By David Atashroo, MD
Operating rooms are the economic backbone of most hospitals, representing a significant portion of total revenue, and often operating at a higher profit margin than other departments. And, even more importantly, they’re responsible for providing critical, life saving care for patients. However, many hospitals struggle to have efficient operating room utilization, limiting the number of patients receiving care and impacting their financial growth strategy. Efficient operating room utilization is crucial for maximizing patient care and hospital revenue.
In this blog, we will explore three challenges that limit OR utilization, and share how the right application of machine learning algorithms can solve them.
Unused Block Time and Its Impact on Surgical Block Time Utilization
One of the most common causes of inefficiencies in surgical block time is unutilized block time. Surgeons must proactively release the allocated block time they know they will not be using or it will show as unavailable. Even if the hospital has an auto-release policy, it usually kicks in just a few days before the block that is being released. By the time the auto-release occurs, it’s too late to fill the time, resulting in the time going unused. If there’s no mechanism in place for identifying time that is likely to go unused, and no intervention to encourage the surgeon to release it early, this problem will persist.
This is where machine learning can help. Machine learning algorithms can analyze historical EHR data to predict, up to one month in advance, which blocks or partial blocks are likely to go unused. When there’s high confidence in these predictions, emails can be automatically sent to the surgeon, encouraging them to release the time early, making it available for others. The simple act of nudging a surgeon to release surgical block time that we know they are not going to use makes a big impact on freeing up OR time earlier so it is more likely to get filled. This proactive approach frees up more time that would otherwise go to waste, allowing other surgeons to fill these time slots with high-value cases.
Manual Scheduling Processes Hindering Operating Room Utilization
Even when operating room time is available, hospitals often struggle to schedule cases efficiently due to a lack of visibility into open time slots, affecting overall operating room utilization. Manual scheduling processes require a lot of back-and-forth between schedulers and the OR, which makes it challenging for schedulers to find available time slots outside of their surgeon’s assigned block time. The lack of visibility into open time makes it difficult to schedule cases and creates more unused time.
Advanced machine learning algorithms, like the ones that power Qventus’ Perioperative Solution, can help solve this problem by simplifying the scheduling process and serving up open time slots that meet the length and equipment requirements of a specific case. There are two ways in which we do this. By improving scheduling efficiency and algorithmically suggesting best-fit times, schedulers are able to book cases more easily and find time they otherwise may not have found, enhancing overall operating room utilization.
The first is Available Time Outreach. Available Time Outreach works by automatically offering available time directly to surgeons who are predicted to be the best fit based on practice patterns and past performance. The algorithm can be modified to weigh institutional priorities, such as growing targeted service lines or improving robotic utilization. This automated, data-driven approach enables leaders to proactively fill open time with high priority cases.
The second is TimeFinder. TimeFinder is an intuitive reservation interface used to view and request OR time in real time. Machine learning algorithms filter and prioritize time slots that are predicted to be the best fit based on the surgeon’s past case time performance and other factors, which makes it simple and efficient for users to find time and schedule cases.
By improving scheduling efficiency and algorithmically suggesting best-fit times, schedulers are able to book cases more easily and find time they otherwise may not have found.
Inaccurate Case Length Estimations
Another issue is the inaccurate estimation of case durations, which impacts operating room utilization. When case lengths are not accurately predicted, it can lead to wasted operating room time when cases end early, or it can result in delays when cases run longer than anticipated. These inconsistencies can disrupt the entire surgical schedule, creating a snowball effect that causes even more unused time.
Machine learning models are a great solution for this, because they can quickly analyze large, complex datasets that humans aren’t able to. Machine learning models can analyze a variety of data points to improve operating room utilization calculation, such as historical case lengths, surgeon efficiency, and even patient-specific factors like age and comorbidities.
Qventus has developed a Case Length Adjustment Tool (CLAT) that analyzes many different data points to predict case length more accurately. These data points include:
- The type of procedure
- The hospital and specific OR in which the case is being performed
- The clinician(s) involved
- Seasonality
- Time of day
- Trends in a surgeon’s time performing the same operation.
The impact that more accurate case length estimation can have is powerful. At University of Arkansas for Medical Sciences, CLAT improved case length estimation by 30%, resulting in a 40-hour-per-year reduction in wasted OR time.
You can learn more about the Case Length Adjustment Tool in this blog.
The Future of Operating Room Utilization Optimization
The future of operating room utilization lies in the continued advancement of technology. Innovations in AI and machine learning are paving the way for more precise surgical block time utilization and operating room utilization calculations.
Advanced machine learning algorithms can help address common challenges that limit OR utilization, including improving surgical block time utilization, making it possible to predict unused block time, improve scheduling efficiency, and enhance case length estimation accuracy. As these technologies evolve, and adoption to technology like Qventus increases, hospitals will be better equipped to predict demand, optimize schedules, and ultimately improve patient outcomes and financial performance. Through the power of machine learning, Qventus is helping healthcare facilities unlock the full potential of their operating rooms.
