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Learning curve of speech recognition.语音识别的学习曲线。
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2
Improvement of report workflow and productivity using speech recognition--a follow-up study.使用语音识别改善报告工作流程和提高生产力——一项随访研究。
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Frequency and spectrum of errors in final radiology reports generated with automatic speech recognition technology.使用自动语音识别技术生成的最终放射学报告中的错误频率和频谱。
J Am Coll Radiol. 2008 Dec;5(12):1196-9. doi: 10.1016/j.jacr.2008.07.005.
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A clinician survey of using speech recognition for clinical documentation in the electronic health record.临床医生对电子健康记录中使用语音识别进行临床文档记录的调查。
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Speech recognition for clinical documentation from 1990 to 2018: a systematic review.1990 年至 2018 年临床文档的语音识别:系统评价。
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Expediting the turnaround of radiology reports: use of total quality management to facilitate radiologists' report signing.加快放射学报告周转:运用全面质量管理促进放射科医生签署报告
AJR Am J Roentgenol. 1994 Apr;162(4):775-81. doi: 10.2214/ajr.162.4.8140990.

引用本文的文献

1
Speech recognition for clinical documentation from 1990 to 2018: a systematic review.1990 年至 2018 年临床文档的语音识别:系统评价。
J Am Med Inform Assoc. 2019 Apr 1;26(4):324-338. doi: 10.1093/jamia/ocy179.
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Physician experience with speech recognition software in psychiatry: usage and perspective.精神科医生使用语音识别软件的经验:使用情况与观点。
BMC Res Notes. 2018 Oct 1;11(1):690. doi: 10.1186/s13104-018-3790-y.
3
[IT systems in radiology and IT systems for radiologists].[放射学中的信息技术系统及放射科医生使用的信息技术系统]
Radiologe. 2014 Jan;54(1):40-4. doi: 10.1007/s00117-013-2539-9.

本文引用的文献

1
Non-clinical errors using voice recognition dictation software for radiology reports: a retrospective audit.非临床错误使用语音识别听写软件进行放射学报告:回顾性审计。
J Digit Imaging. 2011 Aug;24(4):724-8. doi: 10.1007/s10278-010-9344-z.
2
Voice recognition software: effect on radiology report turnaround time at an academic medical center.语音识别软件:对学术医疗中心放射科报告周转时间的影响。
AJR Am J Roentgenol. 2010 Jul;195(1):194-7. doi: 10.2214/AJR.09.3169.
3
Immediate and sustained benefits of a "total" implementation of speech recognition reporting.语音识别报告“全面”实施的即时和持续效益。
Br J Radiol. 2010 May;83(989):424-7. doi: 10.1259/bjr/58137761. Epub 2010 Mar 11.
4
Letter to the editor re: Voice recognition dictation: radiologist as transcriptionist and Improvement of report workflow and productivity using speech recognition--a follow-up study.致编辑的信:关于语音识别听写:放射科医生作为转录员以及使用语音识别改善报告工作流程和提高生产力——一项后续研究。
J Digit Imaging. 2009 Dec;22(6):560-1. doi: 10.1007/s10278-009-9197-5. Epub 2009 Apr 22.
5
The effect of voice recognition software on comparative error rates in radiology reports.语音识别软件对放射学报告中比较错误率的影响。
Br J Radiol. 2008 Oct;81(970):767-70. doi: 10.1259/bjr/20698753. Epub 2008 Jul 15.
6
Improvement of report workflow and productivity using speech recognition--a follow-up study.使用语音识别改善报告工作流程和提高生产力——一项随访研究。
J Digit Imaging. 2008 Dec;21(4):378-82. doi: 10.1007/s10278-008-9121-4. Epub 2008 Apr 24.
7
Radiology report turnaround: expectations and solutions.放射学报告周转时间:期望与解决方案。
Eur Radiol. 2008 Jul;18(7):1326-8. doi: 10.1007/s00330-008-0905-1. Epub 2008 Mar 8.
8
Improving the utility of speech recognition through error detection.通过错误检测提高语音识别的效用。
J Digit Imaging. 2008 Dec;21(4):371-7. doi: 10.1007/s10278-007-9034-7.
9
Overcoming obstacles to work-changing technology such as PACS and voice recognition.克服诸如图像存档与通信系统(PACS)和语音识别等工作变革技术的障碍。
AJR Am J Roentgenol. 2005 Jun;184(6):1727-30. doi: 10.2214/ajr.184.6.01841727.
10
Expediting the turnaround of radiology reports in a teaching hospital setting.加快教学医院环境下放射学报告的周转速度。
AJR Am J Roentgenol. 1997 Apr;168(4):889-93. doi: 10.2214/ajr.168.4.9124134.

语音识别的学习曲线。

Learning curve of speech recognition.

机构信息

HUS Medical Imaging Center, Helsinki University Central Hospital, PO Box 750, 00029, Helsinki, Finland,

出版信息

J Digit Imaging. 2013 Dec;26(6):1020-4. doi: 10.1007/s10278-013-9614-7.

DOI:10.1007/s10278-013-9614-7
PMID:23779151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3824918/
Abstract

Speech recognition (SR) speeds patient care processes by reducing report turnaround times. However, concerns have emerged about prolonged training and an added secretarial burden for radiologists. We assessed how much proofing radiologists who have years of experience with SR and radiologists new to SR must perform, and estimated how quickly the new users become as skilled as the experienced users. We studied SR log entries for 0.25 million reports from 154 radiologists and after careful exclusions, defined a group of 11 experienced radiologists and 71 radiologists new to SR (24,833 and 122,093 reports, respectively). Data were analyzed for sound file and report lengths, character-based error rates, and words unknown to the SR's dictionary. Experienced radiologists corrected 6 characters for each report and for new users, 11. Some users presented a very unfavorable learning curve, with error rates not declining as expected. New users' reports were longer, and data for the experienced users indicates that their reports, initially equally lengthy, shortened over a period of several years. For most radiologists, only minor corrections of dictated reports were necessary. While new users adopted SR quickly, with a subset outperforming experienced users from the start, identification of users struggling with SR will help facilitate troubleshooting and support.

摘要

语音识别 (SR) 通过缩短报告周转时间来加快患者护理流程。然而,人们对放射科医生需要进行长时间的培训以及增加秘书负担表示担忧。我们评估了具有多年 SR 经验的放射科医生和新接触 SR 的放射科医生必须进行多少校对,以及新用户需要多长时间才能达到有经验用户的水平。我们研究了来自 154 名放射科医生的 250 万份报告的语音识别日志记录,经过仔细排除后,定义了一组 11 名有经验的放射科医生和 71 名新接触 SR 的放射科医生(分别为 24833 份和 122093 份报告)。我们分析了语音文件和报告的长度、基于字符的错误率以及语音识别字典中不包含的单词。有经验的放射科医生对每份报告纠正了 6 个字符,而新用户则纠正了 11 个字符。一些用户的学习曲线非常不利,错误率没有像预期的那样下降。新用户的报告更长,而有经验用户的数据表明,他们的报告最初长度相等,但在几年内缩短了。对于大多数放射科医生来说,只需要对口述报告进行少量的修改。虽然新用户很快就适应了语音识别,但有一部分用户从一开始就表现优于有经验的用户,识别出使用语音识别存在困难的用户将有助于解决问题和提供支持。