How to Leverage Analytics to Target ED Backlogs
Avoid Overload with Actionable Solutions
When it comes to addressing backlogs in your emergency department (ED), simple analytical tools—integrated into your care team’s workflow—can help your patient flow.
According to Joseph Guarisco, chair of the operations committee for the American Academy of Emergency Medicine, analytics can make a difference in a hospital’s ED operations by creating better clinical outcomes and capturing much-needed income.
Solving ED backlog is particularly crucial for rural hospitals, where the emergency room is the access point for more than 50 percent of all admits.
“The importance of analytics in ED operations is driven by the realization that much of what we experience in the ED is predictable,” Guarisco says. “What appears to most to be chaos is simply variation around predictable events. Mapping and understanding variation creates the opportunity to predict events within a range of probabilities. Especially, allowing us to solve ED crowding mathematically.”
4 ED Backlog Areas Where Analytics Can Make an Impact
To develop actionable insights about ER backlogs, the key practical consideration for rural hospitals is to focus on how to use data effectively without being overwhelmed by a lot of unstructured data that creates cognitive overload.
Which backlogs in your ED can address through the use of predictive analytics? Here are four common areas ripe for analytics measurement and problem-solving.
In light of insurers’ recent announcement about plans to “review and adjust claims for the most severe and costly ED visits” and an anticipated dent in ED billings, hospitals need tools to ensure that ED patients aren’t stuck waiting for a bed to come open or, worse, leaving without being seen.
To improve patient flow, warning alerts on incoming admits can help staff determine the best use of available bed capacity. Also, analytics tools can help set time parameters for transfer to patient rooms.
Another measurable data point is the impact of bottlenecks around ancillary services. Especially, lab orders, radiology orders, and consult orders. When those services cause a workflow stoppage, ED management can benefit from early identification, causing a cascading effect on time spent in the waiting room.
Current Patient Volumes Against Historical Data
When data gathered and analyzed, the result is a “high level of predictability of the average number of patients who might arrive on a given day, in a given month, in a given year,” Guarisco says. That data also results in a predictable distribution of acuity coming to your ED and a predictable level of tolerance for how long a patient will wait before leaving.
But what about when there’s variation from the average? It’s a tough problem to solve, with hospital leaders rating ED the most difficult clinical area in which to achieve improved results in efficiency and cost reduction. Past performance provides information about the expected average day, while facilities must manage to the inevitable variations to that average.
Real-time Capacity of ED Resources
Analytics can aggregate a number of factors related to backlogs. It includes patient flow, staff saturation, patient wait times, and delays in ancillary services. When those factors are connected to integrated action plans that go into motion when an alert is triggered, staff know what steps to take in order to alleviate a backlog.
The need is immediate: According to Guarisco, if hospitals continue managing ED “using traditional staffing and workflow models, then [hospitals] will be priced out of business from a service and quality perspective.” However, directing analytics tools at key problems and taking into consideration context-specific workflows offer a new way of managing an ED that can enable rural hospitals to survive and thrive.
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