Mayo Clinic’s CFO on Turning Analytics Into Advantage

By tightly integrating cost, quality, and outcomes data, and deploying AI with a human-centered philosophy, Mayo Clinic is using analytics not just to manage margins, but to quicken clinical discovery and also redefine productivity.

As health systems grapple with persistent inflation, workforce shortages, and uneven payer rate growth, many still struggle to connect financial performance with clinical outcomes in a meaningful way.

Mayo Clinic believes it has moved further along that curve. CFO Dennis Dahlen says mature data integration, advanced analytics, and AI are helping the health system close financial gaps, fuel clinical breakthroughs, and rethink productivity—all while keeping humanity at the center of care.

Finding the Balance Through Analytics

Dahlen says the organization is ahead of the curve in linking cost, quality, and outcomes, which is an area where many health systems still struggle.

“I landed into a place where the integration of clinical and financial and quality and safety information was pretty mature,” he notes, while acknowledging that refinements are still ongoing.

Mayo’s internal analytics are central to closing the gap between payer rate increases and inflation.

“We use the integrated data quite well to take advantage of opportunities,” he explains. “We scale and redesign and then we rinse and repeat.”

Externally, Mayo is experimenting with new ways to make complex performance data accessible, such as its recently launched Health Locator tool, which merges quality, safety, and outcomes metrics for more than 5,000 hospitals.

While cost is not yet included, Dahlen sees it as part of the broader journey toward transparency and integration.

Where Mayo truly differentiates itself, he says, is in using advanced analytics and AI to accelerate clinical discovery—generating value that would be impossible with traditional methods.

He cites a recent neurosurgery project that emerged “in a matter of a couple of weeks,” identifying medication patterns associated with significantly improved glioblastoma survival rates. Those findings have led to new clinical trials.

Similarly, officials say Mayo’s AI-enhanced imaging tool has enabled pancreatic cancer diagnosis 18 months earlier with 97% sensitivity, and AI-driven EKG interpretation is transforming a century-old tool into a predictor of AFib, amyloidosis, low ejection fraction, and even liver disease.

For CFOs, that robust integration of financial and clinical data is not just an operational priority, but an asset that fuels redesign, productivity, and breakthrough clinical performance.

Technology and Humans

Mayo Clinic is leaning heavily into advanced technology to reinforce, rather than erode, the human connection at the core of care. Dahlen underscores that “we keep the patient in the center of everything we do,” noting that even the organization’s AI supercomputing cluster is named after the health system’s founding order of nuns as a “reminder to [keep] humanity at the center of all our AI innovation.”

The super pod, powered by NVIDIA chips, fuels Mayo’s generative and agentic AI development, but its purpose is tightly anchored to a human-centered philosophy.

For Mayo, technology must enhance human relationships, not replace them.

 “If we adopt technology that dilutes that humanness… we’re going in the wrong direction,” he says.

It’s this principle that guided the design of Mayo’s Hollywood-quality clinician avatars, which have tested surprisingly well with patients. Dahlen describes “pretty high adoption rates [and] pretty high affection rates,” emphasizing that avatars and similar tools are meant to extend, not diminish, the clinician’s presence.

 “We don’t want to displace the human element in healthcare,” he said. “We want to replace it and extend it… to leap above the fact that a clinician only has so many minutes in a day.”

The CFO takeaway? As virtual care and AI adoption accelerate, the most sustainable digital investments will be those that safeguard the patient-provider relationship while expanding clinicians’ capacity, balancing efficiency with empathy.

Reexamining Productivity

Dahlen says productivity in healthcare will soon look very different—even if the core definition remains the same.

“I can’t see the definition of productivity changing… but the way we look at it will definitely change,” he explains. This includes shifting from siloed departmental metrics to cross-departmental performance shaped by AI-enabled workflows.

Nursing, radiology, and pathology are early examples: An internally built AI model now handles most nursing documentation; radiologists review AI-pre-read images rather than starting from scratch; and digitized pathology will soon allow similar agentic support.

These tools, he notes, free clinicians to focus on higher-value work and may eventually “reduce the demand… on the population for nursing talent.”

Finance, he argues, plays a central role in enabling this transformation.

“It’s really up to us to take it from an objective distance… think about the business model… and identify opportunities,” he says.

This must be done all while ensuring sustainable workforce and care models. Finance must also “create resource pools to sustainably invest” in the technologies that will redefine collaboration and productivity across the health system.