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7 Ways coding automation is vital to capturing reimbursement


Automation can play a large role in your coding strategy.

coding | © Khemmenat - stock.adobe.com

© Khemmanat - stock.adobe.com

Capturing correct reimbursement is crucial for any physician’s practice. Unfortunately, coding staff shortages, chart backlogs, and guideline changes often lead to denied claims and revenue leakage. While a myriad of solutions have been developed, only coding automation can truly solve the problem. Below are seven ways this AI technology improves revenue capture. But first, it’s important to understand the core reason that coding is a weight on many practices today.

The key problem in today’s medical coding process

When you think of the core challenges facing coding, many issues come to mind – cost, completion time, the chronic shortage of coders, and the administrative strain to your practice. The underlying problem, however, concerns mindset: practices accept the status quo without realizing there are alternatives. Administrators and physicians traditionally have accepted the “fact” that a heavy coding burden is a necessary chore, even though it’s time-consuming, cumbersome, and often frustrating. The truth is, it can be simple, straightforward, and highly accurate.

The latest coding automation technology, based on deep learning AI, has revolutionized the physician coding process. I get it, this statement sounds hard to believe. The best way to illustrate the impact of AI coding is to look at how another groundbreaking technology transformed the way we live. It’s a commonplace item you likely have in your home: the washing machine. Though this metaphor may sound a bit unusual, bear with me and you’ll soon see the connection.

Before the washing machine, cleaning clothes could span anywhere from a half day to an all-day event. Similar to the time-consuming nature of coding, people simply accepted that this was the way things were. But when the washing machine entered the picture, it caused a complete paradigm shift. Suddenly, a task that took hours upon hours could be completed in 30-60 minutes. This radically transformed people’s lives – unlocking more time, energy, and focus to complete more important tasks.

Like the washing machine, AI coding automation will save your practice many hours of work every week, freeing time for your physicians and staff to work on higher-value activities. This technology will fundamentally transform the way coding is completed. Don’t be surprised if one day everyone in the healthcare industry looks back to this era and thinks, “How did we ever work without AI coding?”

Coding automation’s impact on revenue capture

You’ve learned about coding’s key problem. Now, let’s look at the specifics of exactly how AI technology helps your practice. Here are seven ways coding automation improves reimbursement capture.

  1. Catches errors: The simple fact is, your coders and physicians will make mistakes occasionally – it’s human nature. Mistakes are especially common when your staff are overwhelmed with a backlog of work or patients to be seen. How does coding automation help? It flags documentation deficiencies early so they can be corrected. For example, the AI technology will catch missed procedures, test interpretations, incorrect E/M code levels, and other inaccuracies.
  2. Reduces workforce shortage issues: There’s a chronic shortage of coders today. This issue has driven up costs and overtime, hurting your bottom line. From a reimbursement-capture perspective, the workforce shortage puts stress on your coding staff, contributing to errors and chart backlogs. These problems slow down the pace at which you can capture reimbursement and complete the revenue cycle.
  3. Improves accuracy: Not only does coding automation catch errors and reduce stress on your coding team, it also improves overall accuracy. It comprehensively identifies the correct procedures and treatments and assigns the most appropriate codes, often increasing your RVUs, where appropriate. AI coding automation also analyzes clinical narratives to assign the most accurate code – capturing nuances in a patient’s condition that may be difficult for coding teams to pinpoint.
  4. Enables real-time feedback: Hybrid coding models combine both AI and human coders. In this model, AI provides coding teams with real-time feedback on their work – making suggestions for improvement and correcting inaccuracies. In addition, the feedback loop can be given to physicians. Based on how they document patient encounters, AI can provide suggestions to add more detail and identify incomplete or missing information, ultimately improving clinical documentation upstream.
  5. Cross-references medical documentation: Coding automation has the ability to cross-reference an assigned code with a patient’s EHR, consultation reports, and other medical records as available – reviewing all of a patient’s history instantly. This cross-referencing function analyzes whether the assigned code exhibits any contradictions or discrepancies, ensuring accuracy and proper reimbursement.
  6. Adapts to guideline changes: With new guideline changes in effect this year, your coding team may still be adapting. AI ensures that none of the common 2023 E/M errors and changes slip through the cracks. For example, according to the new guidelines, the effort it takes a physician to consider multiple diagnoses, treatments, and procedures should be accounted for in the E/M code. As AI technology instantly incorporates guideline changes, it will catch this update to ensure you’re properly reimbursed.
  7. Provides comprehensive coding support: By now, you can see how comprehensive coding automation can be. In addition to its accuracy, ability to catch errors, and quick adaptation to guideline changes, the AI accounts for common, yet easy, momentary lapses of concentration by physicians during their documentation of specific encounters – like a problem visit and annual checkup on the same day. While a physician could forget to account for the two encounters, AI will catch both and ensure proper reimbursement.

Another benefit of coding automation: improved patient care

Reimbursement capture isn’t the only way coding automation helps a practice. Improved patient care is an indirect benefit of the technology. Just like the washing machine freed up the time of countless Americans in the mid-1900s, AI does the same for physicians today – enabling them to devote more energy and attention to patients. With a reduced administrative burden, comes less stress – meaning physicians are less likely to experience burnout. Why does this matter? Patients of burned out physicians are more likely to receive unnecessary tests, experience mistakes, and have an overall lower satisfaction level with a medical facility.

Along these lines, coding automation improves the patient experience. As it speeds up the post-visit process, patients will be able to receive their bills more promptly. While this may not initially seem like a benefit, from a patient’s perspective, faster billing means they’ll be able to more accurately budget for their healthcare expenses and be less likely to forget about a bill. Without coding automation and thus a slower billing cycle, patients may be surprised with a legitimate bill they expected to come weeks or even months earlier, leading to frustration and agitation with your practice.

In summary, the benefits of AI coding automation are simple: physicians can focus more on patients and quality of care improves, as does the patient experience.

Your coding burden can become a thing of the past

Coding has probably been a weight on your practice for as long as you can remember. Hopefully, now you’re starting to see that change is possible. AI coding automation can substantially lighten your entire practice’s workload and improve revenue capture along the way. It’s only a matter of time before the technology transforms the entire healthcare industry. Will you be an early adopter and take advantage of the benefits sooner?

Austin Ward is Head of Growth at Fathom, the leader in autonomous medical coding. He oversees the company's go-to-market efforts and client analytics. He brings broad experience in health systems, technology, and data science and has worked at BCG, the Bill & Melinda Gates Foundation, and in venture capital. He holds an MBA from Stanford University, MPA from Harvard University, and BAs from the University of Chicago.

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