Guest Column | June 13, 2017

The Data Proves It: First Case Starts And Turnover Time Are Not Your Best Metrics

Ashley Walsh

By Ashley Walsh, MHA, Sr. Financial Analyst and former Perioperative Business Manager, UCHealth and Director, Client Services, LeanTaaS

As a perioperative business manager for almost a decade, I’ve seen dozens of operating room efficiency improvement projects and benchmarks that focus heavily on first case delays and turnover times. On the surface, that makes sense; if the first case doesn’t start on time, it has a snowball effect on the rest of the day. Turnover delays have a similar effect.

At UCHealth — like everyone else — we focused on these two metrics for a long time. But in 2016, we did a comprehensive study of our operating room utilization and found that first case delays and turnovers are only a small part of the equation.

As a quick background, UCHealth operates five (soon to be seven) hospitals in Colorado with a collective capacity of 1,620 beds, 17,134 employees, 113,000 inpatient admissions, 2.5M outpatient visits, 357,000 emergency department visits, and 66,000 surgeries.

Over the years, I’ve seen many ways people calculate block utilization, but the most common way — and one we used at UCHealth — is total in-block minutes (plus turnover minutes) divided by total allocated block time minus released minutes. We’ve spent a lot of time studying and trying to improve first case start and turnover times. Despite seeing improvement in first case start and turnover, there was not a significant improvement in overall utilization. We decided to delve deeper to identify the root cause of underutilization.

We started with two questions:

  • To what extent do these two metrics actually impact our overall utilization?
  • What other metrics or conditions are contributing significantly to our utilization?

UCHealth’s Anschutz Inpatient Pavilion (AIP) is home to multiple clinical and surgical suites. It has 25 ORs, and 90 percent of rooms are allocated to blocks every day for surgeons, surgeon groups, and service lines. On average, about 500 minutes per day per room are allocated as block time, and about 357 minutes per day per room are utilized, yielding 71 percent block utilization. First cases usually start between 7 a.m. and 9:30 a.m. The total first case delay for 2016 across all block rooms was 16,331 minutes. On average, that amounts to three minutes per day per block room, which boils down to 0.6 percent of block time or 2.1 percent of unused block time.

We measure turnovers as the wheels-out to wheels-in time with exclusions for cases that end early — as often happens. Our goal for scheduled turnovers is 30 minutes, and we consider anything longer than that a delay. In 2016, our turnover delay across all block rooms was 97,197 minutes. On average, that’s 18 minutes per day per block room, which equates to 3.5 percent of block time or 12.3 percent of unused block time.

First case delays (2.1 percent) and turnover delays (3.5 percent) amount to a small portion of unused block time.

What are the key factors contributing to underutilization? We identified three:

  • scheduled downtime
  • last-minute cancellations
  • case length overestimation

Scheduled Downtime

We calculate scheduled downtime as total block minutes minus case minutes (including cancellations). This includes any portion of the block time that did not have cases in it. In our case, scheduled downtime contributed to 54 percent of total unused time. Even if everything went according to the schedule — i.e., there were no delays or cancellations — scheduled downtime was the biggest contributor to unused block time.

Last-Minute Cancellations

Cases that were cancelled within a week of their scheduled time are considered last-minute cancellations. In our case, the last-minute cancellations contributed to 21 percent of overall unused time.

Case Length Overestimation

We calculate case length overestimation as the difference between the actual case time versus scheduled time. Block by block, this includes the difference between total scheduled time minus actual case time performed. In our case, this contributed to 11 percent of total unused time.

Key Takeaways

  1. Scheduled downtime is the single largest driving factor for underutilization of block time. We have an operational paradox in OR block scheduling: Some surgeons have more time than they need, and some surgeons need more time than they have. Dashboards and common metrics like first case delays and turnover delays hide the real problem: the right blocks are not always efficiently assigned to the right surgeon at the right time.
  2. Block utilization as a metric is not actionable. If a surgeon’s utilization is 54 percent, there’s not much we can do about it. A better approach is to look at their utilization patterns and find practical time slots we can take away from them without affecting their actual work. For example, if a surgeon is consistently ending cases early and the time difference is such that we can accommodate cases in it, then it makes sense to look at reallocating that time. Similarly, if a surgeon is consistently releasing blocks above a certain acceptable threshold (to accommodate for vacations, conferences, clinic time, etc.), then it makes sense to have a conversation with them about how those blocks can be better utilized.
  3. To improve OR efficiency (and utilization), make block allocations more dynamic. At UCHealth, we recently adopted an OpenTable-like approach to block requests and releases. Using their mobile phones, surgeons and their schedulers can easily request block time when they need it and release blocks they don’t need. The solution sends them a message when block time becomes available and sends them reminders to release blocks they are unlikely to use. Moving to such a dynamic model has enabled us to quickly reallocate blocks and improve overall utilization.

What do you think? I would love to hear your thoughts and experiences on this.

About The Author

As director of client services for LeanTaaS, Ashley leads collaborative efforts with more than 30 healthcare organizations to develop high-ROI predictive analytics solutions that improve patient access, patient/staff satisfaction, optimize resource utilization, and reduce service delivery costs. Prior to joining LeanTaaS, Ashley was a former perioperative business manager at UCHealth Metro Denver campus. She obtained her bachelor’s degree in Health Science from Truman State University and her master’s in Health Administration from Maryville State University.