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Emergency Department (ED) healthcare workers (HCWs) have had one of the highest incidences of burnout [4-6] even prior to the pandemic. To evaluate the prevalence of burnout in this population and the preferred methods of coping with the COVID-19 situation, we conducted a cross-sectional study amongst doctors and nurses in our regional health cluster's Emergency Departments (EDs) and Urgent Care Center (UCC). Besides the use of technological media, spending time with family and friends and acts of gratitude from the department and peers were preferred methods for coping with the pandemic.

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.

International humanitarian aid during disasters should be needs-based and coordinated in response to appeals from affected governments. We identify disaster and population factors associated with international aid appeal during disasters and hence guide preparation by international humanitarian aid providers. In this retrospective database analysis, we searched the Emergency Events Database for all disasters from 1995 to 2015. Disasters with and without international aid appeals were compared by location, duration, type of disaster, deaths, number of people affected, and total estimated damage. Logistic regression was used to examine the association of each factor with international aid appeal. Of 13,961 disasters recorded from 1995 to 2015, 168 (1.2%) involved international aid appeals. Aid appeals were more likely to be triggered by disasters which killed more people (OR 1.29 [95% confidence interval (CI) 1.02-1.64] log10 persons), affected more people (OR 1.85 [95%CI 1.57-2.18] / log10 persons), and occurred in Africa (OR 1.67 [95%CI 1.06-2.62). Earthquakes (OR 4.07 [95%CI 2.16-7.67]), volcanic activity (OR 6.23 [95%CI 2.50-15.53]), and insect infestations (OR 12.14 [95%CI 3.05-48.35]) were more likely to trigger international aid appeals. International aid appeals were less likely to be triggered by disasters which occurred in Asia (OR 0.46 [95%CI 0.29-0.73]) and which were transport accidents (OR 0.12 [95%CI 0.02-0.89]). International aid appeal during disasters was associated with greater magnitude of damage, disasters in Africa, and specific types of disasters such as earthquakes, volcanic activity, and insect infestations. Humanitarian aid providers can focus preparation on these identified factors.

Study objective We examined the effectiveness of an antimicrobial surface coating for continual disinfection of high touch-frequency surfaces in the emergency department (ED). Methods Following a preliminary observation identifying stretcher rails as the surface with highest touch-frequency in the ED, we conducted a pilot randomized controlled trial involving 96 stretcher rails. The stretchers were randomized to receive an antimicrobial surface coating or placebo coating. Routine cleaning of stretchers subsequently continued as per hospital protocol in both arms. Sampling for total aerobic, gram-positive halophilic, gram-negative and methicillin-resistant Staphylococcus aureus bacteria was performed pre- and post-treatment at 24 h, 7 days and 180 days. Individuals who applied the coating and outcome assessors were blinded to the allocated arms. The primary outcome is contamination of antimicrobial versus placebo rails measured as colony forming units per cm 2 (CFU/cm 2 ). Results Baseline total aerobic bacteria was comparable between placebo and intervention arms (0.84 versus 1.32 CFU/cm 2 , P  = 0.235). Total aerobic bacteria contamination was significantly lower on antimicrobial versus placebo rails at 24 h (0.61 versus 1.01 CFU/cm 2 , median difference 0.40 CFU/cm 2 , 95% confidence interval [CI] 0.01 to 1.01 CFU/cm 2 ). There was a non-statistically significant tendency for contamination to be lower on antimicrobial versus placebo rails at 7 days (1.15 versus 1.50 CFU/cm 2 , median difference 0.35 CFU/cm 2 , 95% CI -0.64 to 1.28 CFU/cm 2 ), but higher at 180 days (2.06 versus 1.84 CFU/cm 2 , median difference − 0.22 CFU/cm 2 , 95% CI -1.19 to 0.78 CFU/cm 2 ). Conclusion This is the first double-blinded, placebo-controlled, randomized trial to evaluate an antimicrobial surface coating on high touch-frequency surfaces in the emergency department. Total aerobic bacteria found on antimicrobial-coated patient transport stretcher rails was significantly lower than placebo rails at 24 h.

