Tech Adoption Dominates the Conversation at HFMARCC

"Learn to fail but fail fast," says Mayo Clinic's revenue cycle execs.

The first full day of the 2025 HFMA Revenue Cycle Conference was lively, with one topic unsurprisingly dominating discussions: technology adoption.

As hospitals and health systems face ongoing financial pressures, the need to optimize revenue cycle performance through automation and AI-driven solutions has never been greater.

From streamlining prior authorizations to improving claim denials management, the promise of technology in revenue cycle operations is clear—but so are the challenges of implementation.

Revenue cycle leaders are under increasing pressure to justify the ROI for new tools while navigating staff resistance, interoperability hurdles, and shifting regulatory requirements.

As adoption accelerates across the industry, the conversation has shifted from whether to implement automation to how to do so effectively.

Kevin Barnes, director of revenue cycle automation, and Michelle Amos, manager of revenue cycle automation, both from Mayo Clinic, took the stage to share their lessons learned and practical strategies for success.

Their insights provided a roadmap for organizations seeking to move beyond pilot programs and achieve measurable impact with revenue cycle technology.

Here’s what they had to say.

Achieving buy-in

Getting various stakeholders on board is the first obstacle to overcome.

When assessing potential for new technologies, revenue cycle leaders at Mayo Clinic develop roadmaps to success that are shared with senior leaderships and technology steering committees. If a technology passes the initial test, the roadmap helps to ensure that all stakeholders are working toward a common strategic goal during implementation.

Of course, demonstrating ROI is key to achieving buy-in. However, ROI calculations can go beyond just dollar amounts. In some instances, the potential amount saved in salary may move a project forward.

However, if a technology can, for instance, help to get medications approved for patients more quickly, it provides value even without a monetary amount attached.

Confronting staffing challenges

Technology adoption projects can consume significant amounts of workforce resources. Health systems should consider the pros and cons of using in-house versus contracted workforce resources to help limit the impact, Barnes and Amos said.

While in-house resources offer consistency, create accountability, and provide deeper alignment with strategic goals, they are also costly.

On the other hand, benefits to contracted workforce resources allow greater flexibility to scale technology projects and speedier deployment.

However, these resources come with an institutional knowledge gap and may not offer the post-implementation maintenance and support that health systems need.

Tweaking responsibilities for in-house staff is one way health systems can leverage existing institutional knowledge to support technology adoption, according to Barnes and Amos.

Mayo Clinic shifted some of its EHR support staff into business analyst roles. Because of their background with the organization, these individuals are well-equipped to identify use cases for technologies and to help prepare the road maps used to determine feasibility.

How to tell if automation technology is working

Barnes and Amos shared four rules to help determine if automation technology is working effectively. These tools should be:

• Repetitive – automation is most effective when it is used for processes that reoccur with regularity.
• Rule-based – automation should be driven by defined logic controls for each step in a given process.
• Stable – automation should be applied in instances where changes are not expected in the near-term.
• Consistent – automation is ineffective if applied to processes where too many exceptions would require human intervention.

Learning to fail

Mayo Clinic has seen positive results overall from its automation initiatives. One success story can be seen in the health system’s efforts to automate outpatient medical prior authorizations.

Before adopting an automated process, Mayo Clinic staff had been manually sorting and processing more than 1,000 faxes per day. Following automation, more than 80 percent of prior authorization requests were classified without human interaction.

Of course, not every technology implementation project produces the desired results. And that’s okay – if failures produce lessons learned. It is also important that health systems not prolong their failures. It can be easy to fall victim to the sunk-cost fallacy, devoting evermore resources to a solution that is not working.

“Learn and fail,” Barnes said. “But fail fast.”