语音识别系统医疗报告的准确性与医生在医院的经验

Z. Karbasi, K. Bahaadinbeigy, L. Ahmadian, Reza Khajouei, M. Mirzaee
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引用次数: 2

摘要

语音识别(SR)技术已经存在了二十多年。但是,它在卫生保健机构中很少使用,也没有统一应用于所有临床领域。本研究旨在探讨语音识别系统在实际卫生服务环境中四种不同情况下的准确性。我们还报告了医生使用语音识别技术的经验。为了进行这项研究,在专家医生的计算机上安装了NEVISA SR软件professional v.3。医生对预先指定的医疗报告进行了四种不同模式的测试,包括沉默环境下的慢表达、拥挤环境下的慢表达、沉默环境下的快速表达和繁忙环境下的快速表达。结果:语音识别软件的平均准确率最高的是在沉默环境下的慢表情,平均准确率最低的是在忙碌环境下的快表情。在所有参与研究的医生中,53.3%的医生认为语音识别系统的使用促进了工作流程。结论:我们发现软件的准确性普遍高于预期,其使用需要系统升级和操作。为了通过语音识别达到最高水平的识别率和减少错误,还可以考虑环境噪声,软件或硬件类型,参与者的培训和经验等影响因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy of Speech Recognition System’s Medical Report and Physicians' Experience in Hospitals
Introduction: Speech recognition(SR) technology has been existing for more than two decades. But, it has been rarely used in health care institutions and not applied uniformly in all the clinical domains. The aim of this study was to investigate the accuracy of speech recognition system in four different situations in the real environment of health services. We also report physicians' experience of using speech recognition technology.Method:. To do this study, NEVISA SR software professional v.3 was installed on the computers of expert physicians. The pre-designated medical report was tested by the physicians in four different modes including slow expression in a silent environment, slow expression in crowded environments, rapid expression in a silent environment and rapid expression in a busy environment.  After using the speech recognition software by 15 physicians in hospitals, a designed questionnaire was distributed among them. .Results: The results showed that the highest average accuracy of speech recognition software was in the silent environment by slow expression and the minimum average accuracy was in the busy environment by rapid expression. Of all the participants in the study, 53.3% of the physicians believed that the use of speech recognition system promoted the workflow.Conclusion: We found that software accuracy was generally higher than the expectation and its use required to upgrade the system and its operation.  In order to achieve the highest level of recognition rate and error reduction by speech recognition, influential factors such as environmental noise, type of software or hardware, training and experience of participants can be also considered.
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