Operating Rooms are the economic backbone of a hospital, often generating more than 50 percent of revenues and profits for the institution. A single block of Operating Room time (~500 minutes) can generate $50K – $100K+ in revenue per day, depending on the payor and case mix. ORs are also the most expensive resource to operate. And so, when it comes to allocating OR time, “Every block is sacred.” Yet the way blocks of OR time are allocated, taken away, released into open time, and requested by other surgeons leaves a great deal of room for optimization. In Chapter 4 of the book “Better HealthCare Through Math” we go into the details of how exactly a combination of predictive analytics, cloud based software tools and changes to policy and process can achieve substantial improvements. We summarize the problem and the 7 high level tenets of the solution below.
Why and How “Blocks” Work: Surgeons typically have to organize their lives around clinic days, teaching, and other commitments, so they have many restrictions on which days they can avail themselves of block time. What has evolved is a system whereby the hospital sets aside a block of time and allots it to a surgeon or service line or group, so it becomes easy for that surgeon to conduct multiple similar surgeries in sequence with the same team, room, and equipment. These blocks of time are then reserved on a recurring basis for that surgeon or service line. In most hospitals, surgeons are encouraged to release upcoming blocks of time they know they will not be using—because they will be on vacation or at a conference for example—so that other surgeons may use them. If a surgeon has not scheduled any surgeries for their block by, say, a week before the date, the hospital’s system may “auto-release” their block to others.
The Problem This Creates: Having made such allocations, matching supply and demand in the OR arena is notoriously thorny. The fundamental issue is as follows:
Supply of block time—and OR time in general—is limited by the number of rooms, the equipment available, and the availability of staff (nursing, anesthesia). It is also severely limited by the number of blocks that have already been allotted. The traditional method of allocating block time leads to a situation whereby a number of health systems we have worked with find themselves “heavily blocked,” with not much open time left to attract new volume.
Demand, however, is fluid, unpredictable, and volatile, as in other healthcare areas. Just because a surgeon “owns” a reserved block of time, that does not mean he or she has more predictable scheduling needs than a surgeon that does not own block time. All surgeons require a varying and dynamic number of minutes of OR time from week to week, due to a variety of reasons that are impossible to predict, such as the number of patients they see in the clinic each week, the percentage of those patients that will require a surgical procedure, and the lead time needed for scheduling each procedure.
To make matters worse, most health systems use a flawed metric, block utilization, to make decisions about the number of blocks that should be awarded or taken away from surgeons and service lines. Block utilization is a gross average that penalizes surgeons for small, fragmented chunks of unused time (room turnover, finishing early, late first-case starts, etc.) that are often beyond the surgeon’s control and cannot be usefully repurposed into booking additional cases.
Furthermore, the block utilization metric does not account for the inherent volatility in case durations across surgeons and service lines. When a surgeon “opens up” a 75-year-old patient with multiple preexisting conditions, there is no telling what kind of complications they are going to run into or how long that procedure is going to take. This volatility makes any relative comparison of block utilization numbers inherently flawed.
Solving the problem of sub-optimal OR utilization requires a learning system and process that:
● improves OR access for all surgeons and patients
● improves asset utilization rates for hospitals
● creates greater accountability for block owners
● creates transparency and visibility into metrics that matter and that everyone can agree on
7 Elements To The Solution: The engine needed for this solution involves a combination of prescriptive analytics that provide actionable recommendations, scalable software tools that enable easier planning, decision-making, block rightsizing, and the freeing up of more open time and changes to policy (e.g. block policy) using metrics that make sense. Specifically there are 7 components to optimizing the use of OR time:
- Get more time released earlier and make finding open time as easy as using Open Table
- Don’t rely on “tribal” rules for booking
- Evaluate block usage in a more meaningful way than using “Block Utilization”
- Provide clean, credible, and transparent real-time data
- Engage stakeholders in the data
- Stop using “bad math”
- Stop ”admiring the problem”
To gain similar benefits in your own organization is largely a matter of mindset and willingness to adapt to new tools. We get into each of the following 7 elements of the solution in Chapter 4 of the book Better HealthCare Through Math. You can also reach us at info@leantaas.com for more information on how dozens of health systems are adopting such a data driven approach.
