Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

Between February 2013 and March 2017, a total of 536,399 patients were assigned via the IVENA system and 79% of them were transferred to hospitals outside the original destination. The number of patients allocated through IVENA between February 2013 and March 2017 increased by 17.57%. At the same time, the number of residents in Munich has officially increased by 07%

Most patients were assigned in March. However, there is no seasonal trend to identify. The working day to which the most work day is assigned is Monday, the most unassigned work day is Sunday. It decreased again Wednesday and then again on the weekend as there was a notable increase in the week after the weekend until it climbs up on Friday.

When requesting a shutdown of the IVENA system, the hospital needs to explain the reason. The most common reason is "overcrowding", followed by "unclassified". It is converted to a single text inserted by the dispatcher.

The steepest increase occurred between 7 am and 8 am. In the afternoon, the two curves stagnated at night and decreased again. Figure 1 shows that hospital closure is direct but delayed as a result of an increase in the number of patients. In this analysis, we quantify and characterize the increase before closure. There is a clear indication that hospital closure is caused by a sudden increase in the number of patients arriving via EMS. The average downtime was 308 minutes (95% confidence interval (CI) [288 minutes; 329 minutes]) and the average closing interval was 1101 minutes (95% CI [1010 minutes; 1192 minutes]).

In the Munich region there is no common policy to decide whether patient load is sufficient to justify closure.

Therefore, several transport policies were tested in the simulation. Policies 2 and 3 show closed time windows for closing until the department reopens. When the number of patients in the department falls below a predetermined threshold, strategy 1 will be resumed.

This is an ecological study to determine if there is a relationship between the mortality rate of children and the movement of ambulances and not determining the causal relationship. The risk of metastasis is based on the presence of a shift in patient hospitalization or emergency visits. Because the THCIC data has been identified, it can not be determined whether patient transfers or transfers are delayed due to the transfer, and whether the delay affects the outcome. Risk of hospitalization for hospitalized patients is based on overlapping transfusion time and hospitalization time, not the actual ED arrival time required for hospitalization. The lack of information on the ED 's arrival time is the obstacle to the inpatient database. Since the time to entry is an important period that may be affected by congestion, we decided to define the transfer of contacts using an overlap time of at least 30 minutes between transfer and admission time.

Movement of ambulance as a congested agent in the emergency department: Influence on the child mortality rate in the metropolitan area

From the beginning we were aware that the movement of ambulances was one of many consequences of ED and hospital congestion. Therefore, in order to maximize the possibility of identifying the study investigating the movement of ambulances, a search strategy to identify all simulation studies of ED congestion, as well as simulation studies of hospital patient procedures including ED Designed. A complete search string for each database is listed in the appendix (including Medline medical terms).

Reduce the movement of ambulances in hospitals and areas: Insights from a systematic evaluation simulation model

We used an a priori defined protocol to search medicine, engineering, and operational research literature and use ED simulation modeling techniques, ED compactness published in 1966 and 2012 and / or Study the movement of ambulances related to hospitalization problems. Based on the definition of Pritsker, we define the simulation as developing the mathematical or logical model of the system and experimental manipulation of the model on the computer. Descriptions of these methods of studying ED congestion have been previously described in the literature. .22 In short, queuing theory is a formal mathematical study. Discrete event simulation is used to analyze complex queuing systems that can not be analyzed algebraically. Discrete event simulation evaluates the effects of multiple individual (ie, discrete) events occurring over time.

Reduce the movement of ambulances in hospitals and areas: Insights from a systematic evaluation simulation model