Literature DB >> 31442847

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

Foster R Goss1, Suzanne V Blackley2, Carlos A Ortega3, Leigh T Kowalski3, Adam B Landman4, Chen-Tan Lin5, Marie Meteer6, Samantha Bakes7, Stephen C Gradwohl8, David W Bates9, Li Zhou10.   

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.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

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

Mesh:

Year:  2019        PMID: 31442847     DOI: 10.1016/j.ijmedinf.2019.07.017

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  7 in total

1.  Application of the i-PARIHS framework in the implementation of speech recognition technology as a way of addressing documentation burden within a mental health context.

Authors:  Brian Lo; Khaled Almilaji; Damian Jankowicz; Lydia Sequeira; Gillian Strudwick; Tania Tajirian
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

Review 2.  Electronic Health Record Optimization and Clinician Well-Being: A Potential Roadmap Toward Action.

Authors:  Tina Shah; Andrea Borondy Kitts; Jeffrey A Gold; Keith Horvath; Alex Ommaya; Opelka Frank; Luke Sato; Gretchen Schwarze; Mark Upton; Lew Sandy
Journal:  NAM Perspect       Date:  2020-08-03

3.  Building the evidence-base to reduce electronic health record-related clinician burden.

Authors:  Christine Dymek; Bryan Kim; Genevieve B Melton; Thomas H Payne; Hardeep Singh; Chun-Ju Hsiao
Journal:  J Am Med Inform Assoc       Date:  2021-04-23       Impact factor: 4.497

Review 4.  What place does nurse-led research have in the COVID-19 pandemic?

Authors:  E Castro-Sánchez; A M Russell; L Dolman; M Wells
Journal:  Int Nurs Rev       Date:  2021-02-10       Impact factor: 3.384

5.  Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.

Authors:  Stephanie Tulk Jesso; Aisling Kelliher; Harsh Sanghavi; Thomas Martin; Sarah Henrickson Parker
Journal:  Front Psychol       Date:  2022-04-07

6.  Evaluation of the clinical application effect of eSource record tools for clinical research.

Authors:  Bin Wang; Xinbao Hao; Xiaoyan Yan; Junkai Lai; Feifei Jin; Xiwen Liao; Hongju Xie; Chen Yao
Journal:  BMC Med Inform Decis Mak       Date:  2022-04-11       Impact factor: 2.796

Review 7.  Applications of natural language processing in ophthalmology: present and future.

Authors:  Jimmy S Chen; Sally L Baxter
Journal:  Front Med (Lausanne)       Date:  2022-08-08
  7 in total

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