On average, the health systems generated $1,223 in incremental revenue per provider per month for ambulatory care.
As health systems look for ways to reduce clinician documentation burden while improving financial performance, AI tools embedded directly into clinical workflows are boosting operational and revenue improvements.
A KLAS Research report has evaluated the performance and return on investment of Suki’s AI-powered clinical intelligence platform across three U.S. health systems: FMOL Health, theFranciscan Missionaries of Our Lady Health System, a nine hospital system based in Baton Rouge, Louisiana that serves Louisiana and Mississippi; McLeod Health, based in Florence, South Carolina, a seven hospital network serving 12 counties in northeastern South Carolina; and Rush University System for Health based in Chicago that has three hospitals.
All deployed Suki’s technology in ambulatory care settings to support clinical note creation and evaluation and management (E/M) coding within their existing electronic health record workflows.
WHY THIS MATTERS
The KLAS report found clinicians using the platform reduced after-hours documentation by between 35% and 65%. This indicates a substantial decrease in documentation burden and more time returned to normal working hours.
The analysis also identified direct financial impact tied to improved coding performance. On average, the participating organizations generated $1,223 in incremental revenue per provider per month, without introducing additional productivity requirements or changes to clinical schedules.
In addition to documentation and revenue metrics, the report highlighted operational improvements associated with patient access and throughput.
There were organic increases in patient volume at the three health systems, which the report attributed to gains in clinical efficiency and workflow performance following implementation of the platform.
Dr. Bryon Frost, CMIO at McLeod Health, told Healthcare Finance News that Suki’s AI captured the full clinical encounter in real time, including interruptions and multi-party dialogue, and automatically generated structured notes that clinicians simply reviewed and validated.
“That end-to-end flow minimized manual input and eliminated the need for post-visit reconstruction of the encounter,” he said. “A key differentiator for us was workflow flexibility and ease of use.”
Clinicians could use Suki through the standalone application or directly within Epic Haiku, depending on their preference and clinical setting. This consistency across environments reduced friction, shortened adoption time, and allowed clinicians to realize time savings without increasing cognitive load, according to the report.
Frost said those technical capabilities–real-time capture, context awareness, and native EHR integration–are what make meaningful time savings possible at scale.
“The technology fits into the clinician’s day rather than asking the clinician to adapt to the technology,” he explained.
Building on this success, McLeod plans to expand ambient clinical intelligence to inpatient providers to bring the same documentation relief to hospital-based teams.
“We are also preparing to implement ambient nursing documentation, allowing nurses to automatically capture documentation as they deliver care,” Frost said.
The aim is to further reduce administrative burden and enable more direct patient interaction across the care team.
THE LARGER TREND
Frost said from a financial perspective, healthcare organizations should view incremental revenue gains without productivity mandates as a signal that the system itself is functioning better, not that clinicians are working harder.
“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.”
At a system level, improvements in documentation accuracy and coding do more than optimize individual encounters.
They begin to reshape how care is delivered across the organization. When clinical documentation consistently reflects true medical decision-making, risk, and complexity, it creates a more reliable foundation for operational, financial, and clinical decisions.
Frost predicted that as AI becomes more deeply embedded in clinical operations, these capabilities lay the groundwork for more sustainable delivery models.
“The system becomes less reliant on heroics and overtime, and more grounded in accurate data, clinician presence, and intentional use of capacity,” he said. “That is what a truly clinician-centered, scalable care model looks like.”