JMIR Publications

ABSTRACT

Background:

The Telemedicine and Advanced Technology Research Center (TATRC) commenced a new research portfolio specifically addressing Autonomous Casualty Care (AC2) in 2023. The first project within this portfolio addresses the current and historical challenges of capturing tactical combat casualty care (TCCC) data in operational settings.

Objective:

The initial AC2 effort, the Passive Data Collection using Autonomous Documentation (AutoDoc) research project, conducts systematic, simulated patient and casualty care scenarios, leveraging suites of passive sensors inputs to populate a data repository that will automate future care.

Methods:

To obtain the required data sets, TATRC will engage consented human subject care provider participants in one of six randomized simulated TCCC scenarios. These simulations will leverage mannikins (low and high fidelity) and live simulated patients (e.g., consented human subject actors). All consented participants (e.g., both the care providers and live simulated patients) will be equipped with suites of sensors that will passively collect data on care delivery actions and patient physiology.

Results:

At the time of this publication, data collection using the passive sensor suites inputs is ongoing. Simulated data is being collected at Fort Detrick, Maryland; Fort Sam Houston, Texas; Fort Indiantown Gap, Pennsylvania; Fort Liberty, North Carolina; and a commercial site in Greenville, North Carolina. Over the course of the research project’s data collection period, additional data collection sites will be amended to the institution review board (IRB) protocol as research partnerships and collaborations expand.

Conclusions:

The Military Healthcare System (MHS) lacks real world data sets for TCCC care at the point of injury (POI). Developing a data repository of simulated TCCC data is required as an essential step towards automating TCCC care. If TATRCs research efforts result in the ability to automate care delivery documentation, this will alleviate the cognitive burden of TCCC care providers in austere, chaotic environments. By generating a TCCC data repository through this AutoDoc research project, TATRC will have opportunities to leverage this research data to create machine learning (ML) and artificial intelligence (AI) models to advance passive, automated medical documentation across the healthcare continuum.