It’s nearly impossible to turn on or read the news without hearing a story about artificial intelligence (AI). While it seems like it’s a completely new technology, in fact,we have been co-existing with bots for many years through the use of automated systems, interacting with chat bots when contacting customer service departments, and of course, with some of the most well known AI personalities, Siri & Alexa.

 

AI technology is reshaping the landscape of many industries, and it’s poised to have a significant impact on the healthcare industry. AI solutions are already being deployed throughout many areas of the revenue cycle – patient scheduling and registration, eligibility verification, charge capture, claims & denial management, payment collection, patient communication and of course medical billing and coding.

 

Over the past two decades, much of the medical coding has been performed using Computer Assisted Coding (CAC) technology. CAC is an early form of AI primarily utilizing natural language processing (NLP) to assign CPT, ICD-10-CM & HCPCS codes to encounters. While the use of CAC has continued to grow since its inception, autonomous coding solutions are now positioned to replace CAC technology.

 

In speaking with coding professionals across the country, I have noted that often autonomous coding is thought of as the next version of CAC, when in fact, the technology is very different from CAC in its design and functionality.

 

Let’s take a look at only a few key differences between CAC and autonomous coding.

 

1. CAC is a tool that relies heavily on human intervention to arrive at correct codes, while autonomous coding solutions can code many encounters in seconds with no human intervention

 

2. CAC requires reports with a predefined structure and cannot easily adapt to changes in language patterns. AI can process reports with unstructured data and can detect language patterns and consider context.

 

3. CAC is primarily driven by NLP, focusing on the identification of specific words and phrases to suggest codes, while relying heavily on rule-based coding. Autonomous coding solutions utilize machine learning, deep learning and algorithms to assign codes.

Machine learning is a process in which computers learn from data without being explicitly programmed. Deep learning takes this process a step further using multiple layers to recognize complex patterns in data. Deep learning models are taught using a large historical data set to train algorithms to ensure the coding engine produces the most relevant predictions, capturing the subtle variations and coding style of the provider. This process of using large amounts of historical data is what makes achieving a high direct-to-bill percentage possible.

 

4. In a typical CAC workflow, reports are sent to the coding engine, the engine reads the text and provides code suggestions. A coder then reviews the coding engine output and makes any necessary changes. If a repetitive error is noted, a rule may be created to correct the issue. This can be a time consuming process, resulting in hundreds and even thousands of rules being established over a course of time. With an autonomous coding solution, reports are sent to the coding engine, coded within seconds and sent directly to billing. Any encounters that cannot be coded by the engine, will be sent to a human coder for review. As coders modify an assigned code or add or delete codes, the model is trained and re-trained in real-time each time a coder makes these changes.

 

 

Conclusion

 

While traditional CAC workflows still require considerable manual intervention and rule-setting, autonomous coding solutions stand out with their real-time learning capabilities. Not only do they streamline the coding process, but they also continuously refine their accuracy with each human intervention. The convergence of technology and human expertise in this domain signifies a promising step towards more accurate and efficient coding in the future.