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Complete and resilient documentation for operational medical environments leveraging mobile hands-free technology in a systems approach: Experimental study

  • Min Jae Woo
  • , Prabodh Mishra
  • , Ju Lin
  • , Snigdhaswin Kar
  • , Nicholas Deas
  • , Caleb Linduff
  • , Sufeng Niu
  • , Yuzhe Yang
  • , Jerome McClendon
  • , D. Hudson Smith
  • , Stephen L. Shelton
  • , Christopher E. Gainey
  • , William C. Gerard
  • , Melissa C. Smith
  • , Sarah F. Griffin
  • , Ronald W. Gimbel
  • , Kuang Ching Wang
  • Kennesaw State University
  • Clemson University
  • Microsoft USA
  • NetApp
  • Prisma Health Richland Hospital

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish
Article numbere32301
JournalJMIR mHealth and uHealth
Volume9
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • Attention
  • Audio
  • Challenge
  • Development
  • Disruption
  • Documentation
  • Emergency medical services
  • Medical information
  • Military medicine
  • Natural language processing
  • Paramedic
  • Prehospital documentation
  • Qualitative
  • Simulation
  • Speech recognition
  • Speech recognition software

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