Coffee & Conversations Webinar Recap: The Truth about AI Coding and Automation in Healthcare

With the advancement of AI in coding, are we moving toward replacing coders? This is one of the interesting questions raised at our recent webinar, The Truth about AI Coding and Automation in Healthcare. This webinar was the first in our Coffee and Conversations webinar series that covers exciting and trending topics within the healthcare technology space featuring insights from subject matter experts.  

 Here are three highlights from our webinar speaker, Gary Bernklow, Senior Product Director at medaptus. 

1. AI, CAC, AC – Decoding the Acronyms 

 What is the difference between AI, CAC, and AC? These three acronyms all sound similar, but they’re not the same.  

 AI = Artificial Intelligence. Technology that enables computers and machines to simulate human intelligence and problem-solving capabilities​
 

CAC = Computer-assisted coding. A software tool that uses natural language processing (NLP) and machine learning to analyze clinical documentation and suggest appropriate medical codes (ICD and CPT) for charging​ 

​AC = Autonomous Coding. Leverages AI and/or logic to generate codes automatically and qualify the encounter as “final coded ready” without any human intervention.​ 

        2. Is AI Just Hype?

Using AI, organizations want to automatically be able to pull all the billable codes from patient encounter documentation. Sounds simple, right? The problem is that things can be open to interpretation in the medical field. ​ 

When using NLP, for instance, your AI tool must learn an individual’s specific language patterns to discern important information. “Cindy’s birthday comes before Stacey’s birthday”. AI might interpret that sentence to mean that Cindy is older than Stacey. However, the author might mean only that Cindy’s birthday comes before Stacey’s in the calendar year and actually Stacey is 3 years older. ​ 

Data-driven analyses, on the other hand, would look exclusively at both Cindy’s and Stacey’s birth date to determine who is older.​ 

While AI coding sounds like an ideal next step for the technology industry, there’s still a lot up in the air that can lead to missed charges, inaccurate billing, and inefficiencies. These need to be worked out before you try to implement AI coding.​ 

3. Important Questions to Ask

If you’re debating using CAC or AI coding, there are some very important questions to ask: 

1. What is the minimum accuracy rate you need to justify implementing a solution like this? 

1. What is your definition of accuracy? 

1. How much are you looking to improve your level of accuracy with your coding?  

1. Aside from the popularity of AI, how in-depth have you researched it?  

1. How much time do you have to dedicate to inputting data into a CAC system? 

Watch the Recording 

Get access to all of Gary’s insights on AI and coding automation. Watch the webinar on-demand today.

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