Centralized Ambulance Destination Determination: A Retrospective Data Analysis to Determine Impact on EMS System Distribution, Surge Events, and Diversion Status

Abstract

Introduction: Emergency medical services (EMS) systems can become impacted by sudden surges that can occur throughout the day, as well as by natural disasters and the current pandemic. Because of this, emergency department crowding and ambulance "bunching," or surges in ambulance-transported patients at receiving hospitals, can have a detrimental effect on patient care and financial implications for an EMS system. The Centralized Ambulance Destination Determination (CAD-D) project was initially created as a pilot project to look at the impact of an active, online base hospital physician and paramedic supervisor to direct patient destination and distribution, as a way to improve ambulance distribution, decrease surges at hospitals, and decrease diversion status.

Methods: The project was initiated March 17, 2020, with a six-week baseline period; it had three additional study phases where the CAD-D was recommended (Phase 1), mandatory (Phase 2), and modified (Phase 3), respectively. We used coefficients of variation (CV) statistical analysis to measure the relative variability between datasets (eg, CAD-D phases), with a lower variation showing better and more even distribution across the different hospitals. We used analysis of co-variability for the CV to determine whether level loading was improved systemwide across the three phases against the baseline period. The primary outcomes of this study were the following: to determine the impact of ambulance distribution across a geographical area by using the CV; to determine whether there was a decrease in surge rates at the busiest hospital in this area; and the effects on diversion.

Results: We calculated the CV of all ratios and used them as a measure of EMS patient distribution among hospitals. Mean CV was lower in Phase 2 as compared to baseline (1.56 vs 0.80 P < 0.05), and to baseline and Phase 3 (1.56 vs. 0.93, P <0.05). A lower CV indicates better distribution across more hospitals, instead of the EMS transports bunching at a few hospitals. Furthermore, the proportion of surge events was shown to be lower between baseline and Phase 1 (1.43 vs 0.77, P <0.05), baseline and Phase 2 (1.43 vs. 0.33, P < 0.05), and baseline and Phase 3 (1.43 vs 0.42, P < 0.05). Diversion was shown to increase over the system as a whole, despite decreased diversion rates at the busiest hospital in the system.

Conclusion: In this retrospective study, we found that ambulance distribution increased across the system with the implementation of CAD-D, leading to better level loading. The surge rates decreased at some of the most impacted hospitals, while the rates of hospitals going on diversion paradoxically increased overall. Specifically, the results of this study showed that there was an improvement when comparing the CAD-D implementation vs the baseline period for both the ambulance distribution across the system (level loading/CV), and for surge events at three of the busiest hospitals in the system.

Figure 1

Flow Diagram with instructions on how to contact CADDie and how to assist with destination determination. Pt, patient; PES, Psychiatric Emergency Services; CADDIe, centralized ambulance destination-determination; ED, emergency department; NSTEMI, non-ST-elevation myocardial infarction; EKG, electrocardiogram; VS, vital signs; UCSF, University of California San Francisco.

Figure 2

Comparing the average daily rate of surge events, in the different phases of the Centralized Ambulance Destination Diversion (CAD-D) project. Hospitals A, B and C showed decreased surge events in the CAD-D phases compared to baseline.

Figure 3

The coefficient of variation* among the baseline, Phase 1, Phase 2, and Phase 3 portions of the project. *Lower coefficient of variation in all phases compared to baseline showed improved level loading of the system with improved patient transport distribution. CV, coefficient of variation.

Figure 4

Daily system diversion totals from implementation of the study to January 2021. Total daily diversion on X-axis compared to total daily transports on y-axis. Total diversion time does not seem to correlate with daily transports.