The healthcare revenue cycle space is facing a staffing shortage. But as hospitals and health systems manage workforce challenges and related financial impacts from bottlenecks in claims submissions and follow-ups, more are finding help from automation.

Use of machine learning and robotic process automation offers the potential to give healthcare providers the opportunity to streamline key RCM workflows while filling critical staffing gaps.

The talent shortage puts healthcare providers between a rock and a hard place, economically speaking. They're under pressure to contain costs – but labor shortages lead to inflated wages. And without sufficient revenue cycle personnel, it's difficult for providers to collect what they're owed by patients and payers.

Providers cannot offer patients the financial counseling they need to understand their responsibility and options for meeting it. They cannot manage denials effectively. This hurts the provider's revenue stream and cash flow.

These issues also can make RCM work more difficult and less satisfying to the professionals who remain in provider settings. That dissatisfaction leads to retention issues, so providers must again tap into an expensive labor market.

We spoke recently with Noel Felipe, senior vice president and revenue cycle practice leader at Firstsource, a business process outsourcing company that serves various industries, including healthcare. He focuses on the role artificial intelligence and machine learning can play in helping provider organizations deal with RCM staffing challenges.

Q. How can AI and machine learning help with the staffing shortage? What can it do? What should it not be doing?

A. The short answer is AI and machine learning are force multipliers for providers and their revenue cycle professionals. These tools can tackle an increasingly wide array of activities throughout and adjacent to the revenue cycle.

At the lowest level of intelligence, robotic process automation software bots can automate tasks like eligibility verification. Moving up in sophistication, machine learning algorithms can find the patterns in denied claims and help identify root causes. Further along still, generative AI can summarize physician notes and recommend correct medical codes.

Those are just a few application examples. Combining AI tools is going to create even more powerful solutions. A generative AI tool can monitor health insurers' claims submission rules and regulations – essentially reading them – then inform a machine learning algorithm about rule changes so it can review claims before they're submitted and alert RCM professionals.

Generative AI tools can scan a medical record in seconds to pull any additional data required.

That said, these tools can't make complex decisions. Humans will always need to remain in the loop to validate the output from machine learning and AI tools used in more sophisticated processes. Also, AI/ML solutions often are more expensive and take longer to implement than a software bot.

Q. What are the outcomes of applying AI to RCM? You suggest avoiding high labor costs, increasing financial flexibility, improving work satisfaction rates and delivering better patient experiences. How so?

A. As is the case with any good automation, an AI solution can simply do more work, and do it faster and more accurately, than a human can. Providers can accomplish more with fewer human resources. They may still pay premium rates for revenue professionals, but they won’t need as many.

Further, with AI doing the routine and boring work, the professionals will have time for patient financial counseling, complicated claims and managing appeals. AI tools can assist in these tasks, essentially being copilots to enable RCM professionals to focus on the most critical work.

The financial experience is an integral part of the overall patient experience and their perceptions of its quality. The healthcare industry's payment processes are complex, confusing and difficult for patients to navigate.

Healthcare providers who streamline their revenue cycles with AI augmenting human talent can offer patients a much better financial experience. They can use AI to guide patients through the pre-registration process, presenting different payment options based on patient demographics.

They can generate better estimates of what patients will owe, and RCM professionals will have time to explain those estimates. Prior authorization queries and requests can be done in near real time. Cleaner claims created with automation and AI reduce denied claims. All these AI-supported actions deliver a smoother financial experience to patients while improving revenue flows for providers.

Q. What are some tips you can offer healthcare provider organizations looking to implement AI tools in RCM?

A. Start small. Be sure the task really requires AI. If a task requires very little intelligence, experience or insight, it may be a better candidate for RPA. RPA software bots mimic the keystrokes of human operators and can do these repetitive tasks endlessly, never wearying or making errors. Think claim status checks, benefit verification and eligibility checks.

Understand the complexity of the decisions involved in a process. Software bots can follow simple, set rules for making if/then decisions. AI and ML can make more sophisticated decisions based on data models that contain more variables but have discernible patterns. The simpler the decision, the quicker the technology implementation.

Go after opportunities that improve RCM employee and patient experiences. RCM staff almost certainly know what their pain points are and what patients complain about. Bots and algorithms can quickly tackle something like a backlog of claims status checks and summarizing reasons for denied claims. Build on smaller successes: These also are improving data accuracy, and good data is key to good results with AI and machine learning.

Stay on top of how AI is becoming real within the revenue cycle. The technology's capabilities are advancing rapidly, and large vendors like Microsoft and Google are working to make AI easy and intuitive for businesspeople to use. Providers should make certain their RCM software and service providers are already incorporating AI capabilities into their solutions.

Follow Bill's HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.