Caregivers have a responsibility, in high stress of combat environments, to collect/report data that documents injury event, severity, care, and outcome. Gathering such data accurately and completely is critical to patient care. Accurate data is also needed to support operation/logistics planning, force modeling, casualty forecasting, training, and compliancy. The military and its research partners have already developed effective mobile tools such as Battlefield Medical Information System - Tactical (BMIST) for collecting data at the point of care, but so far the data input interfaces are incapable of reliably capturing critical patient encounter data in the harsh battlefield environments. This research will explore innovative cutting-edge technologies that will enable military care providers to access and enter patient encounter data in combat environments into a BMIST device using a hands- and eyes-free interface. The research will demonstrate how comprehension and context of verbal utterances in BMIST improves human-machine interactions. The utilized technologies will be robust enough to withstand the noise, vibration, and generally harsh environment of the battlefield. Other focus areas include an analysis of innovative technology alternatives for data collection and input at the point of care, creation of a preliminary architecture comprising the most promising approaches and assessment of its feasibility.
Keywords: Bmist Enhancement, Point-Of-Care Data Collection, Natural Language Comprehension (Nlc), Natural Language Generation (Nlg), Human-Machine Interactions, Health Surveillance, Ope
