Strategies for Improved Hospital Response to Mass Casualty Incidents

Modelling patient trajectories in emergency department simulations using retrospective patient cohorts

Computer simulations of emergency departments (EDs) are tools that can support managing and optimising ED operations. A core component of ED simulation models is patient trajectories, defined as the series of activities patients undergo in the ED from arrival to discharge. The combined duration of these activities, and waiting times between them, determines a patient's length of stay (LOS). Patient trajectories are often calibrated and validated solely based on the estimated acuity of patients assigned upon arrival. However, acuity is a prospective patient indicator that inconsistently reflects patients' actual urgency and resource usage as seen retrospectively upon discharge. Here, we propose a data-driven ED simulation model in which patient trajectories are modelled based on both acuity and retrospective patient indicators. We show that including retrospective patient indicators recovers the observed LOS distributions more accurately than when using acuity alone. We also demonstrate how the use of retrospective patient indicators leads to more plausible estimates of the impact of increased stress in the ED on patients' LOS. Our work exemplifies how we can better utilise ED data to make the development and evaluation of ED simulation models more accurate and robust, enabling them to provide more reliable and useful operational insights.

Learner evaluation of an immersive virtual reality mass casualty incident simulator for triage training

Background

To minimize loss of life, modern mass casualty response requires swift identification, efficient triage categorization, and rapid hemorrhage control. Current training methods remain suboptimal. Our objective was to train first responders to triage a mass casualty incident using Virtual Reality (VR) simulation and obtain their impressions of the training's quality and effectiveness.We trained subjects in a triage protocol called Sort, Assess, Lifesaving interventions, and Treatment and/or Transport (SALT) Triage then had them respond to a terrorist bombing of a subway station using a fully immersive virtual reality simulation. We gathered learner reactions to their virtual reality experience and post-encounter debriefing with a custom electronic survey. The survey was designed to gather information about participants' demographics and prior experience, including roles, triage training, and virtual reality experience. We then asked them to evaluate the training and encounter and the system's potential for training others.

Results

We received 375 completed evaluation surveys from subjects who experienced the virtual reality encounter. Subjects were primarily paramedics, but also included medical learners as well as other emergency medical service (EMS) professionals. Most participants (95%) recommended the experience for other first responders and rated the simulation (95%) and virtual patients (91%) as realistic. Ninety-four percent (94%) of participants rated the virtual reality simulator as "excellent" or "good." We observed some differences between emergency medical service and medical professionals regarding their prior experience with disaster response training and their opinions on how much the experience contributed to their learning. We observed no differences between subjects with extensive virtual reality experience and those without.

Conclusions

Our virtual reality simulator is an automated, customizable, fully immersive virtual reality system for training and assessing personnel in the proper response to a mass casualty incident. Participants perceived the simulator as an adequate alternative to traditional triage and treatment training and believed that the simulator was realistic and effective for training. Prior experience with virtual reality was not a prerequisite for the use of this system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44247-024-00117-5.

Obstetric operating room staffing and operating efficiency using queueing theory

INTRODUCTION

Strategies to achieve efficiency in non-operating room locations have been described, but emergencies and competing priorities in a birth unit can make setting optimal staffing and operation benchmarks challenging. This study used Queuing Theory Analysis (QTA) to identify optimal birth center operating room (OR) and staffing resources using real-world data.

METHODS

Data from a Level 4 Maternity Center (9,626 births/year, cesarean delivery (CD) rate 32%) were abstracted for all labor and delivery operating room activity from July 2019-June 2020. QTA has two variables: Mean Arrival Rate, λ and Mean Service Rate µ. QTA formulas computed probabilities: P0 = 1-(λ/ µ) and Pn = P0 (λ/µ)n where n = number of patients. P0…n is the probability there are zero patients in the queue at a given time. Multiphase multichannel analysis was used to gain insights on optimal staff and space utilization assuming a priori safety parameters (i.e., 30 min decision to incision in unscheduled CD; ≤ 5 min for emergent CD; no greater than 8 h for nil per os time). To achieve these safety targets, a < 0.5% probability that a patient would need to wait was assumed.

