Why computer-assisted coding software contributes to failing revenue cycle management goals

While virtually every aspect of a practice's operations affects revenue cycle management, one area holds massive power to either negatively or positively impact it—medical coding and billing.

No matter what area of medicine a practice specializes in, a healthy, stable revenue cycle is crucial to its success.

While virtually every aspect of a practice's operations affects revenue cycle management (RCM), one area holds massive power to either negatively or positively impact it—medical coding and billing.

Many practices implement technology to boost their medical coding and billing capacity. Practices may consider using computer-assisted coding (CAC) systems when searching for the right solution.

However, if improving your rev cycle and seeing ROI are the goals, using a modern artificial intelligence (AI) solution to automate coding will yield better results.

What is CAC vs. AI automation?

While some use these terms synonymously, CAC and AI automation are vastly different. Understanding the differences is imperative as their impact on your revenue cycle varies.

Medical coders use legacy CAC tools to assist with coding workflows to marginally improve coder productivity, whereas AI automated medical coding can code charts independently without human intervention.

CAC tools affect workflow and productivity, but they don't automate processes. They use basic natural language processing (NLP) to analyze medical documents and identify key terms and phrases. It then suggests codes it believes would be correct for the treatment or service provided, basically acting as an assistant to a coder.

AI automation takes it a step further by mimicking human intelligence. It takes hundreds of millions of coded encounters, a practice’s historical coding data, learns from it, and decides how it should code accordingly. Using AI to act as an actual medical coder opens the door for enhanced efficiency, quality, and accuracy of a practice's coding operations.

CAC and AI-based medical coding automation: pros, cons, and limitations

When discussing the pros, cons, and limitations of both technologies, it's best to start at the beginning: implementation.

Implementing a traditional CAC system requires substantial configuration and training for users. Setup configurations must explicitly lay out custom rules that represent the nuance of clinical context, formats, and edits. As the number of clinicians, facilities, and payors a practice works with rises, so does the number of rules the CAC system needs. After configuration, team members need extensive and ongoing training on how to use the software properly. This process can sometimes take years to get up and running.

CAC software does help to alleviate the burden of medical coding teams experiencing a high volume of work. However, it still requires team members’ time and energy to verify suggested codes and make changes where necessary.

On the other hand, AI automation doesn't require manually inputting and managing custom rules. As previously mentioned, it can go into a medical practice's existing coding data and learn from it. The system then uses this information to act as a human would and interpret clinical interactions, take differences in clinicians' notes into account, while simultaneously layering on payor guidelines—making it much easier on medical coding staff.

When it comes to accuracy, CAC tools aren’t very effective because their automation levels are limited, and practices need to spend time and energy verifying suggested codes and ensuring they configure back-end algorithms properly. Because AI automation works as a human would, it can generate more work at a higher accuracy level.

Like any technology, AI has its limitations. Due to the complex nature of medical coding, AI technology still requires actual humans to handle more in-depth issues like managing and resolving denied claims.

However, AI is intelligent enough to know when it can and cannot successfully code. If it determines something needs the attention of a medical coder, it will pass it along to them. This process allows for increased accuracy and efficiency throughout coding and billing operations.

How CAC tools can cost practices

Often, practices implement a CAC system to improve their revenue cycle and do not see the expected financial results and outcomes. While CACs have the potential to boost productivity, they can cost practices in the long run.

A significant factor contributing to lost money is time. Because CAC tools don’t code charts independent of medical coders, coding teams still spend time on simple, routine coding tasks that could be automated.

Another aspect is speed. Quicker coding of charts accelerates the revenue cycle and positively improves accounts receivable (A/R). When using a CAC system, coding still requires approval from staff members, which leads to longer turnarounds, sometimes taking days.

When coding teams spend time on unnecessary tasks, they experience decreased payment velocity and complete fewer charts, both of which negatively impact a practice’s revenue cycle.

ROI of AI-based medical coding automation

With an AI automated coding solution, practices can deliver more accurate charts ready for billing in near real-time.

Fast coding turnaround with high levels of accuracy makes it easier for practices to obtain timely reimbursements, decrease days in A/R, and limit the chances of denied claims. Increased speed also helps practices operating under a value-based care model reduce revenue risk.

Autonomous, accurate coding also allows practices to hire fewer coding employees while maintaining efficient operations. Existing staff members can focus on the more complicated tasks AI can’t automate and oversee their resolution.

Reach RCM goals with AI automation

While CAC systems may help coders increase productivity, they’ll never act alone to code charts. If your practice is looking to improve cash flow, CAC systems may not deliver the desired results.

With AI, practices can leverage the power of automation to reach their RCM goals. Using the right AI medical coding automation solution can usher in a new era of increased efficiency, accuracy, and quality.

Amit Jayakar is the Vice President Commercial Operations of Fathom, a Tarsadia and Founders Fund backed company that uses deep learning to automate medical coding. At Fathom, Mr. Jayakar drives the demand generation and pipeline for the commercial organization.