UETMIS and CRCHUS, CIUSSS de l'Estrie - CHUS, Sherbrooke, Canada.
FMSS, Université de Sherbrooke, Sherbrooke, Canada.
J Med Syst. 2018 Apr 3;42(5):89. doi: 10.1007/s10916-018-0947-0.
Speech recognition is increasingly used in medical reporting. The aim of this article is to identify in the literature the strengths and weaknesses of this technology, as well as barriers to and facilitators of its implementation. A systematic review of systematic reviews was performed using PubMed, Scopus, the Cochrane Library and the Center for Reviews and Dissemination through August 2017. The gray literature has also been consulted. The quality of systematic reviews has been assessed with the AMSTAR checklist. The main inclusion criterion was use of speech recognition for medical reporting (front-end or back-end). A survey has also been conducted in Quebec, Canada, to identify the dissemination of this technology in this province, as well as the factors leading to the success or failure of its implementation. Five systematic reviews were identified. These reviews indicated a high level of heterogeneity across studies. The quality of the studies reported was generally poor. Speech recognition is not as accurate as human transcription, but it can dramatically reduce turnaround times for reporting. In front-end use, medical doctors need to spend more time on dictation and correction than required with human transcription. With speech recognition, major errors occur up to three times more frequently. In back-end use, a potential increase in productivity of transcriptionists was noted. In conclusion, speech recognition offers several advantages for medical reporting. However, these advantages are countered by an increased burden on medical doctors and by risks of additional errors in medical reports. It is also hard to identify for which medical specialties and which clinical activities the use of speech recognition will be the most beneficial.
语音识别在医学报告中越来越多地被使用。本文的目的是在文献中确定该技术的优缺点，以及其实施的障碍和促进因素。通过 2017 年 8 月在 PubMed、Scopus、Cochrane 图书馆和卫生技术评估与传播中心进行了系统的系统评价检索。还查阅了灰色文献。使用 AMSTAR 清单评估了系统评价的质量。主要纳入标准是使用语音识别进行医学报告（前端或后端）。还在加拿大魁北克省进行了一项调查，以确定该省这项技术的传播情况，以及导致其实施成功或失败的因素。确定了五项系统评价。这些综述表明研究之间存在高度的异质性。报告的研究质量普遍较差。语音识别的准确性不如人工转录，但可以大大缩短报告的周转时间。在前端使用中，医生需要花更多的时间进行口述和纠正，而不是人工转录。使用语音识别，主要错误的发生频率要高出三倍。在后端使用中，注意到转录员的生产力有潜在的提高。总之，语音识别为医学报告提供了一些优势。然而，这些优势被医生的工作量增加和医疗报告中出现额外错误的风险所抵消。也很难确定语音识别将对哪些医学专业和哪些临床活动最有益。