RESULTS

There were 4,017 total activities in the operating room and 3,092 CD in the study period. Arrival rate λ was 0.45 (patients per hour) at peak hours 07:00-19:00 while λ was 0.34 over all 24 h. The service rate per OR team (µ) was 0.87 (patients per hour) regardless of peak or overall hours. The number of server teams (s) dedicated to OR activity was varied between two and five. Over 24 h, the probability of no patients in the system was P0 = 0.61, while the probability of 1 patient in the system was P1 = 0.23, and the probability of 2 or more patients in the system was P≥2 = 0.05 (P3 = 0.006). However, between peak hours 07:00-19:00, λ was 0.45, µ was 0.87, s was 3, P0 was 0.48; P1 was 0.25; and P≥2 was 0.07 (P3 = 0.01, P4 = 0.002, P5 = 0.0003).

CONCLUSION

QTA is a useful tool to inform birth center OR efficiency while upholding assumed safety standards and factoring peaks and troughs of daily activity. Our findings suggest QTA is feasible to guide staffing for maternity centers of all volumes through varying model parameters. QTA can inform individual hospital-level decisions in setting staffing and space requirements to achieve safe and efficient maternity perioperative care.

Management of Mass-Casualty Incidents in Nepal: A Qualitative Case Study of Three District Hospitals in Nepal

INTRODUCTION

The frequency of disasters world-wide has significantly increased in recent years, leading to an increase in the number of mass-casualty incidents (MCIs). These MCIs can overwhelm health care systems, requiring hospitals to respond quickly and effectively, often with limited resources. While numerous studies have identified the challenges in managing MCIs and have emphasized the importance of hospital disaster preparedness, there is a research gap in the preparedness level and response capacities of district hospitals in Nepal.

STUDY OBJECTIVE

This study attempts to fill this gap by understanding the perception of hospital staff in managing MCIs in district hospitals of Nepal.

METHODS

A qualitative case study was conducted in three district hospitals in Nepal. Semi-structured interviews were conducted with the hospital personnel, using an interview guide. An inductive thematic analysis was carried out to understand their perception on the most recent MCI management.

RESULTS

Three themes emerged from the data analysis: enablers in MCI management, barriers in MCI management, and recommendations for the future. Use of multiple communication channels, mobilization of entire hospital teams, mobilization of police in crowd control, presence of disaster store, and pre-identified triage areas were the major enablers that facilitated successful MCI management. Nonetheless, the study also revealed challenges such as a lack of knowledge on MCI response among new staff, disruptions caused by media and visitors, and challenges in implementing triage.

CONCLUSION

This study emphasized the importance of hospital disaster preparedness in managing MCIs and highlighted the significance of overcoming barriers and utilizing enablers for an efficient response. The findings of this study can provide the basis for the Ministry of Health and Population Nepal and district hospitals to plan initiatives for the effective management of MCIs in the future.

Frequency of Mass Casualty Incidents (MCIs) Responded to by Helicopter Emergency Medical Services (HEMS)

OBJECTIVE

Mass casualty incidents (MCIs) challenge out-of-hospital and in-hospital personnel as well as impose significant pressure on available resources. Helicopter emergency medical services (HEMS) crews may be involved in the response to an MCI. Thus, there is epidemiological interest to understand the frequency of MCIs responded to by HEMS.

METHODS

We used a nationally representative database of emergency medical services activations in the United States (National Emergency Medical Services Information System). We queried 911-initiated (scene) activations during the years 2021 and 2022 within the database and paid special attention to activations defined by crews as MCIs. From this, we were able to calculate the frequency at which HEMS crews responded to MCIs relative to the total number of HEMS scene activations.

RESULTS

Of a total of 177,509 HEMS scene activations, less than 1% involved MCIs (0.27%, 486/177,509). Nationally, HEMS crews responded to roughly 2.74 MCIs per 1,000 activations. Variation in MCI frequency was noted across the day of the week, the month of the year, and the time of the day.

CONCLUSION

HEMS crews do respond to MCIs although infrequently. Certain time periods are associated with a greater frequency of MCIs. These data will hopefully be able to inform preparedness and training.

Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China

OBJECTIVE

Today, the development mode of public hospitals in China is turning from expansion to efficiency, and the management mode is turning from extensive to refined. This study aims to evaluate the efficiency of clinical departments in a Chinese class A tertiary public hospital (Hospital M) to analyze the allocation of hospital resources among these departments providing a reference for the hospital management.

METHODS

The hospitalization data of inpatients from 32 clinical departments of Hospital M in 2021 are extracted from the hospital information system (HIS), and a dataset containing 38,147 inpatients is got using stratified sampling. Considering the non-homogeneity of clinical departments, the 38,147 patients are clustered using the K-means algorithm based on workload-related data labels including inpatient days, intensive care workload index, nursing workload index, and operation workload index, so that the medical resource consumption of inpatients from non-homogeneous clinical departments can be transformed into the homogeneous workload of medical staff. Taking the numbers of doctors, nurses, and beds as input indicators, and the numbers of inpatients assigned to certain clusters as output indicators, an input-oriented BCC model is built named the workload-based DEA model. Meanwhile, a control DEA model with the number of inpatients and medical revenue as output indicators is built, and the outputs of the two models are compared and analyzed.

RESULTS

Clustering of 38,147 patients into 3 categories is of better interpretability. 14 departments reach DEA efficient in the workload-based DEA model, 10 reach DEA efficient in the control DEA model, and 8 reach DEA efficient in both models. The workload-based DEA model gives a relatively rational judge on the increase of income brought by scale expansion, and evaluates some special departments like Critical Care Medicine Dept., Geriatrics Dept. and Rehabilitation Medicine Dept. more properly, which better adapts to the functional orientation of public hospitals in China.

CONCLUSION

The design of evaluating the efficiency of non-homogeneous clinical departments with the workload as output proposed in this study is feasible, and provides a new idea to quantify professional medical human resources, which is of practical significance for public hospitals to optimize the layout of resources, to provide real-time guidance on manpower grouping strategies, and to estimate the expected output reasonably.

Beirut Blast: The Experiences of Acute Care Hospitals

Mass Casualty Incidents recently increased in intensity and frequency at an unprecedented rate globally. On August 4, 2020, a massive blast hit the Port of Beirut severely damaging its healthcare sector. This study aims to provide a comprehensive understanding of the impact of the Beirut blast on acute care hospitals in the Beirut area, with a focus on understanding healthcare professionals' (HCPs) responses and encountered challenges. A qualitative research design method was adopted to evaluate the experiences of HCPs at acute hospitals located within 5 kilometers of the blast epicenter. 9 hospitals participated in the study. 11 semi-structured interviews were conducted with key informant HCPs using a designed interview guide. HCPs reported severe infrastructural damages in their corresponding hospitals, and 2 were completely non-functional post-blast. Other than physical injuries sustained by HCPs, the blast imposed substantial strains on their mental health, exacerbated by the ongoing socio-economic crises in Lebanon. Moreover, the findings revealed critical challenges which hindered hospitals' emergency responses at the level of communication, coordination, and human resources, as well as supplies. Participants urged for the need to conduct proper triage, arrange emergency operating centers, and deploy outdoor treatment tents among others, to effectively respond to future disasters. The Beirut blast overwhelmed the Lebanese healthcare system and challenged its level of emergency preparedness. This generated evidence to address the deficiencies and strengthen the existing hospitals' emergency response plans. Future efforts should include prioritizing hospitals' emergency preparedness to ensure the provision of care at increased capacity following the impact of a large-scale disaster.

Development and Evaluation of Innovative and Practical Table-top Exercises Based on a Real Mass-Casualty Incident

PURPOSE

The aim of this work was to develop a table-top exercise (TTX) program for mass-casualty incident (MCI) response based on a real incident to evaluate the program.

METHODS

The TTX program was developed based on the 8 TTX design steps. Convenience sampling was adopted to recruit recently graduated physicians in China. After the TTX training, the participants completed a self-designed questionnaire, as well as the Simulation Design Scale (SDS) and Educational Practices in Simulation Scale (EPSS).

