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在系统方法中利用移动免提技术实现操作性医疗环境的完整和有弹性的文档记录:实验研究。

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

机构信息

School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.

Department of Electrical and Computing Engineering, Clemson University, Clemson, SC, United States.

出版信息

JMIR Mhealth Uhealth. 2021 Oct 12;9(10):e32301. doi: 10.2196/32301.

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.

摘要

背景

院前记录是一项具有挑战性的任务,容易导致信息丢失,因为护理人员在需要不断关注患者的嘈杂环境中工作。

目的

本研究旨在开发一种免提院前记录的移动平台,通过聚合所有针对抗噪和领域自适应的现有解决方案,为急救人员在作业医疗环境中提供帮助。

方法

该平台旨在从受伤现场的实时音频流中提取有意义的医疗信息,并在患者到达前将完整的记录传输到野战医院。为此,我们深入研究了以下具有模块化改进的最先进的自动语音识别(ASR)解决方案:抗噪 ASR、多风格训练、定制词汇表和语音增强。该平台的开发严格遵循定性研究和基于模拟的评估,通过在端到端解决方案的每个过程步骤中逐步改进来解决相关挑战。主要性能指标包括机器转录文本输出的医疗文字错误率(WER)和通过将自动生成的文档与医生的手动文档进行比较计算得出的 F1 分数。

结果

从所有在医生监督的模拟演练中发生的对话中确定了完成记录所需的总共 15139 个独立单词。基线模型的表现不佳,WER 为 69.85%,F1 得分为 0.611。抗噪 ASR、多风格训练和定制词汇表提高了整体性能;与手动文档相比,最终平台的医疗 WER 达到 33.3%,F1 得分为 0.81。语音增强降低了性能,医疗 WER 从 33.3%增加到 46.33%,相应的 F1 分数从 0.81 降低到 0.78。所有性能变化均具有统计学意义(P<.001)。

结论

本研究提出了一种完全适用于作业医疗环境中的免提院前记录的移动平台,并介绍了实施过程中得到的经验教训。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/618b/8548972/df1a9b348176/mhealth_v9i10e32301_fig1.jpg

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