Reports From the Field

A Novel Emergency Department Surge Protocol: Implementation of a Targeted Response Plan

Journal of Clinical Outcomes Management. 2015 November;22(11)

References

1. Bond K. Interventions to reduce overcrowding in emergency departments. [Technology report no 67.4]. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2006.

2. Richardson DB, et al. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust 2006;184:213–6.

3. Sprivulis PC, et al. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med J Aust 2006; 184:208–12.

4. Asplin BR, Magid DJ, Rhodes KV, et al. A conceptual model of emergency department crowding. Ann Emerg Med 2003; 42:173–80.

5. Affleck A, Parks P, Drummond A, et al. Emergency department overcrowding and access block. CAEP Position Statement. CJEM 2013;15:359–70.

6. Weiss SJ, Derlet R, Arndahl J, et al. Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS). Acad Emerg Med 2004;11:38–50.

7. Weiss SJ, Ernst AA, Nick TG. Comparison of the National Emergency Department Overcrowding Scale and the Emergency Department Work Index for quantifying emergency department crowding. Acad Emerg Med 2006;13:513–8.

8. Jones SS, Allen TL, Welch SJ. An independent evaluation of four quantitative emergency department crowding scales. Acad Emerg Med 2006;13:1204–11

9. Bernstein SL, Verghese V, Leung W, et al. Development and validation of a new index to measure emergency department crowding. Acad Emerg Med 2003;10:938–42

10. General Accounting Office. Hospital emergency departments–crowded conditions vary among hospitals and communities. GAO-03-460. Washington, DC: US General Accounting Office; 2003.

11. Moseley MG, Dickerson CL, Kasey J, et al. Surge: a organizational response to emergency department overcrowding. J Clin Outcomes Manage 2010;17:453–7.

12. Jenkins JL, O’Connor RE, Cone DC. Differentiating large-scale surge versus daily surge. Acad Emerg Med 2006; 13:1169–72.

13. McCarthy ML, Aronsky D, Kelen GD. The measurement of daily surge and its relevance to disaster preparedness. Acad Emerg Med 2006; 13:1138–41.

14. Asplin BR, Flottemesch TJ, Gordon B. Developing models for patient flow and daily surge capacity research. Acad Emerg Med 2006;13:1109–13.

15. Nager AL, Khanna K. Emergency department surge: models and practical implications. J Trauma 2009; 67(2 Suppl):S96–9.

Outcome Measures

The main outcome measure was the frequency of sustained (≥ 6 hours) high surges, a marker of inability to respond effectively. High surges were defined as Moderate and Major surges combined. Our expert group consensus was that combinging the Moderate and Major surge categories to represent “high” surge was reasonable since they both require mobilizing resources on a hospital-wide level, and failure to improve despite 6 continuous hours of actively trying to address such high surges would lead to significantly higher risk for quality of care and patient safety issues.

Secondary outcomes include overall frequency of reaching high surge levels at various components of the Input-Throughput-Output ED flow model, hospital occupancy levels, and care facilitators’ perceptions on workload and overall effectiveness of the surge protocol.

Results

ED Flow

Table 2 presents the summary statistics for both campuses comparing the pre- and post-implementation time periods. During the study period, the average number of daily ED visits decreased slightly by 10 patients per day (pre 439.4, post 429.4, P = 0.04), while the average daily hospital occupancy levels steadily rose above 100% (pre 99.5%, post 101.2%, P = 0.01). Despite rising hospital occupancy levels, the proportion of time the ED reached high surge levels decreased for Input (pre 4.4%, post 2.7%, P = 0.01) and Throughput (pre 20.5%, post 18.1%, P = 0.08) components of ED flow after implementation. The frequency of high surges in the Output component did significantly increase (pre 7.7%, post 10.8%, P = 0.002).

Statistical Process Control Charts

Figure 3 shows SPC charts for the different Input-Throughput-Output components of the 2 different ED campuses over the study period. Daily frequency of sustained high surges lasting 6 or more consecutive hours were plotted along with hospital occupancy levels. The number of times data points rose above the upper limit of the SPC chart (ie, above normal expected variation) pre- and post-intervention were used for statistical comparison. Overall for the 2 campuses combined, the frequency of sustained high surges above normal variation remained stable for Input and Throughput (pre 4.5%, post 0.0%, P = 0.13; pre 3.5%, post 2.7% , P = 0.54) components of the ED flow model, respectively. More importantly, the frequency of sustained high surges in the Output component decreased, reaching statistical significance [pre:7.7% vs post:2.0% , P = 0.01], despite a rise in the total number of times the ED reached severe Output surges and overall hospital occupant levels.

Survey of Care Facilitators

The primary users and drivers of the surge protocol—the care facilitator group—felt strongly that the tool was easy to use and that it made a positive difference. 72% felt that the ED surge protocol has increased their workload but 92% felt that it was good for overall flow of the ED. Specific feedback included having a much more standardized language around communicating (and acting on) surges, and a better overall bird’s-eye view of the department.

Discussion

Despite a call for urgent research on implementing solutions targeting daily ED surges (vs. global ED crowding) over a decade ago at the Academic Emergency Medicine 2006 Consensus Conference [12], little work has been published on distinguishing, measuring, and dealing with ED surges. McCarthy et al proposed the rate of patient arrivals to the ED by time of day as a rudimentary definition of surge, although they provided very little specific guidance on what to do with that information in the setting of responding to spikes in surges [13]. Asplin et al described a number of theoretical models to bridge ED census, daily surges, length of stay and quality of care, however they were never validated in real-life scenarios [14]. A systematic review published in 2009 summarizing articles that described theoretical and practical ED surge responses found a large heterogeneity of different proposed models with little standardization and multiple shortcomings [15].