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UCHealth Deploying Predictive Analytics Tool to Improve OR Utilization and Enhance Patient Care

May 10, 2016
by Heather Landi
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"This is the kind of stuff that gets me out of bed in the morning, to really make a difference for our doctors, for our nurses, for other staff members, and most importantly, our patients." - UCHealth CIO Steve Hess
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Last fall, the University of Colorado Health (UCHealth), a five-hospital health system that serves communities across Colorado and operates clinics in Wyoming and Nebraska, deployed a predictive analytics solution in its infusion centers to better manage scheduling complexity and improve resource utilization. That technology implementation proved so successful that UCHealth leadership are moving forward to deploy a similar solution for its operating rooms with the aim of optimizing OR utilization to improve efficiency and enhance patient care.

UCHealth announced a partnership with Silicon Valley startup LeanTaaS to deploy its iQueue for Operating Rooms solution at the health system’s flagship hospital, Aurora, Colo.-based University of Colorado Hospital for its 38 OR rooms. The health system plans to integrate the data analytics platform into all OR operations at all five hospitals—109 OR rooms total—later this year and next year, according to UCHealth CIO Steve Hess.

Hess says the deployment of the data analytics platform, first at the health system’s infusion centers, and now in its ORs, enables the health system to tackle some of the most complex challenges of hospital operations, specifically related to capacity issues and workflow.

“We’re good at a lot things—we have brilliant clinicians, brilliant nurses, incredible administrators and clinical leadership. We have an enterprise EHR system and we have really good robust data out of the system. We have good process improvements, but some of these workflow and capacity issues are really hard to work through, because people can see the data, but they don’t understand how to take action on the data. The combination of process improvement and data analysis is hard, to say the least. What we’re seeing is that often it’s easy to get the data out, but it’s hard to then translate that into improvements in the EHRs or with workflow to actually make a difference,” he says.

Operating rooms are key resources in a hospital, but most hospitals struggle to manage OR blocks efficiently due to the complexities involved. The manual OR block allocation process, which uses thumb rules and anecdotal evidence to assign OR time to surgeons, results in inefficient operations: a high volume of critical OR blocks go unused and operational bottlenecks go unnoticed, causing severe inefficiencies, according to LeanTaaS.

Hess refers to the OR block allocation process to a Tetris game with schedulers and administrators trying to allocate the blocks of time with maximum efficiency and also enable surgeons to have maximum efficiency. “We often default to scheduling full-day blocks and half-day blocks, so you may schedule a six-hour surgery that only takes three hours.”

The iQueue for Operating Rooms platform uses lean principles and advanced data science to examine dozens of OR operations parameters and then builds predictive models to forecast usage patterns and allocate OR time efficiently to meet hospital objectives. Hess says the analytics platform will provide the health system deep visibility into OR utilization. “It tells us why we’re not fully utilizing our operating rooms, where the bottlenecks are, and how we can make it better. Every minute of OR time is valuable; even a small gain can have a big impact on our bottom line,” he says.

“If we can make use of those ORs much more efficiently that should improve physician satisfaction, and ultimately, increase patient satisfaction, because they can get their elective surgeries scheduled much more timely and efficiently,” he says.

Steve Hess

And, the deployment of this latest technology is just one example of how UCHealth leadership leverages health IT for system-wide process improvement initiatives.

“This is the kind of stuff that gets me out of bed in the morning, to really make a difference for our doctors, for our nurses, for other staff members, and most importantly, our patients,” Hess says.

For the last several years, the health system was focused on implementing the enterprise EHR system and Hess refers to this predictve analytics initiative as the “next step on the journey.”

“For most systems out there, the end of the journey is not implementing the EHRs, it’s actually making use of it to improve processes and patient care and the experience for doctors. And, to me, this is one of the most exciting things we’re doing because we are now getting to that.”

Last year, UCHealth worked with LeanTaaS to adopt its analytics platform to address scheduling complexities with its infusion centers for cancer treatment. “We were having issues with 14 percent continued growth of infusions and long delays, long waits, and peak periods in the middle of the day where we were really not efficient. As a result, our patients were suffering, in terms of there being significant delays in getting that treatment done and we were unable to do add-on, same-day appointments,” Hess says.

The health system adopted LeanTaaS’ iQueue data analytics software for its infusion centers, which went live in October, and has been able to “level off those peaks and valleys” with more efficient utilization of resources and optimal scheduling.

“We saw a 7 percent increase in patient volume, up to a 16 percent increase in patient volume during our peak periods, without an increase in staff or an increase in infusion chairs and beds," Hess says. With the deployment of the technology, UCHealth cut patient wait times by 33 percent aross the entire day, with a 60 percent decrease in patient wait times during peak hours, and a 28 percent decrease in overtime hours. "So it was a win-win-win,” Hess says.

He continues, “We started with infusions because it’s a smaller scope and it’s more easily managed, and we saw success there. So, we decided to look at how we can use this capability with our other highly constrained areas, so we turned to the OR, because it’s a need of ours, it’s an expensive resource and we are constantly battling over the idea of whether we need more ORs.”

iQueue was designed to solve scheduling and operational performance problems using data science and optimization algorithms, according to Mohan Giridharadas, founder and CEO of LeanTaaS, and the technology has been deployed in about 30 infusion centers across the country, including Stanford Cancer Center, a part of Stanford Health Care. The technology has been expanded to other hospital operations, such as operating rooms, and UCHealth is the first hospital to deploy the iQueue for Operating Rooms solution.

“Through this technology, you are able to abstract the data out of the electronic health record (EHR) system and LeanTaaS essentially has this advanced machine learning computing capability to just crunch the data. So by running the data using advanced algorithms you’re able to produce process improvement opportunities and specific actions you can take to produce a more optimal OR schedule,” he says.

LeanTaaS’ data analytics continuously monitors and shows how operating rooms are being utilized, the root cause of delays including first case late starts and turnover times, and identifies opportunities to improve utilization. The data analytics software also has been designed to use mathematical models to detect bottlenecks and reallocate OR blocks. And, the solution enables automated block reallocation with mobile interactions, so surgeons can release assigned blocks, request more blocks and swap blocks with fellow surgeons, and administrators can approve block requests and track activity in real time, according to Giridharadas.

And, Hess says this initiative is not a one-off project, but rather part of an ongoing effort toward continuous process improvement. After rolling out the predictive analytics tool throughout the health system’s ORs, he expects the health system will next look to improve efficiencies with inpatient operations.

“So, inpatient is the natural next place to go after OR. But don’t stop there, think about radiology and imaging, think about lab tests, pharmacy meds, ambulatory clinics, frankly, the canvas is blank in terms of what you can do with machine learning combined with process improvement philosophies,” he says.

He continues, “Here at UCHealth, we’re growing—we’re growing by acquisition, by affiliation, by doing new builds of appropriate care centers—but we also already have a lot of footprint. So, whenever we have throughput issues or capacity issues, we want to turn the conversation away from how many new ORs do we need to build? Instead, we look at how do we use our existing ORs better? In this era of rising healthcare costs, we are trying to be good custodians of the national spend and we want to be able to use our existing resources better, and this is going to help us with that,” Hess says.

He refers to the use of data science and predictive analytics applied to hospital operations as “game changing.”  “We have brilliant physicians and incredible administrators, but they are busy taking care of patients and taking care of staff. It’s hard to step out of their day-to-day role and say, ‘If we do x, y and z, we can move the needle.’ LeanTaaS is getting to that step, and in days and weeks, where maybe we could get to in months or years, and that’s the difference.”

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