Autonomous Coding vs Computer-assisted Coding - Part 2: Key Differences

By Hudson Schertz

This article is Part 2 of a two-part blog series, “Autonomous Coding vs Computer-assisted Coding.” For a close look at the evolution of medical coding and descriptions of computer-assisted coding (CAC) and autonomous coding, read Part 1: The Evolution of Medical Coding.

Autonomous coding and computer-assisted coding (CAC) were both developed to solve the challenges associated with manual coding. While CAC improves coder productivity, it falls short in a number of areas where autonomous coding succeeds. This can be attributed to some key differences between CAC and autonomous coding technology related to implementation, accuracy, coder-dependence, and scalability. Together, these differences directly impact the return on investment (ROI) that CAC and autonomous coding deliver to healthcare organizations.


Key Differences Between Autonomous Coding and Computer-assisted Coding


Implementing CAC software can be a difficult process. Modern CAC software is complex, and staff must change their established coding practices to fit within the new CAC workflow. Supporting this point, a 2021 Gartner guide to computer-assisted coding noted that “[CAC] implementations require significant commitment and engagement by already-overburdened medical coding and clinical staff.”1

Implementing autonomous coding does not typically require staff to change their established coding practices or get trained on how to use the software. Autonomous coding solutions can fit seamlessly into the existing coding workflow. They analyze medical charts, assign the correct codes, and then send charts directly to bill without a human coder ever seeing them. Charts that can’t be coded by the autonomous coding solution are typically sent to human coders, who then code the chart as they normally would.


CAC struggles when it comes to accuracy. Although CAC can identify key terms and phrases in physician notes, it runs into challenges with subjectivity, abbreviations, and ambiguities that result in incorrect code suggestions. When CAC assigns inaccurate codes, it can create bias for human coders and cause them to code less accurately. CAC’s struggle with accuracy is revealed in a study conducted by the American Health Information Management Association (AHIMA) and the Cleveland Clinic, which found that CAC alone- without the intervention of a credentialed coder- had a lower precision rate.2

Autonomous coding solutions leverage multiple subfields of artificial intelligence (machine learning, NLP, deep learning) to interpret complex clinical language and create a complete narrative of the patient visit. This enables autonomous coding solutions to consistently maintain high coding accuracy.


CAC is heavily dependent on human coders. Because CAC-suggested codes must be validated by a human coder, CAC solutions may not increase coder productivity significantly. Additionally, this dependence on human coders creates a risk of coding backlogs during holidays and when staff are on vacation or out sick.

Autonomous coding solutions are built to function independently of human coders. Because of the technology that powers them, autonomous coding solutions are capable of accurately coding charts and sending them to bill with no human involvement. By operating independently, autonomous coding solutions enable healthcare organizations to process charts even when they’re closed for a holiday or experiencing staffing issues.


CAC increases individual coder productivity, but it is not a scalable solution. Because CAC requires human coders to validate every patient chart, healthcare organizations that use it will need to hire additional coding staff when chart volume increases. 

Autonomous coding offers a scalable solution, as most solutions do not require humans to validate codes and can process hundreds (if not more) of medical charts per hour. If a healthcare organization’s chart volume increases, the autonomous coding solution simply increases compute power to meet the new volume.

Nym’s Medical Coding Engine

Nym has developed its own autonomous coding solution. Powered by clinical language understanding (CLU) technology, Nym’s medical coding engine takes provider notes within patient charts and uses them to accurately assign diagnostic and charge codes within seconds.

Nym’s engine does not require staff to change their coding practices or workflow, and it integrates seamlessly into existing enterprise IT stacks. When it comes to accuracy, Nym’s engine maintains coding accuracy over 95 percent and is quickly updated when new CMS guidelines are released to ensure continued compliance. Nym’s engine is also configurable to all payer- or site-specific guidelines and standard operating procedures (SOPs) and integrates with any electronic medical record (EMR).

Unlike CAC, Nym’s engine is fully automated and requires zero human intervention for charts it fully understands. Healthcare organizations who leverage Nym’s engine can constantly process charts, significantly reducing their risk of coding backlogs during holidays or staffing shortages. Nym’s engine is also a scalable solution. It processes thousands of charts per hour and easily handles increased chart volumes by increasing compute power.

Learn more about Nym’s medical coding engine and how it’s reducing the time and cost associated with medical coding and billing for hospitals, health systems, physician offices, and urgent care providers.

The Bottom Line

Medical coding is an essential step of revenue cycle management, but current processes continue to rely largely on manual labor which is time-consuming, expensive, and prone to error. CAC and autonomous coding offer solutions to the challenges of manual coding, but there is a significant difference between their respective impacts. While CAC can improve coder productivity, it historically falls short of expectations due to issues with implementation, accuracy, coder-dependency, and scalability. Autonomous coding can solve the challenges associated with both traditional manual coding and CAC, delivering an excellent solution for hospitals, health systems, and physician offices looking to transform their medical coding processes. While the monetary ROI of autonomous coding will depend on the vendor, healthcare organizations can expect a significant improvement in medical coding operations and revenue cycle performance that goes beyond the impact of CAC.

Interested in learning more about autonomous medical coding with Nym? Check out these helpful resources!

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