Can AI Fix the Most Expensive Room in the Hospital?

Houston Methodist boosted surgical volume using AI tools that reveal delays and downtime inside operating rooms

9 Jan 2026

Hospitals are being asked to do more with less, and nowhere is that tension more visible than inside the operating room. These rooms drive both revenue and patient access, yet they are notoriously hard to manage. Now, a new generation of artificial intelligence tools is promising to squeeze more value out of every surgical day.

Houston Methodist has emerged as a high-profile test case. The health system recently expanded an AI-powered operating room management platform across more than 200 ORs, making it one of the largest deployments of its kind in the country. The technology, developed by Apella, focuses not on medicine but on movement.

Using computer vision and real-time analytics, the system tracks how surgeries progress minute by minute. Cameras observe when cases start, stall, or finish, while software translates that activity into live operational insight. Staff can see which rooms are ready, which are delayed, and where small changes could keep the schedule on track.

That visibility matters as hospitals grapple with staffing shortages, rising labor costs, and a steady climb in surgical demand. Operating rooms are among the most expensive spaces in healthcare. A single delay can ripple across an entire day, quietly eroding capacity.

At Houston Methodist, leaders say the initial pilot delivered a 10% increase in monthly surgical volume without adding staff. The bigger shift, they argue, was behavioral. Teams moved from reacting to problems after they occurred to spotting them early enough to intervene.

The pattern appears to be repeating elsewhere. Apella reports that health systems using its platform typically see surgical volume gains of around five percent. Recent investment is helping fuel broader adoption as hospital executives grow more comfortable with AI that operates behind the scenes.

Skepticism remains. Privacy concerns, workflow disruption, and integration with electronic health records all pose real challenges. Analysts caution that these tools must support clinicians, not dictate their work.

Still, as demand rises and budgets tighten, AI-driven operations are quickly becoming less of an experiment and more of a strategy. In the race to expand care, smarter schedules may matter as much as smarter scalpels.