Computer assisted coding CAC

  CAC coding enables healthcare organizations and health information systems to streamline coding processes by leveraging natural language processing, machine learning, and other AI in healthcare technologies.

Computer assisted coding CAC does not remove the necessity for medical coding experts to be involved in the health information management process. CAC technology can make medical coders more effective and increase clinical documentation improvement for health systems. Coders can review and revise the medical coding computer requirements, and cross check it against quality metrics and patient data standards in real time. 

Today, CAC vendors offer computer assisted coding technology that is most widely used in coding for outpatient services. Some medical specialties using CAC medical coding include radiology, cardiology, pathology and emergency medicine. 


One of the primary benefits of medical coding software is its ability to swiftly analyze EHR data and assign appropriate codes based on its intelligent medical algorithms. This swift automated process significantly reduces the time needed for manual code entry, enabling coders to handle larger volumes of data in shorter timeframes. Furthermore, the standardized nature of medical coding software ensures a higher level of consistency in code assignment, which is pivotal in reducing claim denials due to coding inaccuracies.

Ref https://www.foreseemed.com/computer-assisted-coding

Sample video https://youtube.com/watch?v=u5h6jw7NJBw

Around 2015, the CAC applications helped for a time per record reduction by 22 percent if compared with the non-automated coding process for ICD-10.

The average rate of claim denials can be as high as 23 percent, with a large portion of them rooted in missing or invalid data. Automated coding workflow helps prevent inconsistencies and errors which translates into higher chances of a bill being paid by an insurance company.


1. A medical document is submitted to the CAC engine from the EHR system, RIS (radiology information system), LIMS (laboratory information management system), other platforms or telehealth apps.


2. The CAC engine analyzes the text and makes code suggestions.


3. A certified coder manually reviews and edits the results.


4. Edits are processed to tweak the CAC engine based on the feedback. Depending on the technology type, the engine is adjusted either manually or automatically.


5. The reviewed codes are directed for further analysis or reporting to the EHR system, medical billing software, practice management system. There are three approaches to realizing this workflow — rule-based, AI-based, and hybrid

Rule based: Most medical coding tools are rule-based, meaning that they use if-then logic to link language patterns with associated codes.

AI based: AI tools take advantage of more sophisticated NLP methods and deep learning models trained on large amounts of data. They are capable of detecting language patterns on their own and of considering context when linking those patterns to codes from the vocabulary


Further reading including case study on AI based CAC using IBM Watson: https://www.altexsoft.com/blog/computer-assisted-coding/




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