It’s time for provider organizations to embrace advanced technologies for revenue cycle management (RCM). With hospital cash reserves low, denials for claims and prior authorizations steadily rising, and burnout high, tools that lower administrative burden and costs, and allow RCM teams to apply their expertise to the greatest effect are essential.
Payers are already taking advantage of artificial intelligence (AI) and machine learning (ML) to accelerate the pace of claims denials. Using technology to help effectively manage appeal denials at a similar rate, providers can minimize waste and maximize revenue.
Complexity is adding to the challenge
Claims denials are not only increasing in number, but the average dollar value per denial is also increasing, the complexity is increasing, resolution times are getting longer, and yield per claim is getting smaller. Labor shortages mean fewer people have the expertise to navigate the complex claims process, which in turn significantly impedes payment collections.
“An acute shortage of talent is compounded by the complexity of working with payers,” says Jim Bohnsack, Chief Strategy Officer, Aspirion, a technology-enabled provider of complex claims and revenue integrity solutions. “Leaders can creatively tackle these challenges with advanced technologies. Many have dipped a toe in the water and are trying to understand the AI/ML space. Now, leveraging innovative technology has become non-negotiable.”
Finding the right combination of tools to effectively address rising denials
Advanced technology can streamline denial pattern recognition, extract evidence in clinical documentation, and highlight the root causes of denials. When used effectively, AI and ML maximize yield, accelerate collections, and support staff by reducing administrative burden and burnout.
The key is not merely adopting the technology but applying it effectively. Technology will never replace the need for seasoned revenue cycle professionals. The true value of AI emerges when it can be integrated into workflows addressing real revenue cycle challenges.
The impact of advanced tech on RCM outcomes
While machine learning can help revenue cycle teams more easily spot patterns in claims data that predict which denials are more or less likely to be overturned, enabling them to prioritize work queues intelligently to maximize claims yield, there is even more value in ML's ability to take care of administrative work that otherwise bogs down highly skilled revenue cycle professionals. The best AI approach – leveraging sophisticated AI platforms fueled by large volumes of relevant RCM history and content -- can automate the extraction of essential information from claims, clinical documentation, and managed care contracts and present concise findings and suggestions for RCM professionals to apply their specialized knowledge to overturn denials. This approach is known as “human-in-the-loop.” The result is simplified, accelerated workflows and improved revenue outcomes.
Addressing AI shortcomings and barriers to implementation
When incorporating AI and automation into revenue cycle management, healthcare organizations may face several technical and operational challenges. They may have insufficient infrastructure to effectively deploy models or lack the volume of quality data required to build, train, and mature effective AI models. What’s more, data scientists who understand how to leverage this data are hard to come by, as are employees familiar with the evolving standards and regulations.
One way providers can address these challenges is to leverage industry-leading RCM innovators, such as Aspirion, who have proven success in using AI built on large industry data sets and who can accelerate a health system's path to stronger financial health. Collaborating with partners skilled in large language models (LLMs), such as Google, Microsoft, AWS, and other cloud providers, is a wise strategy for risk mitigation, given the uncertainty and rapid evolution of the technology and the changing regulations surrounding it.
Lack of employee buy-in can also hinder AI implementation, so it’s important to emphasize from the start its potential value. “Starting with a group of power users to test workflows and identifying evangelists within the team ensures a smooth rollout,” says Bohnsack, as does “emphasizing that AI enhances human expertise rather than replaces it.”
Collaboration is also key.
“Collaboration between AI Product/IT teams and finance leaders is crucial for effective change management and training, and clear documentation and user support mechanisms are essential,” Bohnsack says.
Finally, interoperability is critical to derive the most benefit from AI. Infrastructure must be able to route the right claim-level, clinical, contract, and operational data to models, and then fuse the results into operational workflows.
The future of claims denials and the evolution of advanced tech in RCM
Businesses must navigate the rapidly evolving AI landscape judiciously. It is vital to maintain adaptability and avoid overcommitting to a single technology, which could quickly render a business obsolete. Aspirion uses a wide array of AI tools, maintains self-managed and vendor-managed solutions, and experiments with new tools. Aspirion acquired Infinia ML, a leading AI and ML company, which operates as Aspirion’s research and development engine and helps Aspirion’s capabilities evolve.
“Technology is now fundamental to effectively navigating the pursuit of receivables,” says Bohnsack. “Although we are seeing hospital margins beginning to stabilize and operating margins improve, we aren't there yet. Optimizing performance in the revenue cycle will continue to be critical.”