Errors in Clinical Notes Generated by Speech Recognition Are Not Uncommon

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Approximately 7% of every 100 words in clinical documentation that has been created by speech recognition technology are incorrectly dictated.
Approximately 7% of every 100 words in clinical documentation that has been created by speech recognition technology are incorrectly dictated.

Approximately 7% of every 100 words in clinical documentation that has been created by speech recognition technology are incorrectly dictated, according to a study published in JAMA Network Open. These findings demonstrate the importance of manual review, as well as quality assurance and auditing by human editors.

In a cross-sectional analysis, investigators obtained a collection of 217 clinical documents created by speech recognition, including office notes (n=83), discharge summaries (n=75), and operative notes (n=59). A total of 144 physicians had used speech recognition (Dragon Medical 360 | eScription [Nuance]) to create these notes throughout 2016.

Investigators analyzed the documentation for errors annotated in the speech recognition engine-generated documentation, the transcriptionist-edited document, and the physician's signed note, and compared the documents with the audio recordings and medical records.

Among all speech recognition notes, the error rate was 7.4%, or 7.4 errors for every 100 words. After transcriptionist review, the errors decreased to 0.4%. In addition, signed notes resulted in a further reduction in errors to 0.3%. The majority of speech recognition notes contained errors (96.3%) compared with transcriptionist notes (58.1%) and signed notes (42.4%).

Compared with other documentation types, discharge summaries demonstrated higher mean speech recognition error rates (8.9% vs 6.6%; difference, 2.3%; 95% CI, 1.0%-3.6%; P <.001). Comparatively, speech recognition notes generated by surgeons had a lower mean error rate compared with those of other physicians (6.0% vs 8.1%; difference, 2.2%; 95% CI, 0.8%-3.5%; P =.002).

The study is limited by its inclusion of a relatively small number of notes from limited clinical settings, reducing the findings' generalizability across the entirety of clinical care.

Developing automated methods for detecting and correcting errors in speech recognition-generated documentation is "vital to ensuring the effective use of clinicians' time and to improving and maintaining documentation quality, all of which can, in turn, increase patient safety."

Reference

Zhou L, Blackley SV, Kowalski L, et al. Analysis of errors in dictated clinical documents assisted by speech recognition software and professional transcriptionists. JAMA Network Open. 2018;1(3):e180530.

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