TY  - JOUR
AU  - Woo, MinJae
AU  - Mishra, Prabodh
AU  - Lin, Ju
AU  - Kar, Snigdhaswin
AU  - Deas, Nicholas
AU  - Linduff, Caleb
AU  - Niu, Sufeng
AU  - Yang, Yuzhe
AU  - McClendon, Jerome
AU  - Smith, D Hudson
AU  - Shelton, Stephen L
AU  - Gainey, Christopher E
AU  - Gerard, William C
AU  - Smith, Melissa C
AU  - Griffin, Sarah F
AU  - Gimbel, Ronald W
AU  - Wang, Kuang-Ching
PY  - 2021
DA  - 2021/10/12
TI  - Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study
JO  - JMIR Mhealth Uhealth
SP  - e32301
VL  - 9
IS  - 10
KW  - emergency medical services
KW  - prehospital documentation
KW  - speech recognition software
KW  - natural language processing
KW  - military medicine
KW  - documentation
KW  - development
KW  - challenge
KW  - paramedic
KW  - disruption
KW  - attention
KW  - medical information
KW  - audio
KW  - speech recognition
KW  - qualitative
KW  - simulation
AB  - Background: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. Objective: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. Methods: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. Results: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). Conclusions: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2021/10/e32301
UR  - https://doi.org/10.2196/32301
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34636729
DO  - 10.2196/32301
ID  - info:doi/10.2196/32301
ER  -