Ambient AI is delivering measurable returns for health systems, from reduced documentation time to increased patient volume and revenue. New validation data from KLAS Research provides early evidence of how the technology is performing in clinical settings across the country.
During a recent fireside chat hosted by Becker’s Healthcare, Mac Boyter, revenue cycle management and business solutions research director at KLAS Research, and Josh Margulies, vice president of customer marketing at Suki, shared findings from an independent KLAS ROI validation study conducted across three large health systems: Rush University System for Health, McLeod Health and FMOL Health.
Here are four key takeaways from the conversation:
1. Real-time savings
The study’s methodology was designed to cut through vendor claims. KLAS conducted nonblinded research, meaning health systems were named and outcomes directly attributed, allowing for clearer comparisons across organizations.
At McLeod Health, clinicians saw a 26.8% reduction in time spent on notes. At FMOL Health, that figure was 21%, alongside a 65% decrease in after-hours documentation and a 43% reduction in notes open longer than seven days.
Mr. Boyter emphasized the significance of after-hours documentation as a key driver of burnout.
“One of the biggest burnout risks based on our research for clinicians is after-hours documentation. It’s not even the time in note during the day, it’s trying to remember eight hours later that first patient of the day,” said Mr. Boyter.
2. The financial impact
Beyond clinician well-being, the study drew direct connections between ambient AI use and financial performance.
At McLeod Health, the average net gain per provider attributable to Suki reached $1,004 per month, growing to $2,629 when accounting for downstream improvements such as coding uplift. At FMOL Health, improved documentation led to a 6.5% increase in level 4 visits, generating approximately $862 in incremental monthly revenue per provider.
Much of that financial lift stems from what Mr. Boyter described as “doorknob conversations” — the clinical details patients share on the way out the door that often go undocumented.
“Ambient AI capturing everything means that the physicians and clinicians are getting credit for all of the effort,” he said. “And just as important, we are capturing all of that for the patient record and benefit.”
3. Organic adoption matters
One of the study’s more notable findings was that patient volume increased across all three health systems without top-down mandates.
FMOL Health saw a 22% increase in patient volume, while McLeod Health recorded an 18.3% increase in patient encounters per month. In both cases, the growth was voluntary, clinicians chose to see more patients because they had the capacity.
Mr. Margulies said health systems that pushed for increased visit volumes too early saw lower adoption rates.
“The health systems that just wanted it for the benefit of the clinician, ended up seeing more patients just because that’s what their clinicians want to do. They want to help heal,” he said.
Mr. Boyter added that as physician shortages persist post-pandemic, scaling ambient AI tools is becoming increasingly critical.
4. The keys to success
Both speakers emphasized that the outcomes seen at Rush University System for Health, McLeod Health and FMOL Health were driven by deliberate implementation strategies.
Mr. Boyter outlined four pillars of success: strong governance with clinical, IT and revenue cycle leadership aligned from the start; a structured pilot that included skeptics, not just early adopters; clinician champions to drive peer-to-peer adoption; and longitudinal measurement over two to three months to capture a full range of outcomes.
“Making sure that your clinicians are champions of the technologies where appropriate is essential,” Mr. Boyter said. “It’s so much more impactful than trying to have leadership say, ‘You need to be using it.'”
Mr. Margulies echoed that sentiment from the vendor perspective.
“They don’t want to hear from me. They want to hear from people like them,” he said.
Both speakers agreed that ambient AI for physician documentation is shifting from a
differentiator to a baseline expectation across health systems. The next frontier lies in extending
the technology into revenue cycle management, nursing workflows and real-time clinical
decision support and applying the same passive-capture model to other points of administrative
friction across the care team.
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