ArticleCritical care explorations2021
Prolonged, High-Fidelity Simulation for Study of Patient Care in Resource-Limited Medical Contexts and for Technology Comparative Effectiveness Testing.
Article in Critical care explorations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 6 citations in OpenAlex.
- Randomized Controlled Trial of Telementoring During Resource-Limited Patient Care Simulation Improves Caregiver Performance and Patient Survival.Critical care explorations · 2024Trial
- Exploring realism in high-fidelity ambulance simulation: ambulance clinicians' perspectives on correspondence with everyday practice.Advances in simulation (London, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most high-fidelity medical simulation is of limited duration, used for education and training, and rarely intended to study medical technology. U.S. caregivers working in prehospital, resource-limited settings may need to manage patients for extended periods (hours to days). This "prolonged casualty care" occurs during military, wilderness, humanitarian, disaster, and space medicine. We sought to develop a standardized simulation model that accurately reflects prolonged casualty care in order to study caregiver decision-making and performance, training requirements, and technology use in prolonged casualty care.
designModel development.
settingHigh-fidelity simulation laboratory. SUBJECTS: None.
interventionsWe interviewed subject matter experts to identify relevant prolonged casualty care medical challenges and selected two casualty types to further develop our model: a large thermal burn model and a severe hypoxia model. We met with a multidisciplinary group of experts in prolonged casualty care, nursing, and critical care to describe how these problems could evolve over time and how to contextualize the problems with a background story and clinical environment with expected resource availability. Following initial scenario drafting, we tested the models with expert clinicians. After multiple tests, we selected the hypoxia model for refinement and testing with inexperienced providers. We tested and refined this model until two research teams could proctor the scenario consistently despite subject performance variability. MEASUREMENTS AND MAIN
resultsWe developed a 6-8-hour simulation model that represented a 14-hour scenario. This model of pneumonia evolved from presentation to severe hypoxia necessitating advanced interventions including airway, breathing, and shock management. The model included: context description, caregiver orientation scripts, hourly progressive physiology tracks corresponding to caregiver interventions, intervention/procedure-specific physiology tracks, intervention checklists, equipment lists, prestudy checklists, photographs of setups, procedure, telementor, and role player scripts, business rules, and data collection methods.
conclusionsThis is the first standardized, high-fidelity simulation model of prolonged casualty care described in the literature. It may be used to assess caregiver performance and patient outcomes resulting from that performance during a complex, 14-hour prolonged casualty care scenario. Because it is standardized, the model may be used to compare differences in the impact of new technologies upon caregiver performance and simulated patient outcomes..
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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.