TY - GEN
T1 - Training and Decision Support for Battlefield Trauma Care
AU - Nemeth, Christopher
AU - Amos-Binks, Adam
AU - Pinevich, Yuliya
AU - Burris, Christie
AU - Keeney, Natalie
AU - Rule, Gregory
AU - Pickering, Brian
AU - Laufersweiler, Dawn
AU - Heresevich, Vitaly
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - 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.
AB - 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.
KW - human-computer interaction
KW - information visualization
KW - user interface design
UR - http://www.scopus.com/inward/record.url?scp=85098887981&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098887981&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9283216
DO - 10.1109/SMC42975.2020.9283216
M3 - Conference contribution
AN - SCOPUS:85098887981
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3194
EP - 3199
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Y2 - 11 October 2020 through 14 October 2020
ER -