New Peer-Reviewed Research Provides First Independent Evidence That Ambient AI Increases Surgical Case Volume

After deploying Apella's ambient AI in its operating rooms, Houston Methodist research found a 7% increase in surgical case volume and a 46-percentage-point reduction in late-ending OR days, verified through third-party peer review

SAN FRANCISCO, Aug. 5, 2026 /PRNewswire/ -- Apella, a leader in ambient AI for healthcare, today announced the publication of two peer-reviewed studies evaluating the impact of its technology in operating rooms at Houston Methodist. The research is believed to be the first peer-reviewed evidence linking ambient AI to both increased surgical case volume and fewer late-ending OR days. Moreover, Houston Methodist achieved these results without adding new OR resources or staff.

Together, these studies provide some of the strongest real-world evidence to date that ambient AI improves operating room efficiency.

The Problem
The operating room is the single largest source of revenue for most health systems, and most hospitals are under pressure to expand surgical access without adding rooms, staff or hours. Yet many still leave surgical capacity on the table because the data on which they rely to improve operational efficiency is unreliable or unavailable.

When that operational data is trustworthy – accurate, timely, and precise – it can enable process improvements that lead to higher case throughput, reduced overtime, and better day-to-day coordination. Meanwhile, executives field a constant stream of pitches promising AI-driven transformation, typically without credible evidence that the impact is real.

Increased Surgical Case Volume
The lead study, published in the Journal of Imaging, was independently authored by Houston Methodist researchers, led by Chief Innovation Officer and Executive Vice President Roberta Schwartz, Ph.D. The team examined a high-acuity, 15-room cardiothoracic operating suite and measured the following after deploying Apella:

• A 7% increase in case volume, or roughly 300 additional cases per year
• Gains achieved without adding new OR resources or extending the surgical day

Most efficiency claims rest on a simple before-and-after comparison, which cannot fully isolate causation. Instead, Houston Methodist's researchers used a rigorous difference-in-differences methodology from academic economics to compare the intervention site with similar sites that had not yet deployed ambient AI, allowing them to distinguish Apella's impact from broader systemwide and seasonal trends.

These results are especially notable given how difficult it has historically been to consistently demonstrate efficiency improvements in more complex OR settings; in this case, a high-acuity cardiothoracic suite. These results are also consistent with outcomes across Apella's broader customer base, where hospitals have seen a 5% average increase in surgical volume.

"Claims about AI's impact in surgery are often met with skepticism, and understandably so," said Schwartz. "That's why we approached this work with the same rigor as any clinical research study, using independent peer review and a methodology designed to demonstrate causation, not just correlation. The result is evidence we can confidently stand behind."

More Accurate Scheduling
Surgical schedules are only as reliable as the case duration estimates on which they are built. Yet many health systems treat these estimates as a fixed input and accept schedule deviation as part of the complexity of surgery. When those scheduled durations are not based on trustworthy data, hospitals likely end the day with unused capacity or unplanned late time.

The second study, published in Perioperative Care and Operating Room Management, examined the mechanism behind improving case duration scheduling accuracy. Conducted across six hospitals in the Houston Methodist system, it evaluated Apella's Flagged Cases tool, built on a predictive model trained on nearly 700,000 surgeries, that identifies surgeries most likely to be mis-scheduled so OR teams can correct them before the day begins. For cases flagged by Apella, the study found Houston Methodist:

• Improved scheduling accuracy by 40%, with mean absolute error falling from 57.4 to 34.2 minutes
• Reduced late-ending OR days by 46 percentage points, with no reduction in case volume
• Improved schedule deviation, as rescheduled cases were closer to actual durations 72% of the time

The scheduling study also shows Apella works alongside and complements the hospital's existing electronic health record (EHR), drawing on records the hospital already keeps and improving the results of existing investments.

"Every hospital executive is hearing pitches about what AI will supposedly do for them," said David Schummers, CEO and co-founder of Apella. "This is compelling peer-reviewed evidence that deployment of the right ambient AI platform leads to fewer unplanned late OR days and less burnout among all clinicians and staff working in the OR. That's a key component of efficiency and a clear lever for capacity improvement, which is the outcome health system executives care about most. We're proud that, when we worked with Houston Methodist's researchers to test this on their own terms, the results and their trust in Apella held up."

Expanding the Partnership
Houston Methodist has deployed Apella in more than 200 operating rooms, supporting hundreds of thousands of surgical cases across its system. As a result of these impacts on operational efficiency and benefits to OR staff, Houston Methodist joined Apella's Series B financing round in January 2026. 

The newly published research supplements breakthrough findings from the past two years of partnership, demonstrating Apella and Houston Methodist's joint leadership and innovation in the field of ambient AI in the OR:

• OR turnover times decreased by 8.7% and up to 14 minutes (Society of Robotic Surgery, July 2025)
• Data accuracy improved by 42.4% for patient-in times and 51.1% for patient-out times relative to EHR documentation (European Society for Vascular Surgery, January 2025)
• Provider-level perioperative workflow variation can effectively be measured at scale (Journal of Vascular Surgery, April 2026)
• Apella's computer vision achieved F1 scores exceeding 98% for model accuracy, precision and recall (BMJ Health and Care Informatics, December 2025)

About Apella
Apella is an OR optimization platform providing busy hospitals with real-time operational intelligence to eliminate delays, distractions, and inefficiencies throughout the day. Apella uses ambient AI and computer vision, integrated with EHR data, to automate and help surgical teams manage coordination, scheduling, and staffing so they have additional capacity to provide better, safer care to more patients. Learn how leading health systems have transformed their ORs at https://apella.io/.

About Houston Methodist
Houston Methodist is one of the nation's leading health systems and academic medical centers. The health system consists of eight hospitals: Houston Methodist Hospital, its flagship academic hospital in the Texas Medical Center, seven community hospitals and one long-term acute care hospital throughout the Greater Houston metropolitan area. Houston Methodist also includes a research institute; a comprehensive residency program; international patient services; freestanding comprehensive care, emergency care and imaging centers; and outpatient facilities. Houston Methodist employs more than 32,000 people. For more information about Houston Methodist, visit https://www.houstonmethodist.org/newsroom/. 

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SOURCE Apella