Peterborough Regional Health Centre turns fragmented data into trusted insight

Peterborough Regional Health Centre (PRHC) serves a growing and aging population while facing pressures common across healthcare systems: rising patient volumes, increasing clinical complexity, and persistent constraints on staffing, funding, and physical capacity. Leaders and clinicians alike were asked to do more with less while maintaining quality, safety, and compassion in every interaction.

AI offered PRHC a promising way to help address its challenges, but the organization also recognized that AI is only as effective as the data beneath it. “We started from a place of pretty significant data fragmentation and immaturity. If someone needed data, it could take weeks to dig it up, and what came back was often an out-of-date Excel spreadsheet that didn’t really answer the question,” said Lynn Mikula, CEO at PRHC. “So not surprisingly, there just wasn’t a lot of trust in the data, and people were making decisions based more on instinct or the last thing that happened.”

Connecting data across care and operations

PRHC first needed to bring key clinical and operational data into a shared foundation that could support analytics and AI use cases at broader scale.

“We started not by using AI, but by setting up the infrastructure for a scalable, responsible program that would enable AI,” said Evan Lyons, EVP & CIO at PRHC. “For us, a big part of this was getting the architecture right—not just for Epic data, but for our whole data estate.”

PRHC consolidated onto Microsoft Fabric so clinical, operational, financial, and AI initiatives could draw from the same data environment—supporting analytics and AI experiences that connect clinical care with the business operations behind it. “Epic provides the clinical data, but that’s only half of a hospital. There’s a whole business side that has to support the clinical side,” explained Mikula.

PRHC became one of the first Canadian hospitals to fully deploy production workloads in Fabric. Over time, the team landed 18 production systems into OneLake, Fabric’s unified data lake, and moved from waiting weeks for static reports to exploring real-time data. The shift supported faster decisions across clinical and operational workflows and prepared the organization for responsible AI adoption.

Building trust before scaling AI

As data access expanded, PRHC put clear controls in place from the start. “Our first hire was in data governance and policy, and we started framing it from that perspective right out of the gate,” said Lyons. Using Microsoft Purview and Fabric's built-in governance, the team applied classification, role-based permissions, and clear policies to guide how information could be used responsibly while still giving teams what they needed.

“I think it’s really important to take a head-on approach, because people, naturally, are going to be uncomfortable,” said Lyons. “We started at data literacy, worked our way to AI literacy, and then really focused on the tangible meaning of it. It’s not a technology conversation. It’s about having very human conversations and focusing on what this actually means in terms of value for the organization.”

Rather than starting with complex automation, PRHC focused on helping clinicians and leaders explore data in more accessible ways. “We started fairly simple by embedding Copilot on top of our analytics, letting people ask natural language questions of the data, and helping clinicians get curious without having to go learn how to code,” explained Lyons. “This early work helped staff engage with data in a more accessible way while maintaining trust.”

“You get in front of clinicians with a solution, and the first thing they say is, ‘show me the data, show me the evidence,’” said Mikula. “So that’s what we did. We met people where they were, put accurate evidence in their hands, gave them some agency to make decisions with it, and started bringing them along one step at a time toward the bigger vision around agents, orchestration, and governed AI.”

Applying AI-supported insight to high-value workflows

In orthopedics, PRHC is testing a solution built with Microsoft Foundry Azure AI Services in its fracture and cast clinics, where short, high-volume visits can carry months-long implications for recovery. Before an exam, patients can submit questions through a workflow connected to the electronic health record and governed by PRHC’s approved access controls. Those questions can surface concerns, potential safety risks, and signs of increased complexity before the visit. “That’s where this really can start to impact care—because it helps clinicians have a different, better-informed conversation with the patient,” explained Lyons.

PRHC is also working on an AI-powered command center, built using a combination of Azure AI Services, Fabric, and Copilot in Fabric, that helps teams respond more effectively to operational pressures. As Lyons puts it, “We don’t want to replace human judgment. We want to give teams a clearer view of the choices in front of them so they can use their expertise to make the best call.”

Creating measurable operational impact

PRHC used Fabric and Copilot in Fabric to apply AI and analytics and help better understand the drivers of surgery costs. In one example, differences in hip fracture procedures prompted the team to take a closer look at supply chain factors and patient outcomes. Combining clinical, operational, and cost data in Fabric gave clinicians and leaders a shared view of performance, with role-based dashboards that personalized insights for different users while drawing from the same underlying data to help guide improvement efforts.

“What started as a conversation about keeping costs under the funding envelope ended up being much more about managing patient complexity,” said Lyons. “We realized the real opportunity wasn’t in supplies or overhead, it was in avoiding the kinds of complications that drive cost up and quality down.”

That analysis translated into measurable action. Through case costing and financial analytics, the organization identified approximately $800,000 in recoverable funding and additional revenue opportunities, including $124,000 tied to bundled surgical procedures. The same approach helped reduce unnecessary lab utilization by about 14% in a six-month timeframe including a roughly 20% quarter-over-quarter decline. In medication safety and quality, the completion rate for medication reconciliation, a key process that helps ensure patients receive the correct medications, increased from 76% to more than 85%, with several units consistently exceeding 90% performance.

In the emergency department, PRHC used Copilot in Fabric to support AI-driven modeling that helped clinicians manage staffing constraints and changing patient volumes. The insights gave teams a clearer way to align limited resources with anticipated patient needs. “We were able to put the data in front of them and step back, so the strategy was grounded in something they helped shape,” said Mikula. “They rewrote the schedule themselves to make it better.”

PRHC attributes improvements across several emergency department and inpatient flow metrics from January to March 2026 to this operational effort. Fewer patients waited for inpatient beds at 8 a.m.—dropping 44% from 34 to 19—and wait time to inpatient beds decreased 43% from 56.8 hours to 32 hours. As access improved, the share of patients leaving without being seen decreased from 10% to 8.8%, while overall experience scores increased from 77% to 80%. PRHC also introduced a hospitalist swing shift that improved consult response times by about 20% while helping balance inpatient workload and support care quality. These changes contributed to longer-term gains in patient flow, including a roughly 70% reduction in ambulance offload times, from approximately 134 minutes to 40 minutes.

Across the organization, PRHC is changing how teams work with data. “A big shift for us was moving from slow, one-off data requests to a much faster, more iterative way of working,” said Lyons. “Instead of asking for data and getting a static package back weeks later, Fabric allowed us to build and release much more quickly, with a clearer line to business value.”

Clinicians are also experiencing the change in their day-to-day work. “A lot of what we’re doing is about reducing burnout,” said Lyons. “You start to hear it in the day-to-day—people actually sitting down and having a human conversation with patients, instead of running around trying to catch up.”

Extending data and AI across regional care delivery

PRHC is extending this work beyond its own walls. The goal is to give care teams across the region a more complete, patient-centered view of care, spanning partner organizations, primary care, public health, and paramedicine. “Effectively, what we’ve done is take the data and AI foundation we’ve built and made it available to other organizations,” Lyons explained.

The organization is also expanding from individual use cases toward more orchestrated AI scenarios. As Mikula explains: “Because we’ve built the right foundation, we’re in a place where we can start to build at scale, move at speed, and do it with a lot more confidence as we prepare for a future where we’re caring for a much larger patient population with the resources we have.”

For PRHC, the lesson is clear: scalable AI in healthcare starts with usable data. By applying that data to real operational and clinical challenges, PRHC is finding new opportunities to deliver meaningful impact across care delivery and operations.

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