Evidence map›Paper›PMID 34636729›Full record

ArticleJMIR mHealth and uHealth2021

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.

MinJae Woo, Prabodh Mishra, Ju Lin, Snigdhaswin Kar, Nicholas Deas, Caleb Linduff, Sufeng Niu, Yuzhe Yang, Jerome McClendon, D Hudson Smith and 7 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

MinJae WooSchool of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.ORCID 0000-0001-6015-2100
Prabodh MishraDepartment of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0001-6944-9330
Ju LinDepartment of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0002-6970-4247
Snigdhaswin KarDepartment of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0003-4561-7998
Nicholas DeasSchool of Computing, Clemson University, Clemson, SC, United States.ORCID 0000-0002-2306-5101
Caleb LinduffDepartment of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0001-8554-6958
Sufeng NiuLinkedin Inc, Mountain View, CA, United States.ORCID 0000-0002-2826-0301
Yuzhe YangNetApp, Sunnyvale, CA, United States.ORCID 0000-0001-9210-6002
Jerome McClendonDepartment of Automotive Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0002-5435-0247
D Hudson SmithWatt Family Innovation Center, Clemson University, Clemson, SC, United States.ORCID 0000-0003-3041-4602
Stephen L SheltonDepartment of Emergency Medical Services, Prisma Health Richland Hospital, Columbia, SC, United States.ORCID 0000-0002-0350-5245
Christopher E GaineyDepartment of Emergency Medical Services, Prisma Health Richland Hospital, Columbia, SC, United States.ORCID 0000-0003-3976-5644
William C GerardDepartment of Emergency Medical Services, Prisma Health Richland Hospital, Columbia, SC, United States.ORCID 0000-0002-8111-7352
Melissa C SmithDepartment of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.ORCID 0000-0003-0798-8536
Sarah F GriffinDepartment of Public Health Sciences, Clemson University, Clemson, SC, United States.ORCID 0000-0003-4820-3985
Ronald W GimbelDepartment of Public Health Sciences, Clemson University, Clemson, SC, United States.ORCID 0000-0001-8185-4013
Kuang-Ching WangDepartment of Public Health Sciences, Clemson University, Clemson, SC, United States.ORCID 0000-0002-5675-7104

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients.

objectiveThe 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.

methodsThe 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.

resultsThe 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).

conclusionsThis study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation.

Indexed as

SpeechSpeech Recognition SoftwareDocumentationHumansTechnologyattentionaudiochallengedevelopmentdisruptiondocumentationemergency medical servicesmedical informationmilitary medicinenatural language processingparamedicprehospital documentationqualitativesimulationspeech recognitionspeech recognition software

Identifiers

PMID34636729
PMCPMC8548972

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.