Peer-reviewed research
Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists
Li Zhou et al., JAMA Network Open, 2018 · DOI 10.1001/jamanetworkopen.2018.0530
Read the originalRead on October 16, 2026
- What it is
- A cross-sectional study of 217 clinical notes dictated by 144 physicians at two US health care organizations in 2016. It counted errors at each stage: the draft produced by speech recognition, the version edited by a medical transcriptionist, and the note the physician signed.
- How much weight it carries
- Peer-reviewed, open access and funded by the Agency for Healthcare Research and Quality. It covers one commercial medical engine (Dragon Medical 360 with eScription) used in 2016 with professional editing, so error rates for modern engines and for unedited personal dictation will differ.
Facts taken from this source
- 96.3 percent of the speech recognition drafts contained at least one error, against 58.1 percent after transcriptionist editing and 42.4 percent of signed notes.
“Overall, 96.3% of SR notes, 58.1% of MT notes, and 42.4% of SNs contained errors.” (Abstract, Results)
- After a medical transcriptionist edited the drafts, the error rate fell to 0.4 percent, and to 0.3 percent in the notes physicians signed.
“It decreased to 0.4% after transcriptionist review and 0.3% in SNs.” (Abstract, Results)
- In clinical notes dictated in 2016, the draft produced by speech recognition had an error rate of 7.4 percent, that is 7.4 errors per 100 words.
“The error rate in SR notes was 7.4% (ie, 7.4 errors per 100 words).” (Abstract, Results)
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