ArticleInternational journal of medical informatics2016
Incidence of speech recognition errors in the emergency department.
Article in International journal of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech recognition for clinical documentation from 1990 to 2018: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- A hybrid large language model framework for structured data entry from code-switched persian clinical speech.Scientific reports · 2026Article
- An Integrative Review of Automation Integration in Emergency Nursing Practice: Evidence Synthesis and Contextual Application to Rafidia Governmental Hospital, Palestine.Journal of nursing management · 2026Review
- Reclaiming clinical time: The promise and challenges of ambient AI for oncology nurses in the Asia-Pacific region.Asia-Pacific journal of oncology nursing · 2025Article
- Evaluating the Usability, Technical Performance, and Accuracy of Artificial Intelligence Scribes for Primary Care: Competitive Analysis.JMIR human factors · 2025Article
- Speech Recognition Technology and Documentation Efficiency.JAMA network open · 2025Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
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- Expectations for Artificial Intelligence (AI) in Psychiatry.Current psychiatry reports · 2022Review
- Charting Practices to Protect Against Malpractice: Case Reviews and Learning Points.The western journal of emergency medicine · 2022Review
- Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.JMIR mHealth and uHealth · 2021Article
- Asynchronous Speech Recognition Affects Physician Editing of Notes.Applied clinical informatics · 2018Article
- Medical Malpractice Trends: Errors in Automated Speech Recognition.Journal of medical systems · 2018Article
- Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists.JAMA network open · 2018Article
- Incidence of speech recognition errors in the emergency department.International journal of medical informatics · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundPhysician use of computerized speech recognition (SR) technology has risen in recent years due to its ease of use and efficiency at the point of care. However, error rates between 10 and 23% have been observed, raising concern about the number of errors being entered into the permanent medical record, their impact on quality of care and medical liability that may arise. Our aim was to determine the incidence and types of SR errors introduced by this technology in the emergency department (ED).
settingLevel 1 emergency department with 42,000 visits/year in a tertiary academic teaching hospital.
methodsA random sample of 100 notes dictated by attending emergency physicians (EPs) using SR software was collected from the ED electronic health record between January and June 2012. Two board-certified EPs annotated the notes and conducted error analysis independently. An existing classification schema was adopted to classify errors into eight errors types. Critical errors deemed to potentially impact patient care were identified.
resultsThere were 128 errors in total or 1.3 errors per note, and 14.8% (n=19) errors were judged to be critical. 71% of notes contained errors, and 15% contained one or more critical errors. Annunciation errors were the highest at 53.9% (n=69), followed by deletions at 18.0% (n=23) and added words at 11.7% (n=15). Nonsense errors, homonyms and spelling errors were present in 10.9% (n=14), 4.7% (n=6), and 0.8% (n=1) of notes, respectively. There were no suffix or dictionary errors. Inter-annotator agreement was 97.8%.
conclusionsThis is the first estimate at classifying speech recognition errors in dictated emergency department notes. Speech recognition errors occur commonly with annunciation errors being the most frequent. Error rates were comparable if not lower than previous studies. 15% of errors were deemed critical, potentially leading to miscommunication that could affect patient care.
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