Observational studyScandinavian journal of trauma, resuscitation and emergency medicine2020
Efficacy and efficiency of indoor nighttime human external cargo mission simulation in a high-fidelity training Centre.
Observational study in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- Helicopter hoist operations in German mid-range mountains retrospective analysis of incidence, medical characteristics, and mission tactics.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Article
- Helicopter inter-hospital transfer for patients undergoing extracorporeal membrane oxygenation: a retrospective 12-year analysis of a service system.Scandinavian journal of trauma, resuscitation and emergency medicine · 2022Observational
- Airway management in a Helicopter Emergency Medical Service (HEMS): a retrospective observational study of 365 out-of-hospital intubations.BMC emergency medicine · 2022Observational
- Benefits of simulation-based education in hospital emergency departments: A systematic review.Journal of education and health promotion · 2022Review
- Incidence and challenges of helicopter emergency medical service (HEMS) rescue missions with helicopter hoist operations: analysis of 11,228 daytime and nighttime missions in Switzerland.Scandinavian journal of trauma, resuscitation and emergency medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe human external cargo (HEC) operations conducted by Helicopter Emergency Medical Services (HEMS) rarely take place at night, making it difficult for crew members to attain and maintain the level of expertise needed to perform winch operations in the dark. As EASA requirements for training cannot currently be met, we evaluated whether simulation training could be an option.
methodsThis paper reports on a training concept using indoor simulation for the training of nighttime HEC operations. Participants' experience and perceptions were evaluated with a survey and the procedural and economic advantages of the simulation approach were compared with those of the usual outdoor HEC training.
resultsMost participants had limited exposure to real-life nighttime HEC missions before undergoing the simulation-based training. The frequency of training cycles in simulation was much higher compared to conventional training (60 cycles indoors vs. 20 outdoors for HEMS-TC, 20 cycles indoors vs. 4 outdoors for MCM). Trainees perceived that their technical and non-technical skills (NTS) improved with the training. The estimated costs of standard outdoor-based nighttime HEC training (138€ per cycle) are at least 6.5 times higher than the costs of indoor simulated training (approximately 21€ per cycle). With a change to simulation, carbon dioxide emissions could potentially be reduced by more than 35 tons.
conclusionsIndoor simulation training of night HEC operations has advantages with regard to cost-effectiveness, environmental friendliness, and self-reported improvements in skills and knowledge. Its use is feasible and could improve crew and patient safety and fulfill regulatory demands for training intensity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.