Fully explainable AI stands to revolutionize medical coding

By Ilyana Rosenberg

Are you continuously searching for medical coders to code your patient visits?  Even with the usage of Computer Assisted Coding (CAC), do you still find your department losing time and revenue from the manual and laborious processes required for proper validation? Have you ever been frustrated by the inability of your smart tool solutions to explain how it decided on the recommended medical codes? This article explains the current artificial intelligence (AI) approach to medical coding and offers a new groundbreaking alternative.

Medical coding dates back to the 17th century in England with the release of the first diagnosis coding system to monitor cause of death. We’ve come a long way since then as we are now on the 10th edition of a newer method of classification used worldwide: The International Classifications of Diseases (ICD). We have also expanded beyond just disease classification to include procedure and level of service codes for use in medical reimbursement. Today, there are over 68,000 ICD codes, over 10,000 Current Procedure Terminology (CPT) codes, and countless payer-specific and national coding guidelines to follow. However, even with this expansion in the complexity of codes and guidelines, the method by which the codes are assigned to a medical record has remained the same - manual by certified and specially trained medical coders.

Only in the late 1990s was a product finally released that would help coders with the ever changing and increasingly complex coding requirements. CAC technology, based on AI and machine learning, has made medical coding more efficient by reviewing medical records and suggesting the codes that should be assigned to the visit. But there are still two downsides: 1) manual review of codes by medical coders is nonetheless required prior to claim submission in order to select and validate the correct codes, and 2) there is no explanation behind the suggested codes. The “digital subconscious” associated with AI and deep learning leaves users without the coding rational thus questioning the results validity. Hence the name “Black Box” is commonly used to describe such AI approaches. While approaches can be accurate and prove successful in industries where explanation may not be critical, there is no room for this uncertainty in the healthcare industry.

As demand for coding explanations and medical coders increases, Nym offers a completely new AI approach that fully addresses coding needs. Nym’s autonomous medical coding solution offers a different innovative solution to the medical coding industry and overcomes these challenges for you. Unlike CAC solutions that exist in the market today, Nym’s technology provides fully automated and fully explainable medical coding. Nym’s engine uses a linguistic approach to capture the clinical picture of patient visits, apply all relevant coding guidelines (professional as well as facility), and seamlessly integrate the correct medical codes into the local practice management system. All in under five seconds. All with zero human intervention. And with a complete audit trail explaining the validation and reasoning behind every code selected by the Nym engine.

By understanding both the structured and unstructured text in the medical record, Nym accurately identifies what happened at the patient visit, and can therefore assign codes with full confidence. The engine takes a multi-step approach: 1) breaking down sentences to gather building blocks of the narrative, 2) relating sentences together for context and meaning, 3) compiling all chart sections for a complete clinical picture, and 4) applying coding guidelines for final and accurate coding assignment. Unlike black box AI technologies, this computational linguistic approach offers a white box alternative. For each chart the engine codes, a full audit trail is supplied explaining why those specific codes were assigned, drilling down to specific guidelines, the actual chart sections, and unique words used for identifying the correct codes.

Not only does Nym’s Clinical Language Understanding (CLU) engine offer the full suite of medical codes with a detailed rationale behind the code assignment autonomously and quickly, it also provides detailed insights around clinical documentation improvement and crucial metrics for improving your operational efficiency. Capturing the full clinical picture also enables reports on areas including lost charges, revenue opportunities, missing sections, and Relative Value Units (RVU) assignment at the facility and physician levels.

Does your organization have any initiatives surrounding AI? Are you looking for a cost-effective transformative approach to gain efficiencies in your mid-revenue cycle? Intrigued by this new concept of fully automated and fully explainable medical coding? Nym offers an impact analysis to demonstrate how this fully automated coding solution, with complete audit trail explanation, clinical documentation improvement insights, and operational metrics, works on your medical records. Read more about a real life implementation of Nym's technology >> REQUEST CASE STUDY.

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