Load Balancing at Emergency Departments using 'Crowdinforming'

Abstract

Background: Emergency Department (ED) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been studied. In theory, optimal ED patient flow may be assisted via balancing patient loads between EDs (in essence spreading patients more evenly throughout this system). This investigation utilizes simulation to explore "Crowdinforming" as a basis for a process control strategy aimed to balance patient loads between six EDs within a mid-sized Canadian city.

Methods: Anonymous patient visit data comprising 120,000 ED patient visits over six months to six ED facilities were obtained from the region's Emergency Department Information System (EDIS) to (1) determine trends in ED visits and interactions between parameters; (2) to develop a process control strategy integrating crowdinforming; and, (3) apply and evaluate the model in a simulated environment to explore the potential impact on patient self-redirection and load balancing between EDs.

Results: As in reality, the data available and subsequent model demonstrated that there are many factors that impact ED patient flow. Initial results suggest that for this particular data set used, ED arrival rates were the most useful metric for ED 'busyness' in a process control strategy, and that Emergency Department performance may benefit from load balancing efforts.

Conclusions: The simulation supports the use of crowdinforming as a potential tool when used in a process control strategy to balance the patient loads between EDs. The work also revealed that the value of several parameters intuitively expected to be meaningful metrics of ED 'busyness' was not evident, highlighting the importance of finding parameters meaningful within one's particular data set. The information provided in the crowdinforming model is already available in a local context at some ED sites. The extension to a wider dissemination of information via an Internet web service accessible by smart phones is readily achievable and not a technological obstacle. Similarly, the system could be extended to help direct patients by including future estimates or predictions in the crowdinformed data. The contribution of the simulation is to allow for effective policy evaluation to better inform the public of ED 'busyness' as part of their decision making process in attending an emergency department. In effect, this is a means of providing additional decision support insights garnered from a simulation, prior to a real world implementation.

Figure 1:

Average LOS at Facility B vs. Patients Waiting

Figure 2:

Length of Stay at Facility F, 0:00–4:00 AM and 12:00–4:00 PM

Figure 3:

Average Waiting To Be Seen and Length Of Stay (running average) over a one month period.

Figure 4:

Arrival and Departure Rates at Facility A.

Figure 5:

Patient Redirection Decision Process

Figure 6:

Arrival Rates at six Facilities with No Crowdinformed Redirection.

Figure 7:

Arrival and Departure Rates at six Facilities with Weighted Fair Crowdinforming Redirection (multiple passes).