A Practical Approach to Using AI, ML, and Automation in Revenue Cycle

July 2, 2024
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The healthcare industry is rapidly evolving—particularly for payers, who have already embraced artificial intelligence (AI) in their denials strategies—and with it, the need for providers to also embrace smarter, more effective revenue cycle management (RCM) technologies.  

A recent standing-room-only panel session hosted by Aspiron at the 2024 HFMA Annual Conference explored how some perceptive hospitals and health systems are leveraging AI, machine learning (ML), and automation to address challenges in the revenue cycle. The session, titled “A Practical Approach to Using AI, ML, and Automation in Revenue Cycle,” brought together experts from leading healthcare organizations to share their insights and experiences. 

The panel included industry leaders Brad Tinnermon, Senior Vice President of Finance and Shared Services at Kaiser Permanente; Edna Buffington-Price, Vice President of Revenue Cycle at Community Health Services; Ted Syverson, former Vice President of Revenue Cycle at King’s Daughters Medical Center; Spencer Allee, Chief AI Officer at Aspirion; and Jim Bohnsack, Chief Strategy Officer at Aspirion. 

Key Challenges in Revenue Cycle Management 

The panelists identified several significant challenges facing healthcare organizations in revenue cycle management: 

Staffing issues: Healthcare providers are struggling with staffing shortages, particularly in registration and customer service roles. Competitive salaries in other industries are making it difficult to attract and retain talent.

“Middle management turnover,” Syverson said is another concern for health systems. “I think for a lot of years, you had the hypothetical ‘Margaret’ who had 38 years of experience as a biller and almost a hoarder of knowledge, but also kind of that leader for the rest of the folks that were there. Those people are leaving, and they’ve been leaving in droves over the past five to seven years. What I’ve seen as a challenge is, well how do we replace those people? You don’t really replace those people. I think you have to leverage the partnerships that are out there. The partners that have and deliver services, but also assist with scheduling meetings and doing follow up are just tremendously valuable.” 

Tinnermon echoed similar sentiments about challenges with staffing. “Probably our biggest issue right now is not only staffing expense,” he said. “Staffing productivity, staffing quality. And that ripples through the entire revenue cycle front to back. The cost is very high and our productivity is not where it needs to be.” 

Denials management: The increasing complexity and frequency of denials from payers is putting pressure on hospitals and health systems to find more efficient ways to handle appealing and overturning denials

Technological integration: Many healthcare systems face challenges in integrating different components of their technology stack, leading to inefficiencies and data silos

Changing payer landscape: Payers are investing heavily in technology to deploy rules faster, making it difficult for providers to keep up with changing requirements

Lack of legal resources: “Having legal resources that can respond in a timely, effective, and knowledgeable manner, that’s a huge problem,” Syverson explained. “If you don’t have the resources to do that, you get lost, you get trapped, you get behind. You write off dollars.”

Leveraging AI, ML, and Automation 

To address these challenges, some forward-thinking healthcare providers are turning to AI, ML, and automation solutions. Here are some important areas where these technologies are being applied: 

  1. Contactless registration: Buffington-Price shared how her organization implemented contactless registration to improve patient satisfaction and reduce disease transmission. She noted, “We were pleasantly surprised because a lot of our patients in this market, even our older and elderly patients, really didn’t want to sit out in the lobby with other patients. And it went over really well. Point of service increased. Patient satisfaction increased.”
  1. Denials management automation: Organizations are using AI and ML to automate parts of the denials management process, from identifying trends to crafting appeal letters. Allee explained, “We want to surface those trends and share them with our clients. We use predictive models, not just to work the right case at the right time, but also to highlight those trends and how those trends are shifting, because we can use machine learning to identify those patterns in the data and we can retrain every month and look how those are changing month to month.”
  1. Medical record analysis: AI is being used to extract relevant information from medical records, saving time for clinicians and improving the accuracy of appeals. Allee described a system where “there’s a set of predefined questions that get asked for every medical record, and medical records can be hundreds of thousands of pages. So instead of our nurses having to read through all of those, they get a summary that’s extracted from those right at the top, and then there’s a prompt where they can ask their own questions. Instead of having to scan through and read through, they can just ask whatever they want to ask.”
  1. Workflow automation: Healthcare organizations are implementing automation to handle repetitive tasks, allowing staff to focus on higher-value activities. Tinnermon emphasized the importance of strategic implementation, saying, “Definitely get tied at the hip with your IT partners, make sure you understand their roadmap. There are so many different types (of automation). There’s probably eight different terms that are used. And literally defining those so when your team is talking they understand exactly what kind of automation they’re using or proposing and the ramifications of that type of automation, how complicated is it, and where should it be used, whether it’s in workflow or in your overnight batch-type process. So having a roadmap, having a strategy, having an organized approach that’s lined up with your IT group, I don’t think there’s a limit in this space as long as you take the time to sit down and map it out.” 