Purpose: This study aimed to compare cognitive, non-cognitive, and overall learning outcomes for sepsis and trauma resuscitation skills in novices with virtual patient simulation (VPS) versus in-person simulation (IPS).Methods: A randomized controlled trial was conducted on junior doctors in 1 emergency department from January to December 2022, comparing 70 minutes of VPS (n=19) versus IPS (n=21) in sepsis and trauma resuscitation. Using the nominal group technique, we created skills assessment checklists and determined Bloom’s taxonomy domains for each checklist item. Two blinded raters observed participants leading 1 sepsis and 1 trauma resuscitation simulation. Satisfaction was measured using the Student Satisfaction with Learning Scale (SSLS). The SSLS and checklist scores were analyzed using the Wilcoxon rank sum test and 2-tailed t-test respectively.Results: For sepsis, there was no significant difference between VPS and IPS in overall scores (2.0; 95% confidence interval [CI], -1.4 to 5.4; Cohen’s d=0.38), as well as in items that were cognitive (1.1; 95% CI, -1.5 to 3.7) and not only cognitive (0.9; 95% CI, -0.4 to 2.2). Likewise, for trauma, there was no significant difference in overall scores (-0.9; 95% CI, -4.1 to 2.3; Cohen’s d=0.19), as well as in items that were cognitive (-0.3; 95% CI, -2.8 to 2.1) and not only cognitive (-0.6; 95% CI, -2.4 to 1.3). The median SSLS scores were lower with VPS than with IPS (-3.0; 95% CI, -1.0 to -5.0).Conclusion: For novices, there were no major differences in overall and non-cognitive learning outcomes for sepsis and trauma resuscitation between VPS and IPS. Learners were more satisfied with IPS than with VPS (clinicaltrials.gov identifier: NCT05201950).

Purpose The release of the ChatGPT prototype to the public in November 2022 drastically reduced the barrier to using artificial intelligence by allowing easy access to a large language model with only a simple web interface. One situation where ChatGPT could be useful is in triaging patients arriving to the emergency department. This study aimed to address the research problem: “can emergency physicians use ChatGPT to accurately triage patients using the Canadian Triage and Acuity Scale (CTAS)?”. Methods Six unique prompts were developed independently by five emergency physicians. An automated script was used to query ChatGPT with each of the 6 prompts combined with 61 validated and previously published patient vignettes. Thirty repetitions of each combination were performed for a total of 10,980 simulated triages. Results In 99.6% of 10,980 queries, a CTAS score was returned. However, there was considerable variations in results. Repeatability (use of the same prompt repeatedly) was responsible for 21.0% of overall variation. Reproducibility (use of different prompts) was responsible for 4.0% of overall variation. Overall accuracy of ChatGPT to triage simulated patients was 47.5% with a 13.7% under-triage rate and a 38.7% over-triage rate. More extensively detailed text given as a prompt was associated with greater reproducibility, but minimal increase in accuracy. Conclusions This study suggests that the current ChatGPT large language model is not sufficient for emergency physicians to triage simulated patients using the Canadian Triage and Acuity Scale due to poor repeatability and accuracy. Medical practitioners should be aware that while ChatGPT can be a valuable tool, it may lack consistency and may frequently provide false information.

The use of chemical, biological, radiation, and nuclear (CBRN) weapons is not new, and though rare, it is an issue of concern around the world due to their ability to cause large-scale mass-casualty events and their potential threat to global stability. The purpose of this study is to explore the use of CBRN weapons by non-state actors through analysis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims to better inform health care systems of the potential risks and consequences of such events. Data collection was performed using a retrospective database search through the VNSA CBRN Event database. A total of 565 events were recorded. Five hundred and five (505) events (89.4%) involved single agents while 60 events (10.6%) involved multiple agents. Fatalities numbered 965 for chemical agents, 19 for biological agents, and none for radiological and nuclear events. Injuries numbered 7,540 for chemical agents, 59 for biological agents, 50 for radiological events, and none for nuclear attacks. Fatality and injury per attack was 2.22 and 17.37, respectively, for chemical event agents and 0.15 and 0.48, respectively, for biological event agents. Violent Non-State Actors were responsible for 565 unique events around the world involving the use of CBRN weapons from 1990-2020. The United States (118), Russia (49), and Iraq (43) accounted for the top three countries where these events occurred. While CBRN events remain relatively rare, technological advances have the potential to facilitate the use of such weapons as part of a hybrid warfare strategy with significant repercussions for civilian health and health care systems.

