When healthcare leaders talk about the operating room of the future, the conversation can quickly turn to robotics, artificial intelligence, predictive analytics, ambient technology, and whatever comes next.
These technological investments are promising. But at Becker’s Perioperative Summit, a panel of perioperative leaders offered a practical point of view.
The panel brought together the following perioperative leaders to discuss the OR of the future:
The future-ready OR will not be defined by how much technology a hospital can put inside it. It will be defined by whether that technology helps clinicians spend more time caring for patients, gives leaders better information to make decisions, and allows health systems to operate more effectively.
As Mike Guertin put it:
“Technology does not transform an OR. It’s the people using the technology to create systems that more effectively and efficiently care for patients [that do].”
That idea became a common thread throughout the discussion: technology must solve real operational problems. Automation should remove work, and data should lead to action rather than simply create another dashboard.
For years, health systems have added technology to clinical workflows in the name of efficiency. While that is a worthy goal, new tools have also created new tasks: another screen to monitor, another field to complete or another system clinicians have to interact with. These tasks can simply change the work required, instead of removing work altogether.
Shannon Holley, VP Surgical Services at ProMedica, argued that work completed in a new way is not enough, leaders need to evaluate technology on whether technology allows clinicians more time to focus on the patient.
“Anything we can do to make things automated and get them back to the bedside, I think is going to be critical.”
That distinction matters. Sam Davis of Rush University Medical Center described the future workforce similarly. As automation takes on more repetitive operational work, he expects clinicians and leaders to spend less time managing technology and more time interpreting information, exercising judgment and managing complexity.
The panel also challenged another common assumption about the OR of the future: that having more data automatically means having better operations. It doesn’t.
Predictive analytics can identify delays, forecast case duration, anticipate bottlenecks and surface operational patterns. But those insights only create value when someone can use them to make a better decision.
Guertin captured the distinction clearly:
“Predictive analytics is only useful if it helps us in making those decisions. If it doesn’t help us to make better decisions and give us more information to make those decisions, then it’s really just a dashboard.”
Houston Methodist’s Travis Tingle offered a practical example. His team has begun breaking OR performance into the smaller milestones that happen between wheels-in and wheels-out — how long it takes to open a back table, prep and drape a patient or get ready for timeout.
Individually, those minutes may seem insignificant. Collectively, they can create enough additional capacity to perform another case.
“You have to have a plan with all this data. You’re going to have a big spreadsheet full of numbers. But what are you going to do with those numbers?”
That may be one of the most important lessons for health systems investing in AI today. The objective is not visibility for visibility’s sake. It is turning better information into better action.
For Holley and her team at ProMedica, that question became particularly important. Like many health systems, ProMedica had already spent significant effort improving utilization and finding ways to perform more cases efficiently.
Eventually, there is a limit to how much additional capacity can be created in a day. So the organization began asking a different question: Are we accurately capturing and getting reimbursed for the products already being used in those cases?
“You can do all the cases you want. But if you’re not getting paid for everything you’re doing, it’s not really an effective use of your time.”
That led ProMedica to work with AssistIQ to automate supply and implant capture using computer vision. Traditionally, nurses were responsible for manually entering product information, including identifiers and lot information, while simultaneously managing the many clinical responsibilities happening inside the room.
The workflow created opportunities for incomplete documentation. With AssistIQ, the interaction became much simpler: capture the product and move on. The operational and financial impact followed quickly.
Within the first two weeks, Holley reported a 90% reduction in tissue documentation errors. That mattered not only for billing, but for patient safety: accurate tissue documentation is essential when a hospital needs to identify affected patients during a recall.
Within the first three months, ProMedica also saw meaningful improvement in captured charges and case revenue as products that previously had not been documented were now being captured.
Just as importantly, adoption remained high because the technology fit naturally into the clinical workflow.
“By using image capture, they just take a picture, move on. So it worked in the workflow.”
That may be the clearest test of useful healthcare AI: clinicians actually use it because it makes their work easier.
The conversation also expanded beyond the four walls of the OR. Several panelists described a future in which data generated within perioperative environments gives other departments earlier visibility into what is about to happen.
As Tingle explained, “Somebody down the hall knows a data point that’s going to affect me on the other end of the hall in a positive manner. That’s going to be crucial.”
That could mean sterile processing recognizing that an instrument set will be needed earlier than expected. It could mean supply chain anticipating demand. It could mean bed management preparing for a patient whose case has changed. It could mean staff acting on a problem before it becomes a delay.
Holley shared an example from ProMedica’s sterile processing operations. The organization now uses predictive technology to identify upcoming instrument bottlenecks across its hospitals and help teams determine which trays need to be prioritized. A few years ago, Holley said the main hospital was experiencing hundreds of case delays each month, adding up to 50 to 60 hours of delays leading to patient procedures being postponed or rescheduled.
In contrast, the month before the panel, ProMedica only experienced three delays totaling roughly 13 minutes. That is the larger promise of connected perioperative data. Instead of learning something is wrong when a case is already waiting, teams can act before the problem reaches the OR.
For all the enthusiasm around AI, the panelists were equally clear that technology does not eliminate the need for people. In many ways, it makes human judgment more important.
Clinicians and operational leaders will increasingly need to understand the information being surfaced, evaluate whether it makes sense, and determine what action to take next. Holley described that shift already happening among team members who historically spent much of their time pulling data.
As automation makes information easier to access, their value shifts from retrieving data to helping leaders interpret it.
“We don’t need them to pull all these pieces of data. We need them to get the reports that we’re getting now from this AI and the new technology and help us analyze it.”
That evolution may ultimately be much more significant than automating any single task. The healthcare workforce of the future does not necessarily need more dashboards. It needs systems that collect better information automatically — and people empowered to decide what to do with it.
Five years from now, operating rooms will almost certainly contain more AI, more robotics, and more predictive technology than they do today. But the panel made a compelling case that those technologies should not be the goal.
The goal is an operating environment where clinicians spend less time documenting and searching for information. Where supply chain teams can anticipate what is needed. Where leaders can identify problems before they become delays. Where hospitals capture the products they actually use. And where better operational performance ultimately creates more capacity to care for patients.
Or, as Guertin summarized during the discussion:
“Without a margin, there is no mission. But without a mission, there is no purpose.”
The OR of the future will not be built simply by adding more technology. It will be built by choosing technology that gives something back: time to clinicians, visibility to leaders, margin to health systems, and capacity for better patient care.