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Summer 2022

Coding Corner: How to Solve Both Coding Issues and Staff Shortages
By Taylor Ross, CPC, CCS
For The Record
Vol. 34 No. 3 P. 8

For coders, the last few years have been a wild ride, to say the least. COVID-19 had new coding and payer regulations popping up left and right, and mixing that with declining staff numbers, poor employee morale, and retention issues created a recipe for disaster.

Coding issues and inaccuracies stemming from confusion or lack of education around these new rules and guidelines have led to negative consequences, including increased claim denials, which, ultimately, look unfavorably on coding teams.

But when organizations are left understaffed and staffs are overworked, it’s no wonder inaccuracies and other issues are occurring at an increased rate.

What if there was a solution that solved both common coding issues and staffing shortages? Many coding departments are starting to embrace artificial intelligence (AI) to automate entire segments of their jobs to tackle these challenges head-on.

Here’s how.

Solving Common Coding Issues With AI and Automation
Reducing claim denials has always been important to medical organizations, especially considering their impact on cash flow. However, in light of recent health care changes and ongoing updates to coding guidelines, it’s becoming even more of a priority.

Unsurprisingly, like virtually every other facet of health care, COVID-19 had a massive effect on denied claims. According to the Change Healthcare 2020 Revenue Cycle Denials Index, denials have risen 11% nationally since the beginning of the pandemic.

While claims get denied for various reasons, coding inaccuracies are a common source. Denied claims don’t necessarily reflect on the coding team’s lack of skill or expertise. Instead, they reflect on the challenges of working within a complex field with constantly evolving rules and guidelines. Even the best coding department will make errors; after all, they are humans.

Arming coding teams with the proper tools can help stop denied claims at the source. Utilizing AI for audits and automating portions of the coding process can help identify and reduce inaccuracies.

Using AI for regular audits is a great way to take stock of an organization’s coding operations and to prevent denials. AI coding audits look at every coded chart and flag inaccuracies that could lead to a potential denial. Audits can also identify mistakes such as unnecessary downcoding and or excessive overcoding.

This level of transparency gives coding teams new insight into their processes and reveals commonly occurring errors.

AI tools can also work proactively. They can review incoming charts, code them, and pass the remainder (often the more complex cases) to coding personnel. An AI system can analyze large data sets to identify issues before sending them to payers. Because AI systems are regularly updated with the most recent coding guidelines, these charts are coded with a high accuracy level, thus lowering the chance of denials.

AI and automation not only are excellent tools for preventing denied claims but also can support processes for managing denied claims.

For more routine errors, an AI system can typically fix the issue and resubmit the claim. However, due to the incredibly complex nature of denials management, AI can’t 100% automate the process. What AI tools can do in this situation is transfer complicated denials to coding teams with an in-depth summary, making it easier to process and enabling a quicker turnaround.

Coding teams can also use AI and automation to increase their ability to process claims quickly and without inaccuracies. Quicker processing reduces total charge lag and ensures faster payments—another top priority for organizations focused on improving revenue cycle health.

In addition, AI offers scalability. As any coder knows, the health care landscape is ever-changing, and while coding teams need regular education to stay up to date, AI technology can update an entire system seamlessly and quickly.

Solving Staffing Issues
While the health care industry has traditionally used some AI and automation, such as robotic process automation and bots, it is starting to embrace more in-depth use of the technology, especially as health systems face new challenges triggered by COVID-19.

One major challenge is adequate staffing and employee retention. The great resignation doesn’t just apply to physicians and nurses; it’s also affecting coding departments.

According to the Bureau of Labor Statistics, the overall employment of medical records and health information specialists, which includes coders, is projected to grow 9% from 2020 to 2030. It suspects that about 34,300 job openings will pop up, on average, over the decade. However, many of these openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force entirely.

A common misconception is that AI and automation will reduce the available jobs in billing and coding. However, this is not the case. Instead, this technology can boost the efficiency and speed of coding teams. It’s a tool for facilitating and enhancing better work environments, not for taking them over entirely.

Imagine this: A team uses AI to automate routine tasks like payment posting and insurance verifications. They also use AI to fully code some of their more common encounters and charts. Because their AI system is regularly updated, they’re confident in its accuracy. Then, they work on the more in-depth, involved tasks that can’t be automated, like denials management—ultimately reducing the amount of time they spend coding and the possibility of human error. Additionally, it frees up time for regular training and education on the latest coding and payer guidelines.

Inadequate staffing is leading to staff burnout as well. It’s no secret that these past few years have taken a severe toll on the entire health care industry. Finding solutions to build professionals back up is imperative to morale, productivity, and retention.

When a team is inadequately staffed, the remaining members are often overworked, leading to low spirits and increased coding inaccuracies. When a single team member is responsible for the work of what should be multiple professionals, mistakes are bound to occur. This creates a negative feedback loop of inaccuracies, denied claims, increased charge lag, employee burnout, and more inaccuracies.

How can coding teams break the cycle? Thoughtful implementation of AI and automation is a possibility. Use this technology to support existing team members by taking simple tasks off their plates and allowing them to use their expertise on more intricate issues.

The Right Solution
The coding industry is complicated and constantly evolving, making it difficult to find effective solutions to coding inaccuracies and staffing shortages. The ability and scalability of AI and automation to update information on rules and guidelines at a moment’s notice make it an asset.

Embracing new technologies can feel daunting, but it’s a surefire way to solve the common coding and staffing challenges plaguing many of today’s coding departments.

— Taylor Ross, CPC, CCS, is the strategy and operations lead at Fathom, a Tarsadia- and Founders Fund-backed company that uses deep learning to automate medical coding. At Fathom, Ross is involved in strategic analysis and client analytics and reporting. She graduated from the University of Pennsylvania with a bachelor’s degree in economics and minors in mathematics, health care services management, and biological basis of behavior.