The SDMPH 10-year anniversary conference created an opportunity for a researcher to present at a professional association conference to advance their research by seeking consensus of statements using Delphi methodology.

Conference attendees and SDMPH members who did not attend the conference were identified as Delphi experts. Experts rated their agreement of each statement on a 7- point linear numeric scale. Consensus amongst experts was defined as a standard deviation < = 1. Presenters submitted statements relevant to advancing their research to the authors to edit to fit Delphi statement formatting.

Statements attaining consensus were included in the final report after the first round. Those not attaining consensus moved to the second round in which experts were shown the mean response of the expert panel and their own response for opportunity to reconsider their rating for that round. If reconsideration attained consensus, these statements were included in the final report. This process repeated in a third and final round.

37 Experts agreed to participate in the first round; 35 completed the second round, and 34 completed the third round; 35 statements attained consensus; 3 statements did not attain consensus.

A Delphi technique was used to establish expert consensus of statements submitted by the SDMPH conference presenters to guide their future education, research, and training.

The 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?”

Five 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.

A 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%.

This 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.

Emerging evidence is guiding changes in prehospital management of potential spinal injuries. The majority of settings related to current recommendations are in resource-rich environments (RREs), whereas there is a lack of guidance on the provision of spinal motion restriction (SMR) in resource-scarce environments (RSEs), such as: mass-casualty incidents (MCIs); low-middle income countries; complex humanitarian emergencies; conflict zones; and prolonged transport times. The application of Translational Science (TS) in the Disaster Medicine (DM) context was used to develop this study, leading to statements that can be used in the creation of evidence-based clinical guidelines (CGs).

What is appropriate SMR in RSEs?

The first round of this modified Delphi (mD) study was a structured focus group conducted at the World Association for Disaster and Emergency Medicine (WADEM) Congress in Brisbane Australia on May 9, 2019. The result of the focus group discussion of open-ended questions produced ten statements that were added to ten statements derived from Fischer (2018) to create the second mD round questionnaire.

Academic researchers and educators, operational first responders, or first receivers of patients with suspected spinal injuries were identified to be mD experts. Experts rated their agreement with each statement on a seven-point linear numeric scale. Consensus amongst experts was defined as a standard deviation ≤1.0. Statements that were in agreement reaching consensus were included in the final report; those that were not in agreement but reached consensus were removed from further consideration. Those not reaching consensus advanced to the third mD round.

For subsequent rounds, experts were shown the mean response and their own response for each of the remaining statements and asked to reconsider their rating. As above, those that did not reach consensus advanced to the next round until consensus was reached for each statement.

Twenty-two experts agreed to participate with 19 completing the second mD round and 16 completing the third mD round. Eleven statements reached consensus. Nine statements did not reach consensus.

Experts reached consensus offering 11 statements to be incorporated into the creation of SMR CGs in RSEs. The nine statements that did not reach consensus can be further studied and potentially modified to determine if these can be considered in SMR CGs in RSEs.

A systematic literature review (SLR) was performed to elucidate the current triage and treatment of an entrapped or mangled extremity in resource scarce environments (RSEs).

A lead researcher followed the search strategy following inclusion and exclusion criteria. A first reviewer (FR) was randomly assigned sources. One of the 2 lead researchers was the second reviewer (SR). Each determined the level of evidence (LOE) and quality of evidence (QE) from each source. Any differing opinions between the FR and SR were discussed between them, and if differing opinions remained, then a third reviewer (the other lead researcher) discussed the article until a consensus was reached. The final opinion of each article was entered for analysis.

Fifty-eight (58) articles were entered into the final study. There was 1 study determined to be LOE 1, 29 LOE 2, and 28 LOE 3, with 15 determined to achieve QE 1, 37 QE 2, and 6 QE 3.

This SLR showed that there is a lack of studies producing strong evidence to support the triage and treatment of the mangled extremity in RSE. Therefore, a Delphi process is suggested to adapt and modify current civilian and military triage and treatment guidelines to the RSE.

Surge capacity, or the ability to manage an extraordinary volume of patients, is fundamental for hospital management of mass-casualty incidents. However, quantification of surge capacity is difficult and no universal standard for its measurement has emerged, nor has a standardized statistical method been advocated. As mass-casualty incidents are rare, simulation may represent a viable alternative to measure surge capacity.

The objective of the current study was to develop a statistical method for the quantification of surge capacity using a combination of computer simulation and simple process-control statistical tools. Length-of-stay (LOS) and patient volume (PV) were used as metrics. The use of this method was then demonstrated on a subsequent computer simulation of an emergency department (ED) response to a mass-casualty incident.

In the derivation phase, 357 participants in five countries performed 62 computer simulations of an ED response to a mass-casualty incident. Benchmarks for ED response were derived from these simulations, including LOS and PV metrics for triage, bed assignment, physician assessment, and disposition. In the application phase, 13 students of the European Master in Disaster Medicine (EMDM) program completed the same simulation scenario, and the results were compared to the standards obtained in the derivation phase.

Patient-volume metrics included number of patients to be triaged, assigned to rooms, assessed by a physician, and disposed. Length-of-stay metrics included median time to triage, room assignment, physician assessment, and disposition. Simple graphical methods were used to compare the application phase group to the derived benchmarks using process-control statistical tools. The group in the application phase failed to meet the indicated standard for LOS from admission to disposition decision.

This study demonstrates how simulation software can be used to derive values for objective benchmarks of ED surge capacity using PV and LOS metrics. These objective metrics can then be applied to other simulation groups using simple graphical process-control tools to provide a numeric measure of surge capacity. Repeated use in simulations of actual EDs may represent a potential means of objectively quantifying disaster management surge capacity. It is hoped that the described statistical method, which is simple and reusable, will be useful for investigators in this field to apply to their own research.

FrancJM, IngrassiaPL, VerdeM, ColomboD, Della CorteF. A Simple Graphical Method for Quantification of Disaster Management Surge Capacity Using Computer Simulation and Process-control Tools. Prehosp Disaster Med. 2015;30(1):1-7.