviewers or auditors rather than manual processors. We catch errors, provide feedback, and help retrain the system when needed. In many cases, the AI produces more consistent results than human coders ever could—especially with complex surgical cases or lengthy documentation.

The impact goes beyond speed and accuracy. AI significantly reduces operational costs by decreasing the reliance on manual labor. It also lowers denial rates by embedding compliance rules and CMS guidelines directly into the system, reducing audit risks. And because everything is digitized, it unlocks massive potential for data analysis—helping healthcare leaders understand trends and make informed decisions that improve patient outcomes.

Of course, AI isn’t perfect. Its effectiveness depends on the quality and diversity of the data it’s trained on. The more charts we use to train the system, the better it performs. And while AI handles many charts with ease, complex cases still require human expertise. That’s why human coders remain a vital part of the process—not just as backup, but as strategic partners who guide, audit, and continuously improve the system.

Looking ahead, the future of medical coding is likely to be even more autonomous. As AI systems mature, some client workflows may be handled end-to-end with little to no human input—especially those with simpler, more routine cases. Integration with EHRs will become deeper and more seamless, allowing data to be pulled directly from clinical workflows and processed at scale.

The role of the medical coder is evolving. We’re no longer just data entry specialists—we’re becoming quality control analysts, algorithm trainers, and strategic contributors. And honestly, it’s exciting. AI has made our work faster, more accurate, and more impactful than ever before.

Reflecting on my journey, I can confidently say that AI hasn’t replaced coders—it’s empowered us. It’s given us better tools, freed up our time, and elevated our role in the healthcare ecosystem. And this is just the beginning.

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