Trauma THOMPSON: Clinical Decision Support for the Frontline Medic

Eleanor Birch, Kyle Couperus, Chad Gorbatkin, Andrew W. Kirkpatrick, Juan Wachs, Ross Candelore, Nina Jiang, Oanh Tran, Jonah Beck, Cody Couperus, Jessica McKee, Timothy Curlett, De Anna DeVane, Christopher Colombo

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: U.S. Military healthcare providers increasingly perform prolonged casualty care because of operations in settings with prolonged evacuation times. Varied training and experience mean that this care may fall to providers unfamiliar with providing critical care. Telemedicine tools with audiovisual capabilities, artificial intelligence (AI), and augmented reality (AR) can enhance inexperienced personnel’s competence and confidence when providing prolonged casualty care. Furthermore, implementing offline functionality provides assistance options in communications-limited settings. The intent of the Trauma TeleHelper for Operational Medical Procedure Support and Offline Network (THOMPSON) is to develop (1) a voice-controlled mobile application with video references for procedural guidance, (2) audio narration of each video using procedure mentoring scripts, and (3) an AI-guided intervention system using AR overlay and voice command to create immersive video modeling. These capabilities will be available offline and in downloadable format. Materials and Methods: The Trauma THOMPSON platform is in development. Focus groups of subject matter experts will identify appropriate procedures and best practices. Procedural video recordings will be collected to develop reference materials for the Trauma THOMPSON mobile application and to train a machine learning algorithm on action recognition and anticipation. Finally, an efficacy evaluation of the application will be conducted in a simulated environment. Results: Preliminary video collection has been initiated for tube thoracostomy, needle decompression, cricothyrotomy, intraosseous access, and tourniquet application. Initial results from the machine learning algorithm show action recognition and anticipation accuracies of 20.1% and 11.4%, respectively, in unscripted datasets “in the wild,” notably on a limited dataset. This system performs over 100 times better than a random prediction. Conclusions: Developing a platform to provide real-time, offline support will deliver the benefits of synchronous expert advice within communications-limited and remote environments. Trauma THOMPSON has the potential to fill an important gap for clinical decision support tools in these settings.

Original languageEnglish
Pages (from-to)208-214
Number of pages7
JournalMilitary Medicine
Volume188
DOIs
StatePublished - 1 Nov 2023
Externally publishedYes

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