Time-to-Disposition Delays Are Possible if Patient Is Seen Early
Most ED patients likely are unsure if they were seen at the beginning or end of an EP’s shift. It turns out this affects the time-to-disposition (when the decision is made to either admit or discharge the patient).1
In previous work, Bryan Stenson, MD, found ED provider productivity starts high, then declines throughout the course of a shift as tasks build up on the patients already undergoing evaluation and treatment.2 Based on this finding and his own clinical experience, Stenson wondered whether this affected overall workflow in the ED.
“My colleagues and I felt we were potentially delaying dispositions on patients seen early in the shift, as well as being more likely to pick a disposition for a patient at the end of the shift,” says Stenson, attending physician at Beth Israel Deaconess Medical Center in Boston.
Stenson and colleagues wanted to learn if this was occurring on a widespread basis. They analyzed 50,802 cases over a one-year period. A total of 31,869 patients were seen in the first four hours of a shift, and 18,933 patients were seen in the fifth hour (or later). If patients were seen during the first half of a shift, there was a median time-to-disposition of 3.25 hours. Similar patients who were seen in the last half of a shift recorded a median time-to-disposition of 2.62 hours.
The data validated that quicker decisions were made on patients picked up in the latter half of the EP’s shift. “This confirmed our lived experience of a notable difference in how long it takes to choose a disposition based on when the patient was first seen,” says Stenson.
For EDs, the study findings carry important implications that can affect patient flow. “Providers and administrators must be aware of the behavioral patterns that naturally occur during a shift,” Stenson suggests.
Providers often delay dispositions on patients seen early in the shift in favor of initiating new patient workups. That could affect throughput systemwide since, presumably, some of those patients could have been admitted or discharged earlier. “Similarly, bolusing dispositions at the end of a shift may create additional bottlenecks that can affect flow,” Stenson adds.
Although any delays can cause patient safety concerns in the ED, Stenson does not think the risks of delays are too concerning in this situation because the time differences were on the order of minutes, not hours. In Stenson’s view, understaffing is the greater safety risk. “If scheduling is done in a standard way without accounting for these behaviors, you can introduce greater mismatch between patient demand and provider capacity,” he observes.
Importantly, the study findings reflect the different types of work occurring during an ED shift. At the beginning of a shift, providers prioritize seeing new patients and initiating workups. At the end of a shift, providers transition to following up on lab or imaging results, and making decisions on whether to admit or discharge. “Hopefully, administrators and providers can better understand how work gets done in the ED, and that standard ED metrics are often not static, but change throughout the shift due to many variables,” Stenson offers.
This applies to many other ED metrics, too, such as door-to-physician times, left without being seen rates, and patients-per-hour metrics. “Many common variables are often reported as a single number, when in fact they are dynamic and may vary greatly by hour of day, day of week, or volume in the department,” Stenson explains.
Considering that, a “staircase” model of productivity allows for a better way to match hourly patient demand with provider capacity, according to Stenson and a different group of colleagues.3 These researchers analyzed the demand capacity mismatch when a static rate of patients per hour for providers was used at an academic ED vs. a “staircase” model of resident productivity. The staircase model was a better reflection of actual capacity. EDs may benefit by staggering resident staffing throughout shifts to improve patient flow in the ED. “The staircase model looks at the patients-per-hour metric, identifies how it changes throughout an EP’s shift, and uses that to inform scheduling decisions,” Stenson says.
REFERENCES
- Stenson BA, Antkowiak PS, Balaji L, et al. Analysis of time-to-disposition intervals during early and late parts of an emergency department shift. Am J Emerg Med 2021;50:477-480.
- Stenson BA, Anderson JS, Davis SR. Staffing and provider productivity in the emergency department. Emerg Med Clin North Am 2020;38:589-605.
- Stenson BA, Joseph JW, Antkowiak PS, et al. Understanding demand and capacity mismatch in an academic emergency department using a staircase model of provider capacity and staggered shift start times. J Emerg Med 2021;61:336-343.
Recent study findings reflect the different types of work occurring during an ED shift. At the beginning of a shift, providers prioritize seeing new patients and initiating workups. At the end of a shift, providers transition to following up on lab or imaging results, and making decisions on whether to admit or discharge.
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