Sizing capacity levels in emergency medical services dispatch centers: Using the newsvendor approach

Increased number of dispatches in emergency medical services correlates to response time extension

Aim

This study investigated the correlation between the number of emergency medical service (EMS) dispatches and response time extension. In addition, we conducted a simulation to assess the potential for reducing response times by relocating the ambulance based on the number of dispatches.

Methods

This retrospective observational study analyzed data on patients treated with EMS between May 1 and June 25, 2021, in an urban area (Chiba City, Japan). Spearman's rank correlation tests were used to analyze the correlations among the number of dispatches, response time extension, and ambulance distance. We created a heat map to visualize the number of dispatches and distribution of emergency case occurrences, and simulated the relocation of the EMS team with the lowest number of dispatches to the closest EMS team with the highest number of dispatches.

Results

In total, 7915 emergency cases were included. The median response time across all dispatches was 9 min, whereas that for the response time extension cases was 12 min. There was a significant positive correlation between the increased number of dispatches, response time extension (r = 0.94, p < 0.0001), and ambulance distance (r = 0.95, p < 0.0001). The relocation simulation significantly shortened the average response time from 13 min and 30 s to 12 min and 11 s (9.9% decrease, p < 0.0001).

Conclusion

An increased number of dispatches significantly increased the response time extension cases and ambulance distance. Our simulation suggests that EMS relocation can potentially shorten the response time. While increased dispatches influence the response time extension, optimal EMS allocation may improve response times.

Recovery and stress of control center dispatchers in the first waves of the SARS-CoV-2 pandemic

Control center dispatchers (CCDs) are exposed to high levels of work-related mental stress, which are exacerbated by the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The aim of this study was to comparatively analyze the recovery and stress state of CCDs during the first and second waves of the SARS-CoV‑2 pandemic. A total of 490 CCDs (n = 440, t1 and n = 50, t2) with a mean age of 42.26 ± 8.79 years participated directly at the end of the first wave from June to August 2020 (t1) and during the second wave between January and February 2021 (t2) of the SARS-CoV‑2 pandemic. The short form (EBF-24/A; test form S2) of the Kallus recovery-stress questionnaire (EBF) was applied. Over the course of the two survey phases, the dimensions strain significantly increased (t1: 2.47 ± 1.08 vs. t2: 3.12 ± 0.93 points, p < 0.001) and recovery significantly decreased (t1: 3.03 ± 0.94 vs. t2: 2.50 ± 0.81 points, p < 0.001). Significant to highly significant differences were present in the EBF dimensions of the two waves in the majority of cases, even when taking the waves into account. For the variable "recovery in the social field", a medium effect was noticeable in the corrected model (η² = 0.064). The SARS-CoV‑2 pandemic illustrates that the stress experience increased from the first to the second waves, and the recovery of CCDs decreased. These data provide a directional trend as the pandemic is ongoing, and stress and strain situations in control centers may continue to worsen. Immediate health promotion and prevention measures are essential.

An Examination of the Determination of Medical Capacity under a National Health Insurance Program

This paper examines the capacity determination factors of medical services at a national level through the analysis of a mathematical model that maximizes social welfare, which consists of the consumption of private goods and the medical capacity provided by the society. A sensitivity analysis is conducted to investigate the impact of these factors on the medical capacity provided. Furthermore, a case example based on the data provided by the government is presented to discuss the results derived from the theoretical analysis. The results of the sensitivity analysis indicate that individual disposable income, the medical expenditure for each treatment, the level of premium payments, and substitution parameters have a positive impact on medical capacity, while the medical costs and preference parameter negatively affect medical capacity. The results of the correlation analysis based on the data of the case example are consistent with the findings of the theoretical analysis.

Connect dispatch centers for call handling improves performance

The aim of this Letter to the Editor was to report a strategy to reduce time waiting for emergency calls in a dispatch center, in line with a recently published article that reviewed evidence for medical dispatching systems to accurately dispatch Emergency medical Services. Here, we tested the effect of a connected distribution of calls, where a call is allocated to the first available resource among a pooled group of telecommunicators from several dispatch centers. We found that connect dispatch centers improve dispatch center performance, especially during an overloaded period. It could be leveraged to handle emergency calls without delay and to appropriately dispatch Emergency Medical Services.