International humanitarian aid during disasters should be needs-based and coordinated in response to appeals from affected governments. We identify disaster and population factors associated with international aid appeal during disasters and hence guide preparation by international humanitarian aid providers. In this retrospective database analysis, we searched the Emergency Events Database for all disasters from 1995 to 2015. Disasters with and without international aid appeals were compared by location, duration, type of disaster, deaths, number of people affected, and total estimated damage. Logistic regression was used to examine the association of each factor with international aid appeal. Of 13,961 disasters recorded from 1995 to 2015, 168 (1.2%) involved international aid appeals. Aid appeals were more likely to be triggered by disasters which killed more people (OR 1.29 [95% confidence interval (CI) 1.02-1.64] log.sub.10 persons), affected more people (OR 1.85 [95%CI 1.57-2.18] / log.sub.10 persons), and occurred in Africa (OR 1.67 [95%CI 1.06-2.62). Earthquakes (OR 4.07 [95%CI 2.16-7.67]), volcanic activity (OR 6.23 [95%CI 2.50-15.53]), and insect infestations (OR 12.14 [95%CI 3.05-48.35]) were more likely to trigger international aid appeals. International aid appeals were less likely to be triggered by disasters which occurred in Asia (OR 0.46 [95%CI 0.29-0.73]) and which were transport accidents (OR 0.12 [95%CI 0.02-0.89]). International aid appeal during disasters was associated with greater magnitude of damage, disasters in Africa, and specific types of disasters such as earthquakes, volcanic activity, and insect infestations. Humanitarian aid providers can focus preparation on these identified factors.

International humanitarian aid during disasters should be needs-based and coordinated in response to appeals from affected governments. We identify disaster and population factors associated with international aid appeal during disasters and hence guide preparation by international humanitarian aid providers. In this retrospective database analysis, we searched the Emergency Events Database for all disasters from 1995 to 2015. Disasters with and without international aid appeals were compared by location, duration, type of disaster, deaths, number of people affected, and total estimated damage. Logistic regression was used to examine the association of each factor with international aid appeal. Of 13,961 disasters recorded from 1995 to 2015, 168 (1.2%) involved international aid appeals. Aid appeals were more likely to be triggered by disasters which killed more people (OR 1.29 [95% confidence interval (CI) 1.02-1.64] log.sub.10 persons), affected more people (OR 1.85 [95%CI 1.57-2.18] / log.sub.10 persons), and occurred in Africa (OR 1.67 [95%CI 1.06-2.62). Earthquakes (OR 4.07 [95%CI 2.16-7.67]), volcanic activity (OR 6.23 [95%CI 2.50-15.53]), and insect infestations (OR 12.14 [95%CI 3.05-48.35]) were more likely to trigger international aid appeals. International aid appeals were less likely to be triggered by disasters which occurred in Asia (OR 0.46 [95%CI 0.29-0.73]) and which were transport accidents (OR 0.12 [95%CI 0.02-0.89]). International aid appeal during disasters was associated with greater magnitude of damage, disasters in Africa, and specific types of disasters such as earthquakes, volcanic activity, and insect infestations. Humanitarian aid providers can focus preparation on these identified factors.

ObjectiveThe release of ChatGPT in November 2022 drastically lowered the barrier to artificial intelligence with an intuitive web-based interface to a large language model. This study addressed the research problem: “Can ChatGPT adequately triage simulated disaster patients using the Simple Triage and Rapid Treatment (START) tool?”MethodsFive trained disaster medicine physicians developed nine prompts. A Python script queried ChatGPT Version 4 with each prompt combined with 391 validated patient vignettes. Ten repetitions of each combination were performed: 35190 simulated triages.ResultsA valid START score was returned In 35102 queries (99.7%). There was considerable variability in the results. Repeatability (use of the same prompt repeatedly) was responsible for 14.0% of overall variation. Reproducibility (use of different prompts) was responsible for 4.1% of overall variation. Accuracy of ChatGPT for START was 61.4% with a 5.0% under-triage rate and a 33.6% over-triage rate. Accuracy varied by prompt between 45.8% and 68.6%.ConclusionsThis study suggests that the current ChatGPT large language model is not sufficient for triage of simulated patients using START due to poor repeatability and accuracy. Medical practitioners should be aware that while ChatGPT can be a valuable tool, it may lack consistency and may provide false information.