RESULTS

In total, 148 valid questionnaires were collected. The difficulty score of the TTX program was 3.69 ± 0.8. The participants evaluated the program highly, with a score of 4.72 ± 0.54 out of 5. Both the SDS and the EPSS had average scores higher than 4.5. Guided reflection/feedback (M = 4.68, SD = 0.41) and fidelity (M =4.66, SD = 0.57) were the 2 highest-rated SDS subscales. For the EPSS, diverse ways of learning and collaboration were the 2 highest-rated subscales. Multivariate stepwise regression analysis showed that the participants' evaluations of the TTX training course were related to the EPSS score, the difficulty rating, the evaluation of the instructional props, and the degree of participant involvement (F = 24.385, P < 0.001).

CONCLUSIONS

A TTX program for MCIs was developed based on the 2014 Shanghai New Year Crush. The TTX kit is practical and sophisticated, and it provides an effective strategy for MCI training.

Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

OBJECTIVE

The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies.

METHODS

This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care.

RESULTS

Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described.

CONCLUSIONS

These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.

Emotional and psychological implications for healthcare professionals in disasters or mass casualties: A systematic review

AIM

To synthesize and describe the emotional and psychological implications for healthcare professionals who provided care in a mass casualty incident or disaster.

BACKGROUND

The experience of healthcare providers immersed in the actual uncertainty of an ongoing disaster is real, challenging, complex and strongly connected with emotions. Identifying these implications for healthcare professionals is essential for developing strategies to help these professionals deliver high-quality care.

EVALUATION

A systematic review was conducted in PubMed, CINAHL, Scopus, Nursing & Allied Health Database and PsycINFO using published data until February 2021 and following the PRISMA guidelines.

KEY ISSUES

Nineteen articles were included. Factors associated with negative psychological implications were identified and different strategies have been synthesized to prevent or reduce them when caring for the victims of a disaster.

CONCLUSIONS

Feelings of sadness, helplessness, fear and blockage, among others, were identified as common reactions among nurses and other healthcare professionals dealing in mass casualties or disasters. These reactions may lead to post-traumatic disorder, turning professionals into hidden victims.

IMPLICATIONS FOR NURSING MANAGEMENT

Organizations, senior charge nurses and other health service managers need to foster resilience and flexibility among their workforce to improve self-care during a disaster, as well as ensure policies to address a lack of emotional preparedness among their personnel. Some strategies to consider include cognitive behavioural therapy, psychoeducation or meditation.

Surge Capacity and Mass Casualty Incidents Preparedness of Emergency Departments in a Metropolitan City: a Regional Survey Study

BACKGROUND

Emergency departments (EDs) generally receive many casualties in disaster or mass casualty incidents (MCI). Some studies have conceptually suggested the surge capacity that ED should have; however, only few studies have investigated measurable numbers in one community. This study investigated the surge capacity of the specific number of accommodatable patients and overall preparedness at EDs in a metropolitan city.

METHODS

This cross-sectional study officially surveyed surge capacity and disaster preparedness for all regional and local emergency medical centers (EMC) in Seoul with the Seoul Metropolitan Government's public health division. This study developed survey items on space, staff, stuff, and systems, which are essential elements of surge capacity. The number of patients acceptable for each ED was investigated by triage level in ordinary and crisis situations. Multivariate linear regression analysis was performed on hospital resource variables related to surge capacity.

RESULTS

In the second half of 2018, a survey was conducted targeting 31 EMC directors in Seoul. It was found that all regional and local EMCs in Seoul can accommodate 848 emergency patients and 537 non-emergency patients in crisis conditions. In ordinary situations, one EMC could accommodate an average of 1.3 patients with Korean Triage and Acuity Scale (KTAS) level 1, 3.1 patients with KTAS level 2, and 5.7 patients with KTAS level 3. In situations of crisis, this number increased to 3.4, 7.8, and 16.2, respectively. There are significant differences in surge capacity between ordinary and crisis conditions. The difference in surge capacity between regional and local EMC was not significant. In both ordinary and crisis conditions, only the total number of hospital beds were significantly associated with surge capacity.

CONCLUSION

If the hospital's emergency transport system is ideally accomplished, patients arising from average MCI can be accommodated in Seoul. However, in a huge disaster, it may be challenging to handle the current surge capacity. More detailed follow-up studies are needed to prepare a surge capacity protocol in the community.

