March 19, 2021

Capture the right case data to maximize OR utilization

By: Austin Trout

To rebound from the industry-wide disruption caused by COVID-19, many healthcare organizations are focused on optimizing OR processes to clear the backlog of elective surgical procedures and improve financial performance. However, traditional OR block management methods for surgical schedules provide limited foresight into what block time will ultimately go unused, thereby limiting OR access.

Another obstacle is that electronic health records (EHRs) just aren’t made for reporting. Pulling data from EHRs is an imperfect way to get the data needed to make informed decisions.

The ways in which EHR data are input, standardized, and maintained can spell the difference between accuracy and missed opportunities. A healthcare organization makes critical decisions based on this information, and the age-old truism, “garbage in, garbage out,” isn’t an expression anyone wants to apply to the OR schedule.

The use of prescriptive analytics can help OR managers increase prime time utilization, decrease unused block time, accommodate new surgeon volume, and grow market share. To achieve these goals, data must be clean and consistent.

Addressing the three most common data errors will inspire confidence in OR data and lead to reliable, data-driven decision making.


Data error #1: Inadequate block builds

Because most hospitals use block time as the foundation of their OR schedules, all blocks must be built so that the correct surgeon or surgeons are credited with utilization of their block when they perform cases. Unfortunately, there are several ways to be led astray.

Problem: Duplicate surgeons records. Before assessing the system’s block architecture, it’s critical to check that every surgeon has only one record in the EHR (or in the surgical scheduling module if using a stand-alone application). When surgeons have duplicate records in the EHR, it becomes difficult to track case volume or assign surgeons to blocks because it is hard to tell which record is correct.

Solution: Take time to consolidate surgeon records to ensure every surgeon has one EHR ID and one record.

Problem: Block owner mapping/flex strings. When looking under the hood of how blocks are built, OR managers may find that many blocks do not properly connect the appropriate surgeon to the block. Some blocks are missing the connecting mechanism entirely.

One common misstep is editing the block owner mapping at the individual block level, rather than adjusting the underlying block template. Fixing an individual block may seem simpler in the moment, but it will only lead to more headaches down the road.

Solution: Address the block owner mapping in the EHR’s module for building block templates. Ensure that the correct mapping is in place for the recurring block patterns, and not just for an individual block on the front end.

Problem: Group block and service block inclusions. When surgeons team up for a “group block,” some blocks do not include all the necessary surgeons in this group. Group blocks are often built as individual blocks with multiple surgeons’ names visible on the schedule, which does not address the underlying build.

Also, as surgeons leave and join the group, hospitals do not always maintain the group block structure. This can lead to incorrect block utilization and collectable time (and any other block metrics). Surgeons in the practice who are not properly connected to the group block will not contribute to the group utilization correctly. As a result, surgeons will be dissatisfied and will tend not to trust the data.

Solution: Set up a system so that the surgeon group is updated every time a surgeon leaves or joins. Also check to see that the group block is truly built as a group block, with all the necessary surgeons tied to the block.


Data error #2: Data and policy do not align

After ensuring that individual blocks are built correctly, the next step is to verify that the entire OR schedule is set up to match staffing plans, governance policies, and surgeon preferences and expectations. Even if not technically incorrect, any discrepancies can wreak havoc. If the schedule is not aligned, any data collected in the EHR will be unreliable at best, and completely unusable at worst.

Problem: Auto-release settings. Although most hospitals have policies regarding when a block “auto-releases,” not all facilities actually build that auto-release setting into the blocks themselves. The blocks in an EHR may automatically release earlier or later than expected by surgeons and OR leaders. These inconsistencies often lead to confusion and frustration among surgeons and schedulers alike.

Solution: Make sure the blocks’ auto-release settings within the blocks themselves match the organization’s policy.

Problem: Aligning blocks, open time, and closed rooms based on staffing and policy. Blocks in the EHR are often not scheduled to start and stop when the OR teams and surgeons expect them to.

Additionally, rooms in the EHR are not consistently marked as “open” or “closed” for time that is reserved for first come, first served and unstaffed time, respectively.

This means metrics such as block utilization and staffed room utilization can be skewed, making it hard to find ways to improve and build trust in the data.

Solution: Review the block schedule with a fine-toothed comb to verify that:

• Block time on the EHR reflects when a surgeon is expected to perform surgery.

• Open time on the EHR grid reflects time that is available for any surgeon to perform surgery.

• Closed rooms in the EHR reflect time periods when staffing is not needed.

Problem: Making “one-off” changes to individual rooms or days instead of updating templates. When OR teams make changes to blocks or the OR schedule, it’s easy to just make a one-time change to a particular block or room. But making one-off changes—such as “removing” an individual block in the EHR—can leave the schedule vulnerable to similar errors that still exist in that block template and may lead to future inaccuracies in block metrics.

Solution: Although making one-time updates to individual records can be appropriate in some scenarios, a best practice is to make changes to the underlying templates for the block schedule so that the data remain consistent in the future.


Data error #3: Inconsistent data or omissions in data for tracking efficiency

When it comes to measuring OR efficiency, the EHR can be cleaned up to guarantee reliable, actionable data. It is crucial to focus not only on recording data, but on recording the right data that will help to address day-to-day challenges.

Problem: Undocumented delay and cancellation reasons. Reasons for delays or cancellations often are either not documented or documented with vague reasons such as “other” or “delay,” which do not help to address the root cause or hold teams accountable.

Solution: Clean up all the delay and cancellation codes in the EHR so they are specific enough to address a next step, while keeping the list manageable. During COVID-19, consider creating cancellation reasons related to the pandemic.

Problem: Timestamp documentation missing. Many OR leaders get requests for data that they may be unfamiliar with, such as additional detail into turnover metrics. Many leaders are not even sure of their EHR’s full capability, much less what data they have right now. This means that certain data analyses may not be possible if the requisite timestamps are not currently documented.

Solution: OR teams should explore the different timestamps in their data, such as setup and cleanup times, and anesthesia start and stop times. They should also agree on which metrics are most important to measure, and, with those metrics in mind, identify timestamps that will be required to document in the future.


Clean data clears the path for optimization

OR time is extremely valuable, and maximizing OR utilization is difficult. However, this task has never been more important than today, as healthcare organizations play “catch up” from surgical procedures that were canceled or delayed in 2020.

Avoiding these three common OR data issues is a step in the right direction toward opening up the OR schedule through access to transparent data and defensible metrics. ✥


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