Speech Recognition for Medical Dictation: Overview in Quebec and Systematic Review

  • Thomas G. Poder
  • Jean-François Fisette
  • Véronique Déry
Transactional Processing Systems
Part of the following topical collections:
  1. Transactional Processing Systems


Speech recognition is increasingly used in medical reporting. The aim of this article is to identify in the literature the strengths and weaknesses of this technology, as well as barriers to and facilitators of its implementation. A systematic review of systematic reviews was performed using PubMed, Scopus, the Cochrane Library and the Center for Reviews and Dissemination through August 2017. The gray literature has also been consulted. The quality of systematic reviews has been assessed with the AMSTAR checklist. The main inclusion criterion was use of speech recognition for medical reporting (front-end or back-end). A survey has also been conducted in Quebec, Canada, to identify the dissemination of this technology in this province, as well as the factors leading to the success or failure of its implementation. Five systematic reviews were identified. These reviews indicated a high level of heterogeneity across studies. The quality of the studies reported was generally poor. Speech recognition is not as accurate as human transcription, but it can dramatically reduce turnaround times for reporting. In front-end use, medical doctors need to spend more time on dictation and correction than required with human transcription. With speech recognition, major errors occur up to three times more frequently. In back-end use, a potential increase in productivity of transcriptionists was noted. In conclusion, speech recognition offers several advantages for medical reporting. However, these advantages are countered by an increased burden on medical doctors and by risks of additional errors in medical reports. It is also hard to identify for which medical specialties and which clinical activities the use of speech recognition will be the most beneficial.


Speech recognition Transcription Systematic review Reporting error Productivity Turnaround time 



We thank all our HTA partners in the province of Quebec for their help during the benchmark, as well as Dr. Colette Bellavance, Normand Bilodeau, Mélanie Boisvert, Nathalie Carrier, Dr. Édith Grégoire, Maryse Lachance, Benoît Lebel and Line Ménard, for their collaboration.

Compliance with ethical standards

Conflict of interest

We declare no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Thomas G. Poder
    • 1
    • 2
  • Jean-François Fisette
    • 3
  • Véronique Déry
    • 2
    • 3
  1. 1.UETMIS and CRCHUS, CIUSSS de l’Estrie - CHUSSherbrookeCanada
  2. 2.FMSS, Université de SherbrookeSherbrookeCanada
  3. 3.UETMIS, CIUSSS de l’Estrie - CHUSSherbrookeCanada

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