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To treat many patients despite lacking personnel resources, triage is important in disaster medicine. Various triage algorithms help but often are used incorrectly or not at all. One potential problem-solving approach is to support triage with Smart Glasses. In this study, augmented reality was used to display a triage algorithm and telemedicine assistance was enabled to compare the duration and quality of triage with a conventional one. A specific Android app was designed for use with Smart Glasses, which added information in terms of augmented reality with two different methods-through the display of a triage algorithm in data glasses and a telemedical connection to a senior emergency physician realized by the integrated camera. A scenario was created (ie, randomized simulation study) in which 31 paramedics carried out a triage of 12 patients in 3 groups as follows: without technical support (control group), with a triage algorithm display, and with telemedical contact. A total of 362 assessments were performed. The accuracy in the control group was only 58%, but the assessments were quicker (on average 16.6 seconds). In contrast, an accuracy of 92% (P=.04) was achieved when using technical support by displaying the triage algorithm. This triaging took an average of 37.0 seconds. The triage group wearing data glasses and being telemedically connected achieved 90% accuracy (P=.01) in 35.0 seconds. Triage with data glasses required markedly more time. While only a tally was recorded in the control group, Smart Glasses led to digital capture of the triage results, which have many tactical advantages. We expect a high potential in the application of Smart Glasses in disaster scenarios when using telemedicine and augmented reality features to improve the quality of triage.

This study examined differences in learning outcomes among newborn intensive care unit (NICU) workers who underwent virtual reality simulation (VRS) emergency evacuation training versus those who received web-based clinical updates (CU). Learning outcomes included a) knowledge gained, b) confidence with evacuation, and c) performance in a live evacuation exercise. A longitudinal, mixed-method, quasi-experimental design was implemented utilizing a sample of NICU workers randomly assigned to VRS training or CUs. Four VRS scenarios were created that augmented neonate evacuation training materials. Learning was measured using cognitive assessments, self-efficacy questionnaire (baseline, 0, 4, 8, 12 months), and performance in a live drill (baseline, 12 months). Data were collected following training and analyzed using mixed model analysis. Focus groups captured VRS participant experiences. The VRS and CU groups did not statistically differ based upon the scores on the Cognitive Assessment or perceived self-efficacy. The virtual reality group performance in the live exercise was statistically (P<.0001) and clinically (effect size of 1.71) better than that of the CU group. Training using VRS is effective in promoting positive performance outcomes and should be included as a method for disaster training. VRS can allow an organization to train, test, and identify gaps in current emergency operation plans. In the unique case of disasters, which are low-volume and high-risk events, the participant can have access to an environment without endangering themselves or clients. (Disaster Med Public Health Preparedness. 2019;13:301-308).

The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency. This study addresses the research problem: \"Can ChatGPT adequately triage simulated disaster patients using the START protocol?\" by measuring three outcomes: repeatability, reproducibility, and accuracy. Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard. Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%. This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence-guided tools should undergo rigorous statistical evaluation-using methods such as gage R and R-before implementation into clinical settings.

Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers' efforts have led to the emergence of the disaster eHealth (DEH) field. DEH's main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

This study aimed to understand the current landscape of USA-based disaster medicine (DM) programs through the lens of alumni and program directors (PDs). The data obtained from this study will provide valuable information to future learners as they ponder careers in disaster medicine and allow PDs to refine curricular offerings. Two separate surveys were sent to USA-based DM program directors and alumni. The surveys gathered information regarding current training characteristics, career trajectories, and the outlook of DM training. The study had a 57% response rate among PDs, and 42% response rate from alumni. Most programs are 1-year and accept 1-2 fellows per class. More than 60% of the programs offer additional advanced degrees. Half of the respondents accept international medical graduates (IMGs). Only 25% accept non-MD/DO/MBBs trained applicants. Most of the alumni hold academic and governmental positions post-training. Furthermore, many alumni report that fellowship training offered an advantage in the job market and allowed them to expand their clinical practice. The field of disaster medicine is continuously evolving owing to the increased recognition of the important roles DM specialists play in healthcare. The fellowship training programs are experiencing a similar evolution with an increasing trend toward standardization. Furthermore, graduates from these programs see their training as a worthwhile investment in career opportunities.

Current escalation of natural disasters, pandemics, and humanitarian crises underscores the pressing need for inclusion of disaster medicine in medical education frameworks. Conventional medical training often lacks adequate focus on the complexities and unique challenges inherent in such emergencies. This discourse advocates for the integration of disaster medicine into medical curricula, highlighting the imperative to prepare health-care professionals for an effective response in challenging environments. These competencies encompass understanding mass casualty management, ethical decision-making amidst resource constraints, and adapting health-care practices to varied emergency contexts. Therefore, we posit that equipping medical students with these specialized skills and knowledge is vital for health-care delivery in the face of global health emergencies.

Disaster epidemiology (i.e., applied epidemiology in disaster settings) presents a source of reliable and actionable information for decision-makers and stakeholders in the disaster management cycle. However, epidemiological methods have yet to be routinely integrated into disaster response and fully communicated to response leaders. We present a framework consisting of rapid needs assessments, health surveillance, tracking and registries, and epidemiological investigations, including risk factor and health outcome studies and evaluation of interventions, which can be practiced throughout the cycle. Applying each method can result in actionable information for planners and decision-makers responsible for preparedness, response, and recovery. Disaster epidemiology, once integrated into the disaster management cycle, can provide the evidence base to inform and enhance response capability within the public health infrastructure.

This book provides a comprehensive overview of the essential information that everyone working, or hoping to work in the field of aging, should know about disasters, emergencies, and their effects on the mental health and well-being of older persons. It provides the reader with evidence-based approaches for identifying and classifying mental health problems, such as Post-Traumatic Stress Disorder (PTSD), depression, and substance use disorders in older adults, which may occur during and post disasters/emergencies. Specific attention is given to the special needs and approaches to the care of at-risk groups of older persons such as veterans and holocaust survivors; older adults who are isolated, dependent, have mobility problems, communication deficits, are cognitively impaired, or have other co-morbidities; elders who use meals-on-wheels, vital medications, or home care; or older persons who are in senior centers, nursing homes, or assisted living settings.

The occurrence of major emergencies often leads to environmental damage, property damage, health challenges and life threats. Despite the tremendous progress we have made in responding to the many challenges posed by disasters in recent years, there are still many shortcomings. As an emerging technology widely used in recent years, virtual reality (VR) technology is very suitable for many fields of disaster medicine, such as basic education, professional training, psychotherapy, etc. The purpose of this review article is to introduce the application of VR technology in the disaster medical field and prospect its trend in the future.

This systematic literature review explores the applications of social network platforms for disaster health care management and resiliency and investigates their potential to enhance decision-making and policy formulation for public health authorities during such events. A comprehensive search across academic databases yielded 90 relevant studies. Utilizing qualitative and thematic analysis, the study identified the primary applications of social network data analytics during disasters, organizing them into 5 key themes: communication, information extraction, disaster Management, Situational Awareness, and Location Identification. The findings highlight the potential of social networks as an additional tool to enhance decision-making and policymaking for public health authorities in disaster settings, providing a foundation for further research and innovative approaches in this field. However, analyzing social network data has significant challenges due to the massive volume of information generated and the prevalence of misinformation. Moreover, it is important to point out that social network users do not represent individuals without access to technology, such as some elderly populations. Therefore, relying solely on social network data analytics is insufficient for effective disaster health care management. To ensure efficient disaster management and control, it is necessary to explore alternative sources of information and consider a comprehensive approach.