Training and Decision Support for Battlefield Trauma Care

• Main Authors: Nemeth, Christopher, Amos-Binks, Adam, Pinevich, Yuliya, Burris, Christie, Keeney, Natalie, Rule, Gregory, Pickering, Brian, Laufersweiler, Dawn, Heresevich, Vitaly
• Format: Conference Proceeding
• Language: English
• Subjects: Decision support systems; Electric shock; human-computer interaction; information visualization; Machine learning algorithms; Medical diagnostic imaging; Smart phones; Training; Usability; user interface design
• ISSN: 2577-1655
• DOI: 10.1109/SMC42975.2020.9283216

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.