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Beyond The Buzz
During a May 2025 discussion at The Health Management Academy's CIO Forum, HonorHealth Chief Transformation Officer, Jim Whitfill, shared insights from their partnership with Qventus to transform operational efficiency through AI. With $3.2 billion in net patient revenue and more than 3 million patients served annually, HonorHealth faced key challenges common to many leading health systems – ineffective capacity utilization, mounting staff burnout, desire to improve surgical margins, and a pre-admission testing program that needed to scale. Through their partnership, HonorHealth created the equivalent of 84 virtual beds through improved patient flow, without breaking ground or adding staff, while saving $62M through reduced excess days since implementation. Executives also saw a 9.6x ROI on their perioperative investment due in part to a 4% increase in case volume and more than 2,700 cases added in 2024. They also expect to see a reduction in 72-hour surgery cancellations and a boost in pre-admission testing(PAT) staff productivity by up to 50%.
Here are five key takeaways that HonorHealth shared:
Know when “good enough” is not enough
Health systems have made massive investments—both monetary and other resources—in their EHR. Getting as much out of that investment as possible makes complete sense. However, a recent study found that only 2% of CIOs believe that their EHR’s AI solutions are “mature”. So how do you determine when ‘good enough’ just isn’t good enough?
Leaders need to answer three key questions.
Do I believe my EHR can deliver—and keep up on—advanced AI technologies that solve my actual problem?
Can I afford to wait for my EHR to build what they’re promising on the roadmap?
Will my EHR be a true partner in change management?
HonorHealth views having a system of action on top of their EHR system of record to drive measurable growth as a
requirement.
Stay rooted in the problem to see impact
In a dynamic AI landscape, CIOs face constant pressure to implement cutting-edge solutions across their organizations.HonorHealth's problem-first approach demonstrates why maintaining a laser focus on identified challenges—rather than deploying technology for technology's sake—delivers superior outcomes. HonorHealth had an inpatient capacity challenge at one of its disadvantaged hospitals with a high volume of unseen patients in the ER. With the initial partnership focused on inpatient capacity and allowing for improved throughput from the ER, HonorHealth saw a measurable drop in length of stay (LOS) and now consistently ranks in the top quartile or decile of Epic benchmarks for low LOS.
Given the ROI from Qventus’ Inpatient Capacity Solution, HonorHealth broadened its partnership to optimize its OR operations and grow surgical volumes. Like many other systems, surgical volume drives the majority of the hospital's revenue so they can fund their mission of serving patients in their community. Given that every percentage of surgical margin improvement equates to millions of dollars of impact, this was a use case where good enough wasn't good enough.
A realistic baseline is critical to determine if real improvements are being made
Healthcare systems embarking on AI implementation journeys face a critical measurement challenge that threatens to undermine both adoption and accurate performance evaluation. As Jeff Mounzer, Qventus' Chief Product Officer, stated, "One of the biggest fallacies is that health systems are comparing AI outcomes against a process that is being done 100% correctly and perfectly today, which is never the case." This fundamental misalignment creates an impossible standard where new AI solutions are measured against theoretical perfection rather than the inconsistent reality of current healthcare operations.
Consider the clinician reviewing 3,000 patient notes—human performance in this scenario isn't flawless, but potentially includes error rates as high as 20%. When healthcare leaders fail to acknowledge these inherent limitations in current processes, they inadvertently create evaluation frameworks that no technology—regardless of its actual improvement potential—can satisfy. This perfectionist baseline becomes particularly problematic in high-stakes healthcare environments where patient outcomes hang in the balance, forcing CIOs into a paralyzing tension between accelerating adoption to improve care and maintaining unrealistic quality expectations.
The solution to this baseline paradox, as demonstrated by HonorHealth's partnership with Qventus, requires a fundamental shift in implementation philosophy—moving from theoretical ideals to evidence-based experimentation. For HonorHealth, it meant adopting an experimental model – starting small and iterating – to drive adoption and develop appropriate baselines by adjusting expectations from theoretical to practical reality. This allowed HonorHealth to better identify genuine improvements and make more informed decisions about future AI implementation.
A partner’s ability to embed into your organization drives successful AI deployments
CIOs are inundated with solutions boasting superior features and cutting-edge capabilities. However, experience demonstrates that a partner's ability to embed seamlessly into your organization is just as important as the quality of their technology. CIOs surveyed about the biggest frustration clinicians express about AI tools reported that one of the top three most common complaints is that it “wasn’t built with clinician input."
For HonorHealth, it was critical to find a strategic partner who had expertise specific to their problem, could align with their culture and strategic priorities, embed themselves with the health system, and, essentially, become an extension of the HonorHealth team. Jim Whitfill and Jeff Mouzer joked that Jeff essentially “set up an office at Honor” to power real-time communications and problem-solving. HonorHealth’s ability to leverage Qventus’ specialized knowledge on throughput and operations was critical to effectuate change. Qventus’ team of behavioral scientists breakdown client-specific workflows to identify pain points and automate steps.
The distinction between technical capability and effective integration underscores a fundamental principle: domain expertise is an essential bridge between innovative technology and successful clinical adoption and is critical to change management.
The emergence of a digital workforce will continue expanding the CIOs role
AI will fundamentally transform the CIO's mandate, with implications that extend beyond traditional technology management. Jim Whitfill provocatively suggested that we must shift our perspective to recognize that "AI agents should be thought of as HR issues" because "we are developing digital employees." This shift reconceptualizes AI implementations not merely as technology deployments but as the onboarding and management of an entirely new class of workforce. Digital agents will increasingly operate as autonomous team members with distinct "personalities" and capabilities, raising critical questions around integration, performance standards, and governance.
For healthcare CIOs, this evolution creates unprecedented challenges: determining "what's acceptable" behavior from these digital employees, establishing monitoring frameworks, and orchestrating interoperability when agents need to interface with each other. CIOs will need to manage these digital agents and establish frameworks that address everything from agent naming conventions to performance evaluation metrics. Supporting frontline staff through this transition requires CIOs to blend technical expertise with change management proficiency, helping teams understand that AI deployment requires organizations to "train it in the workflow" and follow processes that are "less cookie-cutter and more dynamic." Success will increasingly depend on CIOs’ ability to blend human and digital workforces, creating interoperable systems where digital agents augment human capabilities while maintaining appropriate guardrails around automation.