An Analytical Approach for Dispatch Operations of Emergency Medical Services: A Case Study of COVID-19

: Emergency medical services (EMS) aims to deliver timely ambulatory care to incidents in communities. However, the operations of EMS may contend with suddenly increasing demands resulting from unexpected disasters such as disease outbreaks (e.g., COVID-19) or hurricanes. To this end, it usually requires better strategical decisions to dispatch, allocate, and reallocate EMS resources to meet the demand changes over time in terms of demographic and geographic distribution of incidents. In this study, we focus on the operation of the EMS resources (i.e., ambulance dispatch) in response to a demand disruption amid the COVID-19 pandemic. Specifically, we present a analytical framework to (1) analyze the underlying demographic and geographic patterns of emergency incidents and EMS resources;(2) develop a mathematical programming model to identify potential demand gaps of EMS coverage across different districts;and (3) provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. The proposed method is validated with emergency response incident data in New York City for the first COVID-19 surge from March to April 2020. We found that it takes a long incident response time to scenes which reflects unexpected incident demands during COVID-19 surge. To cover such disruptive demands, ambulances need to be reallocated between service districts while meeting the response time standard. The proposed framework can be potentially applied to similar disruptive scenarios in the future and other operational systems disrupted by other disasters. Highlights: We propose an analytical framework using optimization modeling and simulation techniques for EMS resource allocation in response to a demand disruption amid the COVID-19 pandemic.We propose mathematical programming models to identify potential demand gaps of EMS coverage across different districts.We provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. © 2023, The Author(s).

Heatstroke-related ambulance dispatch risk before and during COVID-19 pandemic: Subgroup analysis by age, severity, and incident place.

BACKGROUND: In summer 2020 under the COVID-19 pandemic, the Ministry of Health, Labour and Welfare has made public warnings that specific preventive measures such as maskwearing and stay-at-home orders, may increase heatstroke risk. In our previous work, we found a lower risk of heatstroke-related ambulance dispatches (HSAD) during the COVID-19 period, however, it is uncertain whether similar risk reductions can be observed in different vulnerable subgroups. This study aimed to determine the HSAD risk during the COVID-19 pandemic by age, severity, and incident place subgroups. METHOD: A summer-specific (June-September), time-series analysis was performed, using daily HSAD and meteorological data from 47 Japanese prefectures from 2017 to 2020. A two-stage analysis was applied to determine the association between HSAD and COVID-19 pandemic, adjusting for maximum temperature, humidity, seasonality, and relevant temporal adjustments. A generalized linear model was utilized in the first stage to estimate the prefecture-specific effect estimates. Thereafter, a fixed effect meta-analysis in the second stage was implemented to pool the first stage estimates. Subsequently, subgroup analysis via an interaction by age, severity, and incident place was used to analyze the HSAD risk among subgroups. RESULTS: A total of 274,031 HSAD cases was recorded across 47 Japanese prefectures. The average total number of HSAD in the pre-COVID-19 period was 69,721, meanwhile, the COVID-19 period was 64,869. Highest reductions in the risks was particularly observed in the young category (ratio of relative risk (RRR) = 0.54, 95% Confidential Interval (CI): 0.51, 0.57) compared to the elderly category. Whereas highest increment in the risks were observed in severe/death (RRR = 1.25, 95% CI: 1.13, 1.37) compared to the mild category. CONCLUSION: COVID-19 situation exhibited a non-uniform change in the HSAD risk for all subgroups, with the magnitude of the risks varying by age, severity, and incident place.

Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan.

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.

Effect of the COVID-19 pandemic on heatstroke-related ambulance dispatch in the 47 prefectures of Japan.

In 2020, Coronavirus disease 2019 (COVID-19) pandemic has brought a huge impact in daily life and has prompted people to take preventive measures. In the summertime, however, the Japanese government has cautioned that some COVID-19 pandemic conditions may affect the risk to heatstroke. This study investigated how the COVID-19 pandemic setting affected heatstroke-related ambulance dispatches (HSAD). Daily HSAD data and relevant weather parameters from June to September from 2016 to 2020 of 47 prefectures in Japan were obtained from the Fire and Disaster Management Agency (FDMA) database. A binary variable representing COVID-19 impact was created, whereby years 2016 to 2019 were coded as 0, while 2020 as 1. We employed a two-stage analysis in elucidating the impact of COVID-19 pandemic on HSAD. Firstly, we regressed HSAD with the COVID-19 binary variable after adjusting for relevant covariates to obtain prefecture-specific effect estimates. Prefecture-specific estimates were subsequently pooled via random effects meta-analysis in generating the pooled estimate. Pooled Relative Risk (RR) of HSAD during the COVID-19 pandemic was 0.78 (95% Confidential Interval [CI], 0.75-0.82). We found an overall statistically significant decrease in HSAD risk during the COVID-19 pandemic in Japan. Specifically, the decrease in the risk of HSAD may be linked to the COVID-19 precautionary measures such as stay-home request and availability of alternative consultation services, which may have decreased the direct exposure of the population to extreme heat.