Ambient AI clinical tools boost revenue by $1.2k/month: KLAS
ILLINOIS, UNITED STATES — Health systems deploying ambient artificial intelligence (AI) documentation tools are reporting significant reductions in clinician after-hours charting alongside measurable revenue gains, according to a KLAS Research report evaluating Suki’s clinical intelligence platform across three United States providers.
The study, which evaluated Suki’s clinical intelligence platform across three United States providers, found clinicians reduced after-hours documentation by 35% to 65%.
Simultaneously, participating organizations generated an average of $1,223 in incremental revenue per provider per month in ambulatory settings — all without adding appointment slots or productivity mandates.
KLAS examined implementations at FMOL Health in Louisiana and Mississippi, McLeod Health in South Carolina, and Rush University System for Health in Chicago.
All deployed the AI tool within existing electronic health record (EHR) workflows to support clinical note creation and evaluation and management coding.
Ambient AI boosts ROI without increasing clinician workload
For health systems facing workforce shortages and margin compression, the promise of documentation-driven revenue improvement, without squeezing clinicians, is particularly compelling.
Dr. Bryon Frost, CMIO at McLeod Health, told Healthcare Finance News that the AI platform captured the full clinical encounter in real time and automatically generated structured notes for clinician review.
“That end-to-end flow minimized manual input and eliminated the need for post-visit reconstruction of the encounter,” Frost said.
“A key differentiator for us was workflow flexibility and ease of use,” Frost added.
Frost emphasized that the return on investment was not fueled by increased patient quotas. “At McLeod, the ROI we observed from ambient AI was not driven by volume pressure or schedule compression,” he noted. “It was a byproduct of better documentation.”
For hospitals and clinics across the U.S., that distinction matters. Improved documentation accuracy can strengthen coding integrity, risk adjustment, and compliance, areas often supported by outsourced revenue cycle management (RCM) teams, offshore coding specialists, or virtual scribes.
AI tools may complement or reshape those staffing models, potentially reducing reliance on manual documentation support while improving coder performance downstream.
Reshaping medical outsourcing and scalable care
Ambient AI enters a market long served by medical scribes, including offshore documentation and coding teams that help providers manage administrative load.
The KLAS findings suggest embedded AI could either augment those services or shift how health systems structure outsourcing partnerships.
Frost said the technology’s real-time capture and EHR integration are critical to sustainable gains.
“The technology fits into the clinician’s day, rather than asking the clinician to adapt to the technology,” he explained.
McLeod now plans to expand ambient clinical intelligence to inpatient providers and nursing teams.
“We are also preparing to implement ambient nursing documentation, allowing nurses to automatically capture documentation as they deliver care,” Frost said.
For provider organizations, the broader implication is strategic: documentation is no longer just an administrative necessity.
“The system becomes less reliant on heroics and overtime, and more grounded in accurate data, clinician presence and intentional use of capacity. That is what a truly clinician-centered, scalable care model looks like,” Frost said.