Best Practices for Implementation 

The panelists shared several best practices for implementing AI, ML, and automation in revenue cycle management: 

  1. Start with a clear strategy: Develop a comprehensive strategy and roadmap for implementing automation and AI solutions. This should include prioritizing projects based on potential impact and feasibility.
  1. Focus on use cases: Begin with specific use cases that address key pain points in your organization. This approach ensures that the technology solutions are aligned with business needs.
  1. Involve end-users: Engage staff members who will be using the new technologies in the design and implementation process. This helps ensure adoption and identifies potential issues early on.
  1. Consider partnerships: Evaluate whether to build solutions in-house or partner with external vendors. Many organizations find that partnering can provide access to expertise and accelerate implementation.
  1. Prioritize data quality: Ensure that your organization has access to high-quality data to train and implement AI and ML models effectively.
  1. Monitor and iterate: Continuously monitor the performance of automated systems and be prepared to make adjustments as needed.

The Time Is Now 

As AI, ML, and automation continue to evolve and payers running full speed ahead, healthcare organizations must lean in now to new developments and leading AI technologies in revenue cycle management in order to level the playing field.  

Allee emphasized the widespread prevalence of AI services, saying, “We’re already living in an AI world, right? Your phones, your cars, your computers, everything that we’re consuming media-wise. All of those are already driven by machine learning and AI. For us as a vendor, we feel it’s our duty to make that investment on behalf of our clients. We know the payer side already has a big lead in technology investments, so how do we start to close that gap? I would encourage everyone to think about what their strategy is. Because you do need to have one.” 

“The guiding philosophy for me always is: how do we let people do the best work that they can at the highest degree of their license?” said Allee when giving a brief summary to the audience on his extensive background in applying machine learning to knowledge-process automation across industries. “And how do we automate things that they don’t really want to be spending their time on anyway? That’s how we are approaching that at Aspirion.” 

By taking a strategic and practical approach to implementing these technologies, hospitals and health systems can improve efficiency, reduce costs, and ultimately provide better care for their patients. Finding the right partner makes all the difference. The right partnership can ease the burden on healthcare leaders, addressing multiple challenges simultaneously. They eliminate the need to constantly keep pace with rapidly evolving AI technology and automation advancements. Additionally, they resolve the ongoing struggle of recruiting and retaining specialized talent. Perhaps most critically, they alleviate the financial strain on providers who are already operating within tight margins, offering a cost-effective solution to complex RCM challenges. 

Aspirion employs teams of data scientists, attorneys, and clinicians with state-of-the-art platforms powered by advanced artificial intelligence. Our approach strikes a balance between specialized expertise and innovative technology to effectively address complex RCM challenges. By partnering with the right company, healthcare leaders can alleviate the anxiety associated with keeping pace with the latest AI technology and automation advancements, as well as the struggles of recruiting and retaining top talent. 

“It is time to develop a strategy surrounding this,” said Syverson to providers who have not yet committed to a path forward. “I think people have been deliberating that the past three to five years. Now is the time. There are a lot of effective solutions and great partners.  

“For me what has made the biggest difference is great account management with partners. Having partners that listen and adapt and personalize the solution, that’s fantastic. That’s the best of the best. Bringing wisdom to the table. Bringing an ear to the table.” 

We invest in advanced technologies so you don’t have to. Let our AI-powered revenue cycle management services boost your revenue recovery outcomes in the shortest time possible. Contact us today! 



For over two decades, Aspirion has helped healthcare providers maximize their hospital revenue recovery by focusing on their most challenging reimbursements. Aspirion’s experienced team of healthcare, legal, and technical professionals combined with industry-leading technology platforms help ensure providers receive their most complex RCM revenue so that they can focus on patient care.

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