Posted Emergency Department Wait Times Are Not Always Accurate

Abstract

Objectives: Hospitals around the United States are advertising emergency department (ED) wait times. The objective was to measure the difference between publicly posted and actual ED wait times and to compare these between ED site volumes.Methods: This study was a retrospective consecutive sample of ED patients at one hospital system with four EDs. The wait times of 8,889 patients were included in this analysis. One ED was in a large teaching hospital with 5,000 ED patients per month; the other three were freestanding or community EDs without teaching and with fewer than 2,000 ED patients per month each. The publicly posted ED wait times at the time of patient arrival were recorded and compared to the actual wait times as retrieved from the ED tracking system. The difference between posted and actual wait times for each site was calculated. Separate one-way analysis of variance (ANOVA) tests with post hoc testing were conducted to assess actual wait time and wait time difference between ED sites.Results: Mean and standard deviation (SD) wait time difference at the main ED with a volume of 5,000 patients per month was 31.5 (AE61.2) minutes. At the facilities with fewer than 2,000 ED patients per month each, the differences in wait times were 4.2 (AE21.8), 8.6 (AE23.8), and 1.3 (AE11.9) minutes. ANOVA results revealed that the main ED had significantly different actual wait time and wait time differences (p < 0.05) when compared to the other three EDs. Conclusions:In one hospital system, publicly posted ED wait times show better accuracy in EDs that see 2,000 or fewer patients per month and less accuracy for an ED that sees 5,000 patients per month, likely due to flow confounders. 1-8 This is a marketing campaign to compete for patients who are assumed to have more discretionary income and less discretionary time. It also attempts to create an orderly distribution of patient flow across available capacity. Decreased wait times are good for the patient. If the posted wait times get the patient to the most appropriate ED and the shortest wait, then both the ED and the patient benefit.A literature review failed to reveal any studies examining the accuracy of these increasingly frequent postings. This study analyzed the accuracy of publicly posted ED wait times. We also measured the difference between publicly posted and actual wait times at three different types of EDs. METHODS Study DesignThis was a cross-sectional observational analysis of ED posted wait times compared to actual patient wait times. The study was approved by the institutional review board at Akron General Medical Center. Study Setting and PopulationData were gathered from four separate EDs in a single hospital system in Akron, Ohio: the main ED, two freestanding EDs (FEDs), and a community hospital (CED). The main ED was an urban teaching hospital, a Level I trauma center, an accredited chest pain center, and an

Freestanding Emergency Departments and the Trauma Patient

“…Publicly displayed wait times (eg, billboards, internet, text messaging) throughout the area can easily attract patients to FEDs. One prior study found that publicly posted ED wait times may not always be accurate (10). To the general public, the physical appearance of an injury cannot sufficiently determine whether they need to be seen at an ED or an FED.…”

Displaying emergency patient estimated wait times: A multi‐centre, qualitative study of patient, community, paramedic and health administrator perspectives

“…In efforts to predict wait times, algorithms have been developed and tested for accuracy 6–8 . This is not straightforward and some algorithms are inaccurate 9,10 . Researchers have begun work on system‐wide load‐spreading for patients once ED capacity is predicted 11 …”

Displaying emergency patient estimated wait times: a multi-centre, qualitative study of patient, community, paramedic and health administrator perspectives

“…In efforts to predict wait-times, algorithms have been developed and tested for accuracy(68). This isn’t straightforward and some algorithms are inaccurate(9, 10). Researchers have begun work on system-wide load-spreading for patients once ED capacity is predicted(11).…”