Radiology Dictation Errors with COVID-19 Protective Equipment: Does Wearing a Surgical Mask Increase the Dictation Error Rate?
COVID-19
Dictation errors
Dictation software
Masks
Personal protective equipment
Speech-recognition
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
received:
29
01
2021
accepted:
04
08
2021
revised:
01
08
2021
pubmed:
26
9
2021
medline:
3
11
2021
entrez:
25
9
2021
Statut:
ppublish
Résumé
Our aim was to determine the effect of wearing a surgical mask on the number and type of dictation errors in unedited radiology reports. IRB review was waived for this prospective matched-pairs study in which no patient data was used. Model radiology reports (n = 40) simulated those typical for an academic medical center. Six randomized radiologists dictated using speech-recognition software with and without a surgical mask. Dictations were compared to model reports and errors were classified according to type and severity. A statistical model was used to demonstrate that error rates for all types of errors were greater when masks are worn compared to when they are not (unmasked: 21.7 ± 4.9 errors per 1000 words, masked: 27.1 ± 2.2 errors per 1000 words; adjusted p < 0.0001). A sensitivity analysis was performed, excluding a reader with a large number of errors. The sensitivity analysis found a similar difference in error rates for all types of errors, although significance was attenuated (unmasked: 16.9 ± 1.9 errors per 1000 words, masked: 20.1 ± 2.2 errors per 1000 words; adjusted p = 0.054). We conclude that wearing a mask results in a near-significant increase in the rate of dictation errors in unedited radiology reports created with speech-recognition, although this difference may be accentuated in some groups of radiologists. Additionally, we find that most errors are minor single incorrect words and are unlikely to result in a medically relevant misunderstanding.
Identifiants
pubmed: 34561781
doi: 10.1007/s10278-021-00502-w
pii: 10.1007/s10278-021-00502-w
pmc: PMC8475440
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1294-1301Informations de copyright
© 2021. Society for Imaging Informatics in Medicine.
Références
CDC (2020) Coronavirus Disease 2019 (COVID-19). In: Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cloth-face-cover-guidance.html . Accessed 12 Jul 2020
Leung NHL, Chu DKW, Shiu EYC, et al (2020) Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat Med 26:676–680. https://doi.org/10.1038/s41591-020-0843-2
doi: 10.1038/s41591-020-0843-2
pubmed: 32371934
pmcid: 8238571
Anfinrud P, Stadnytskyi V, Bax CE, Bax A (2020) Visualizing Speech-Generated Oral Fluid Droplets with Laser Light Scattering. New England Journal of Medicine 382:2061–2063. https://doi.org/10.1056/NEJMc2007800
doi: 10.1056/NEJMc2007800
National Academies of Sciences E (2020) Rapid Expert Consultation on the Possibility of Bioaerosol Spread of SARS-CoV-2 for the COVID-19 Pandemic (April 1, 2020). National Academies Press (US)
Kanal KM, Hangiandreou NJ, Sykes AM, et al (2001) Initial evaluation of a continuous speech recognition program for radiology. J Digit Imaging 14:30–37. https://doi.org/10.1007/s10278-001-0022-z
doi: 10.1007/s10278-001-0022-z
pubmed: 11310913
pmcid: 3489193
Herman SJ (1995) Accuracy of a voice-to-text personal dictation system in the generation of radiology reports. AJR Am J Roentgenol 165:177–180. https://doi.org/10.2214/ajr.165.1.7785581
doi: 10.2214/ajr.165.1.7785581
pubmed: 7785581
Hodgson T, Coiera E (2016) Risks and benefits of speech recognition for clinical documentation: a systematic review. J Am Med Inform Assoc 23:e169-179. https://doi.org/10.1093/jamia/ocv152
doi: 10.1093/jamia/ocv152
pubmed: 26578226
Madisetti V (2018) Video, Speech, and Audio Signal Processing and Associated Standards. CRC Press
Johnson M, Lapkin S, Long V, et al (2014) A systematic review of speech recognition technology in health care. BMC Med Inform Decis Mak 14:94. https://doi.org/10.1186/1472-6947-14-94
Quint LE, Quint DJ, Myles JD (2008) Frequency and Spectrum of Errors in Final Radiology Reports Generated With Automatic Speech Recognition Technology. Journal of the American College of Radiology 5:1196–1199. https://doi.org/10.1016/j.jacr.2008.07.005
doi: 10.1016/j.jacr.2008.07.005
pubmed: 19027683
Hampton T, Crunkhorn R, Lowe N, et al (2020) The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019. J Laryngol Otol 134:577–581. https://doi.org/10.1017/S0022215120001437
Toscano JC, Toscano CM (2021) Effects of face masks on speech recognition in multi-talker babble noise. PLOS ONE 16:e0246842. https://doi.org/10.1371/journal.pone.0246842
Nguyen DD, McCabe P, Thomas D, et al (2021) Acoustic voice characteristics with and without wearing a facemask. Sci Rep 11:5651. https://doi.org/10.1038/s41598-021-85130-8
Faul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41:1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57:289–300
Basma S, Lord B, Jacks LM, et al (2011) Error Rates in Breast Imaging Reports: Comparison of Automatic Speech Recognition and Dictation Transcription. American Journal of Roentgenology 197:923–927. https://doi.org/10.2214/AJR.11.6691
Speech Recognition in Radiology - State of the Market. In: Reaction Data. https://www.reactiondata.com/report/speech-recognition-in-radiology-state-of-the-market/ . Accessed 31 Aug 2021