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UPMC may slow or hold off on some artificial intelligence deployments if the health system does not have the technology infrastructure needed to adequately monitor the tools, according to its chief medical information officer, revealing a deliberate UPMC AI monitoring infrastructure strategy shaping which AI tools reach clinicians and when.
How UPMC AI Monitoring Infrastructure Decisions Actually Get Made
Rob Bart, MD, chief medical information officer at Pittsburgh-based UPMC, told Becker’s that the health system is working to ensure it has the infrastructure not only to support AI platforms but also to continuously monitor them after implementation. “In some cases, the answer is yes,” Bart said. “In other cases, we still need to find an appropriate technology solution, or we have to hold off or slow down some of our deployment until we have those capabilities.”
Why Relying on People Alone Doesn’t Scale
The challenge is becoming more pressing as the number of AI tools health systems use grows. Bart said relying solely on people to repeatedly monitor algorithms can quickly run into workforce constraints, particularly given the limited number of data scientists available to health systems. Dedicated technology platforms can help augment those teams and support ongoing evaluation of AI tools, he said.
The Industry Backdrop Behind This UPMC AI Monitoring Infrastructure Approach
The approach comes as health systems work to build governance capabilities around an increasingly crowded AI environment. More than 90% of health systems have deployed third-party AI solutions, but just 44% have a dedicated platform for testing those tools before rollout, according to an Aug. 6 report from the Center for Connected Medicine at UPMC and KLAS Research.
A Broader Divide the Report Identified
Although 92% of surveyed health systems said they test third-party AI tools before deployment, nearly two-thirds, 63%, characterized their overall AI strategy as developing or ad hoc. Health system leaders cited limited resources, insufficient time and shortages of specialized talent among their leading barriers to adoption, a gap this same UPMC-affiliated research center helped document across the industry.
Why UPMC AI Monitoring Infrastructure Concerns Shape Use Case Prioritization
For UPMC, managing that risk is also influencing which AI use cases the health system prioritizes. Bart said UPMC has focused predominantly on tools designed to make physicians, advanced practice providers and other staff more efficient or effective rather than leaning heavily into AI that directly informs clinical diagnosis. “If we can make them more efficient and effective with those, we can return time to them for the cognitive, the things that are needed in thinking about their patients from a clinical diagnostic, clinical management perspective,” he said.
A Deliberate Path Toward Lower-Risk AI First
That strategy also allows UPMC to introduce AI in areas that may carry less clinical risk while both the technology and healthcare’s governance capabilities mature, according to Bart. He said the health system could move further into clinical diagnostic applications as confidence in their safety grows and regulatory evaluation evolves. Clinical documentation already represents the most common AI use case among health systems surveyed in the report, cited by 52% of respondents, followed by revenue cycle, coding and billing applications at 36%.
Why UPMC Cautions Against Pure ROI Framing Within This AI Monitoring Infrastructure Strategy
UPMC’s experience with ambient documentation illustrates why Bart cautions against evaluating AI solely through traditional financial return on investment. He said the technology has reduced after-hours documentation, often called “pajama time,” for many UPMC physicians from about two hours per night to less than 30 minutes.
Value Beyond Seeing More Patients
UPMC did not adopt the technology on the premise that physicians would necessarily see more patients, he said. Instead, reducing administrative burden and helping address clinician burnout represented value in itself, a framing that positions clinician wellbeing as a legitimate standalone justification for AI investment rather than requiring every deployment to demonstrate direct revenue or productivity gains.
Balancing Safety and Access Within This UPMC AI Monitoring Infrastructure Philosophy
As health systems build stronger governance programs, Bart said the challenge will be balancing those potential benefits against patient safety without creating controls so restrictive that useful technology cannot reach clinicians. “We always want to hedge on the side of safety, but we don’t want to hedge to the point where we’re withholding some of the benefits that AI could afford clinicians and patients in the care process,” he said. “We need to do it with the right guardrails in place.”
Why Avoiding AI Entirely Is No Longer Realistic
That balance is likely to become more consequential as AI becomes embedded across more healthcare technologies. Bart said the industry is already reaching a point at which avoiding AI entirely may no longer be realistic. “It is not possible, in my view, to get care in the United States that is 100% free of artificial intelligence,” he said.
What This UPMC AI Monitoring Infrastructure Approach Means Going Forward
With UPMC explicitly willing to pause or slow AI deployments pending adequate monitoring infrastructure, this approach offers other health systems a concrete governance model that prioritizes sustainable, well-monitored adoption over rapid deployment for its own sake. Given Bart’s framing of efficiency-focused AI as a deliberate stepping stone toward eventual clinical diagnostic applications, health systems earlier in their AI governance maturity may find value in adopting a similarly staged approach rather than attempting to deploy higher-risk diagnostic AI before monitoring capabilities are fully established.
What to Watch Going Forward
As more health systems confront the same gap between AI deployment and monitoring capacity that the UPMC-KLAS report identified industrywide, industry observers will likely watch whether Bart’s staged, efficiency-first strategy becomes a more widely adopted governance model. Given Bart’s acknowledgment that AI-free care is becoming unrealistic in the U.S., this UPMC AI monitoring infrastructure approach may offer a practical middle path for health systems seeking to balance innovation against patient safety as AI tools continue proliferating across clinical and administrative workflows nationwide.
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