Staff Perspectives of Mass Casualty Incident Preparedness

Introduction Mass casualty incidents (MCI) are low-frequency, high-risk events that disrupt the day-to-day operations of medical centers. Day-to-day protocols are insufficient for effectively managing MCI events, creating a need to develop and test MCI-specific protocols. The aim of this project was to utilize interviews to gain insight into staff experience and perspective on MCIs and their institution's response plans. Methods Staff members who participated in an MCI drill were asked semi-structured interview questions regarding their perspectives on their current priorities, the information needed to perform their role, and their greatest concerns about an MCI. This quality improvement (QI) project utilized a qualitative methodology to thematically organize the results of the staff responses. Results A total of 64 staff members with various levels of patient care experience were interviewed to reach thematic saturation. The use of staff interviews helped to identify the four primary themes that emerged, which were: 1) process, 2) supplies and resources, 3) communication, and 4) roles. Furthermore, each theme also included a number of subthemes. Conclusions This project demonstrated the importance of staff experiences related to MCI simulation training and preparedness, which may be useful for future training and emergency response planning. Additionally, the results may be helpful for other institutions when building a robust MCI simulation training program or designing an emergency response plan.

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.

Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic

The current COVID-19 pandemic has demonstrated the vulnerability of healthcare systems worldwide. When combined with natural disasters, pandemics can further strain an already exhausted healthcare system. To date, frameworks for quantifying the collective effect of the two events on hospitals are nonexistent. Moreover, analytical methods for capturing the dynamic spatiotemporal variability in capacity and demand of the healthcare system posed by different stressors are lacking. Here, we investigate the combined impact of wildfire and pandemic on a network of hospitals. We combine wildfire data with varying courses of the spread of COVID-19 to evaluate the effectiveness of different strategies for managing patient demand. We show that losing access to medical care is a function of the relative occurrence time between the two events and is substantial in some cases. By applying viable mitigation strategies and optimizing resource allocation, patient outcomes could be substantially improved under the combined hazards.

Healthcare and education networks interaction as an indicator of social services stability following natural disasters

Healthcare and education systems have been identified by various national and international organizations as the main pillars of communities' stability. Understanding the correlation between these main social services institutions is critical to determining the tipping point of communities following natural disasters. Despite being defined as social services stability indicators, to date, no studies have been conducted to determine the level of interdependence between schools and hospitals and their collective influence on their recoveries following extreme events. In this study, we devise an agent-based model to investigate the complex interaction between healthcare and education networks and their overall recovery, while considering other physical, social, and economic factors. We employ comprehensive models to simulate the functional processes within each facility and to optimize their recovery trajectories after earthquake occurrence. The results highlight significant interdependencies between hospitals and schools, including direct and indirect relationships, suggesting the need for collective coupling of their recovery to achieve full functionality of either of the two systems following natural disasters. Recognizing this high level of interdependence, we then establish a social services stability index, which can be used by policymakers and community leaders to quantify the impact of healthcare and education services on community resilience and social services stability.

Assessing resilience of hospitals to cyberattack

OBJECTIVE

This paper investigates the impact on emergency hospital services from initiation through recovery of a ransomware attack affecting the emergency department, intensive care unit and supporting laboratory services. Recovery strategies of paying ransom to the attackers with follow-on restoration and in-house full system restoration from backup are compared.

METHODS

A multi-unit, patient-based and resource-constrained discrete-event simulation model of a typical U.S. urban tertiary hospital is adapted to model the attack, its impacts, and tested recovery strategies. The model is used to quantify the hospital's resilience to cyberattack. Insights were gleaned from systematically designed numerical experiments.

RESULTS

While paying the ransom was found to result in some short-term gains assuming the perpetrators actually provide the decryption key as promised, in the longer term, the results of this study suggest that paying the ransom does not pay off. Rather, paying the ransom, when considered at the end of the event when services are fully restored, precluded significantly more patients from receiving critically needed care. Also noted was a lag in recovery for the intensive care unit as compared with the emergency department. Such a lag must be considered in preparedness plans.

CONCLUSION

Vulnerability to cyberattacks is a major challenge to the healthcare system. This paper provides a methodology for assessing the resilience of a hospital to cyberattacks and analyzing the effects of different response strategies. The model showed that paying the ransom resulted in short-term gains but did not pay off in the longer term.