Articles: human.
-
During high-fidelity simulations in the Critical Care Air Transport (CCAT) Advanced course, we identified a high frequency of insulin medication errors and sought strategies to reduce them using a human factors approach. ⋯ This study demonstrates the value of descriptive error analysis during high-fidelity simulation using audio-video review and effective risk mitigation using training and cognitive aids to reduce medication errors in CCAT. As demonstrated by post-intervention observations, a human factors approach successfully reduced decision-based error by using didactic training and cognitive aids and reduced skill-based error using hands-on training. We recommend the development of a Clinical Practice Guideline including an SSI protocol, guidelines for a 2-person check, and a cognitive aid for implementation with deployed CCAT teams. Furthermore, hands-on training for insulin preparation and administration should be incorporated into home station sustainment training to reduced medication errors in the operational environment.
-
Stability operations, including humanitarian assistance and disaster relief missions, are key functions of U.S. Military medicine and the Military Medical Humanitarian Assistance Course (MMHAC) is a 2-day course widely used to prepare military medical personnel for such missions. It focuses on caring for those most vulnerable in the wake of disasters, particularly children. The large-scale humanitarian deployment of military medical providers in support of Operation Allies Welcome/Operation Allies Refuge (OAW/OAR) presents an opportunity to evaluate the preparedness of these providers to care for the needs of the Afghan travelers, so we explored the experiences of military medical providers deployed in support of OAW/OAR to inform improvements in the MMHAC. ⋯ Physicians found the OAR/OAW mission meaningful but also identified challenges related to medical care provision, public health, logistics, and ethical dilemmas that hindered their ability to carry out their medical mission. Lessons learned from OAW/OAR highlight several areas in which the MMHAC training could be augmented and improved to further mitigate these challenges.
-
Pediatr Crit Care Me · Jun 2024
Comparative StudyComparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients.
Generative language models (LMs) are being evaluated in a variety of tasks in healthcare, but pediatric critical care studies are scant. Our objective was to evaluate the utility of generative LMs in the pediatric critical care setting and to determine whether domain-adapted LMs can outperform much larger general-domain LMs in generating a differential diagnosis from the admission notes of PICU patients. ⋯ A smaller LM fine-tuned using notes of PICU patients outperformed much larger models trained on general-domain data. Currently, LMs remain inferior but may serve as an adjunct to human clinicians in real-world tasks using real-world data.