Factors associated with ED length of stay during a mass casualty incident

Toward Building Surge Capacity: Potentially Effective Spatial Configurations in Emergency Departments

BACKGROUND

Emergency departments (EDs) have been struggling with overcrowding issues for years. Some spatial configurations have been proposed to improve ED performance in facing overcrowding. Despite similarities with mass casualty incidents (MCIs), when demand for care exceeds the capacity, little is documented about the application of the proposed configurations during MCIs to improve surge capacity.

OBJECTIVES

We aimed to explore the potential of spatial configurations that have been proposed to handle ED overcrowding in daily operations so as to improve surge capacity during MCIs.

METHODS

Using an online Likert-scale survey, 11 spatial design strategies were rated by ED care teams in terms of their potential to improve surge capacity during MCIs.

RESULTS

Responses from 72 participants revealed that establishing an in-house lab was perceived as the most potential strategy, followed by rapid care area, internal waiting rooms, and in-house imaging. In contrast, separate entrance and exit doors, as well as decentralized nurse stations, were perceived as the least potential strategies but also exhibited the most variance in response. Respondents' comments implied that their choice of in-house ancillary services was primarily to improve communication and to reduce turnaround time and risk of errors. Their choice of rapid care and internal waiting areas related to improved flexibility.

CONCLUSIONS

Understanding clinicians' perspectives on potentially effective spatial configurations aids in implementing balanced strategies to better equip EDs to handle overcrowding in daily operations and manage surges during MCIs.

A preliminary study for selecting the appropriate AI-based forecasting model for hospital assets demand under disasters

Purpose

Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

Design/methodology/approach

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

Findings

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

Originality/value

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.

The 2015 Amtrak Philadelphia Train Derailment: After-Action Review of the Emergency Radiology Response at Temple University Health System

PURPOSE

The aim of this article is to assess a large tertiary care medical center's emergency radiology response after the 2015 Amtrak Philadelphia train derailment.

METHODS AND MATERIALS

A total of 55 patients with 308 total CTs and radiographs ordered within 12 hours of arrival to Temple University Health System (combining Temple University Hospital and Episcopal Hospital) emergency departments on May 12 to 13, 2015, were included in this study. A retrospective PACS and electronic medical record chart review of emergency department imaging turnaround times (TAT) during this event was completed and compared with emergency department radiology operations for the same 12-hour period throughout the preceding year. Wilcoxon's rank-sum test analysis was performed.

RESULTS

A total of 308 CTs and radiographs were performed, and 91 radiologically evident injuries were observed in a total of 30 patients, with fractures (n = 51) as the most common type of injury. There were no significant differences in time from patient arrival to beginning of radiological examination (26 min; interquartile range [IQR], 11-58 min) compared with annual median (28 min; IQR, 10-131 min; P = .232). Examination completion TATs were significantly increased (35 min; IQR, 17-112 min) compared with annual median (10 min; IQR, 5-15 min; P < .001), and time required from viewing of the examination by the radiologist to the examination being marked as read was significantly decreased (17 min; IQR, 6-45 min) compared with annual median (248 min; IQR, 126-441 min; P < .001).

CONCLUSIONS

The analysis highlights areas of efficiency in our response but also indicates areas for process improvement in future potential mass casualty events.

Hospitals preparedness using WHO guideline: A systematic review and meta-analysis

Background:

Hospitals play a critical role in providing communities with essential medical care during disasters.

Objectives:

In this article, the key components and recommended actions of WHO (World Health Organization) Hospital emergency response checklist have been considered to identify current practices in disaster/emergency hospital preparedness in actual or potential incidents.

Methods:

Articles were obtained through bibliographic databases, including ISI Web of Science, PubMed, Science Direct, Scopus, Google Scholar, and SID: Scientific information database. Keywords were “Disaster,” “Preparedness,” “Emergency Preparedness,” “Disaster Planning,” “Mass Casualty Incidents,” “Hospital Emergency Preparedness,” “Health Emergency Preparedness,” “Preparedness Response,” and “Emergency Readiness.” Independent reviewers (F.R. and M.H.Y.) screened abstracts and titles for eligibility. STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) checklist was used to qualifying the studies for this review.

Results:

Of 1545 identified studies, 26 articles were implied inclusion criteria. They accounted for nine key components and 92 recommended actions. The majority of principles that had been rigorously recommended at any level of the hospital emergency preparedness were command and control and post-disaster recovery. Surge capacity was considered less frequently.

Conclusion:

We recommend considering the proposed disaster categories by FEMA (Federal Emergency Management Agency). In this framework, different weights for nine components can be considered based on disaster categories. Thus, a more valid and reliable preparedness checklist could be developed.

Optimizing mass casualty burns intensive care organization and treatment using evidence-based outcome predictors

BACKGROUND

Majority of current research focuses on pre-hospital care in mass casualty incidents (MCI) whereas this study is the first to examine multifactorial aspects of intensive care unit (ICU) resource management during a surge in massive burn injury (MBI) patients whilst identifying key outcome predictors that resulted in successful disaster managements.

METHODS

Both critical care, surgical parameters and cost-effectiveness are investigated in patients admitted with severe burns resulting from the explosion. A fully integrated trauma response and expansion of critical care resources in Linkou Chang Gung Memorial Hospital (CGMH) in this incident is analyzed.

RESULTS

52 burn patients were treated in CGMH and 27 patients (51.9%) had TBSA greater than 45% with the mean TBSA of 44.6±20.3%. ICU based management of MBI including early debridement and resource strategizing. The overall mortality rate was 2/52 (3.85%). Patients had an average of 14.8days on mechanical ventilation and 43days as an inpatient in total. Operative treatment wise, 44.2% of patients received escharotomies and each patient received an average of 2 skin grafting procedures. The initial TBSA was a significant predictor for burn wound infection (OR 1.107, 95% CI 1.023-1.298; p=0.011). Each patient cost an average of USD 1035 per TBSA% with an average total cost of USD 50415.

CONCLUSION

With ever increasing chances of terrorist activity in urban areas, hospitals can hopefully increase their preparedness using outcome-predictors presented in this study.

Research of an emergency medical system for mass casualty incidents in Shanghai, China: a system dynamics model

OBJECTIVES

Emergency medical system for mass casualty incidents (EMS-MCIs) is a global issue. However, China lacks such studies extremely, which cannot meet the requirement of rapid decision-support system. This study aims to realize modeling EMS-MCIs in Shanghai, to improve mass casualty incident (MCI) rescue efficiency in China, and to provide a possible method of making rapid rescue decisions during MCIs.

METHODS

This study established a system dynamics (SD) model of EMS-MCIs using the Vensim DSS program. Intervention scenarios were designed as adjusting scales of MCIs, allocation of ambulances, allocation of emergency medical staff, and efficiency of organization and command.

RESULTS

Mortality increased with the increasing scale of MCIs, medical rescue capability of hospitals was relatively good, but the efficiency of organization and command was poor, and the prehospital time was too long. Mortality declined significantly when increasing ambulances and improving the efficiency of organization and command; triage and on-site first-aid time were shortened if increasing the availability of emergency medical staff. The effect was the most evident when 2,000 people were involved in MCIs; however, the influence was very small under the scale of 5,000 people.

CONCLUSION

The keys to decrease the mortality of MCIs were shortening the prehospital time and improving the efficiency of organization and command. For small-scale MCIs, improving the utilization rate of health resources was important in decreasing the mortality. For large-scale MCIs, increasing the number of ambulances and emergency medical professionals was the core to decrease prehospital time and mortality. For super-large-scale MCIs, increasing health resources was the premise.