BackgroundPreventing and treating hypothermia in prehospital settings is crucial. Several products have been developed to prevent heat loss and actively warm patients in prehospital settings. We compared the efficacy and the surface temperature of different antihypothermia products, using a fluid-based model at two ambient temperatures.MethodsWe tested five active (Blizzard Heat with active pads, Ready-Heat, Ready-Heat-II, Hypothermia Prevention and Management Kit (HPMK), Bair Hugger) and five passive (Blizzard Heat, Heat Reflective Shell, sleeping bag, ‘space blanket’, wool blanket) antihypothermia products. A torso model consisting of four 8 L bags of fluid preheated to 36°C±0.5°C (97±0.5°F) was used to compare the devices’ performances at 20°C (68°F) and 8°C (46°F). Inner and surface temperatures were recorded for up to 480 min.ResultsWe found significant differences in heat loss in fluid bags among the tested devices at both temperatures (p<0.001). At 20°C, only HPMK and Ready-Heat-II increased the inner temperature for 480 min while Blizzard Heat with active pads prevented heat loss. Ready-Heat prevented heat loss for 90 min. All the other devices did not prevent heat loss beyond 30 min. At 8°C, none of the products heated the model. Bair Hugger, HPMK, Ready-Heat II and sleeping bag prevented heat loss for 30 min. At 60, 90 and 120 min HPMK, Ready-Heat II and Bair Hugger were the most effective. Over 480 min, Bair Hugger was most effective, with a heat loss of 2.3°C±0.4°C. The surface temperature exceeded 44°C (111°F) for all the exothermic warming devices when used for a prolonged period of time.ConclusionAt 20°C, HPMK and Ready-Heat-II increased fluid temperature in the model, while the other devices decreased heat loss. At 8°C, none of the tested devices increased the temperature. However, active heating devices prevented heat loss slightly better than passive methods. A protective insulation layer should be used with all active heating blankets.

Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training. Ideally, the process of filling the checklist should be done by a fully-aware objective system, capable of recognizing and monitoring the clinical performance. To this end, we have developed an autonomous and a fully automatic speech-based checklist system, capable of objectively identifying and validating anesthesia residents' actions in a simulation environment. Based on the analyzed results, our system is capable of recognizing most of the tasks in the checklist: F 1 score of 0.77 for all of the tasks, and F 1 score of 0.79 for the verbal tasks. Developing an audio-based system will improve the experience of a wide range of simulation platforms. Furthermore, in the future, this approach may be implemented in the operation room and emergency room. This could facilitate the development of automatic assistive technologies for these domains.