A clinician survey of using speech recognition for clinical documentation in the electronic health record

Abstract

Objective: To assess the role of speech recognition (SR) technology in clinicians' documentation workflows by examining use of, experience with and opinions about this technology.

Materials and methods: We distributed a survey in 2016-2017 to 1731 clinician SR users at two large medical centers in Boston, Massachusetts and Aurora, Colorado. The survey asked about demographic and clinical characteristics, SR use and preferences, perceived accuracy, efficiency, and usability of SR, and overall satisfaction. Associations between outcomes (e.g., satisfaction) and factors (e.g., error prevalence) were measured using ordinal logistic regression.

Results: Most respondents (65.3%) had used their SR system for under one year. 75.5% of respondents estimated seeing 10 or fewer errors per dictation, but 19.6% estimated half or more of errors were clinically significant. Although 29.4% of respondents did not include SR among their preferred documentation methods, 78.8% were satisfied with SR, and 77.2% agreed that SR improves efficiency. Satisfaction was associated positively with efficiency and negatively with error prevalence and editing time. Respondents were interested in further training about using SR effectively but expressed concerns regarding software reliability, editing and workflow.

Discussion: Compared to other documentation methods (e.g., scribes, templates, typing, traditional dictation), SR has emerged as an effective solution, overcoming limitations inherent in other options and potentially improving efficiency while preserving documentation quality.

Conclusion: While concerns about SR usability and accuracy persist, clinicians expressed positive opinions about its impact on workflow and efficiency. Faster and better approaches are needed for clinical documentation, and SR is likely to play an important role going forward.

Keywords: Artificial intelligence; Clinical documentation; Efficiency; Natural language processing; Quality of care; Safety; Speech recognition.