Chicago-based Rush University Medical Center has added 1,705 more surgeries and reclaimed more than 257,000 case minutes between fiscal years 2021 and 2024 — a gain driven by predictive analytics designed to make every operating room minute count.
Behind that shift is Sam Davis Jr., DNP, RN, associate vice president of perioperative and interventional clinical services at Rush, steering the system toward a new model of OR efficiency.
“Transforming perioperative services meant confronting real-world constraints — staffing shortages, space limitations, varying levels of buy-in from our clinicians,” said Dr. Davis. “We had fully allocated block time, but we realized we were preventing new surgeons or existing surgeons from putting on new cases.”
Before predictive analytics, Rush’s operating rooms were running on manual data pulls and legacy block allocation methods that lagged behind actual performance.
“As our volumes continued to grow, we had to think outside of the box to use our time efficiently,” Dr. Davis said.
That changed when Rush implemented iQueue for Operating Rooms from LeanTaaS, a predictive analytics platform designed to forecast block utilization and staffing needs in real time.
“The first phase we really focused on was creating access and visibility — creating that data-driven culture,” he said. “We had to break down our silos and foster an environment where our teams felt ownership of their solutions.”
Through iQueue, Rush unified its perioperative metrics — from block utilization and on-time starts to access and release rates — into a unified real-time dashboard. That transparency changed how decisions were made.
With the platform, decisions that once took hours could be made in minutes — a shift that gave leaders time to focus on proactive management instead of digging through spreadsheets.
Once data visibility was established, Rush used predictive and prescriptive modeling to expand services and optimize OR use. The system began identifying underutilized time, reallocating blocks and ensuring fairness while driving growth. The approach also strengthened surgeon engagement by giving physicians greater visibility into their own utilization patterns and scheduling access.
Engagement was key, Dr. Davis said. Daily operational huddles and monthly chair check-ins brought clinical leaders and surgeons into the same room to review data and discuss solutions.
“We were very intentional about including our surgeons in the decision-making process,” he said. “They knew their voices were being heard, and we made changes based on their feedback. That was very important for us to get their buy-in early on.”
The results followed quickly: a 4% increase in primetime OR utilization, a 12% increase in surgeon block utilization and a cultural shift toward proactive management. What once felt like a reactive crisis response became a forward-looking planning model rooted in data.
Now, Rush is applying predictive analytics to another major operational challenge — staffing.
Dr. Davis’s team is working with LeanTaaS to forecast surgical demand and staffing needs with close to 90% accuracy, allowing for proactive, case-specific scheduling. The approach aims to reduce overtime, alleviate burnout and ensure that clinical teams are aligned with surgical volume.
“One of the major opportunities we have is modernizing our staffing workflows,” Dr. Davis said. “We’ve optimized block time, but how do we make sure staffing aligns with everything we’ve put in place?”
For Dr. Davis, predictive analytics has become a framework for sustainability — a way to keep Rush’s operating rooms efficient and its people supported.
“People are part of everything that we do,” he said. “So we want to make sure that they’re really being focused on.”
