학술논문

Training and Decision Support for Battlefield Trauma Care
Document Type
Conference
Source
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2020 IEEE International Conference on. :3194-3199 Oct, 2020
Subject
Engineering Profession
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Decision support systems
Machine learning algorithms
Electric shock
Usability
Smart phones
Medical diagnostic imaging
human-computer interaction
user interface design
information visualization
Language
ISSN
2577-1655
Abstract
In Tactical Combat Casualty Care (TCCC), medics perform Role 1 care for battlefield casualties at point of injury by stabilizing them and transporting them to field care facilities such as a Battalion Aid Station (Role 2) or Field Hospital (Role 3) where clinicians provide critical care. Care provider experience and ability vary, and training in the field can help to improve recall and performance of infrequently used critical care skills. This becomes more necessary during Prolonged Field Care (PFC) when evacuation is not immediately available and more complex treatment may be required. Our Trauma Triage Treatment and Training Decision Support (4TDS) project has developed a decision support system (DSS) for Roles 1 and 2. As an application on a Android smart phone and tablet, 4TDS includes training scenarios in skills such as shock identification and management. 4TDS pairs with various vital signs sensors that can stream data for a machine learning algorithm that can detect the probability of shock in a casualty. A "silent test" is comparing algorithm performance with actual clinical diagnoses at Mayo Clinic, Rochester, MN. Usability assessment in an austere field setting will enable us to determine medic and clinician acceptance of 4TDS and how well it supports their decision making. Faster, more accurate decisions can improve TCCC patient care under conditions in which delays can increase morbidity and mortality.