Speech recognition technology was tested to prepare protocols for radiological examinations. A method to evaluate the efficiency of speech recognition technology implementation for the preparation of radiological examination protocols has been developed. A time-study was conducted to confirm the effectiveness of voice input. The commitment of radiologist to using innovative technologies in their work practices was evaluated.
References
1. Kauppinen T., Koivikko M.P., Ahovuo J. Improvement of report workflow and productivity using speech recognition – a follow-up study. J Digit Imaging. 2008; 1(4): 378–82. doi : 10.1007/s10278 008 9121 4.
2. White K.S. Speech recognition implementation in radiology. Pediatr Radiol. 2005 ; 35: 841–846.
3. Kumah -Crystal Y.A., Pirtle C.J., Whyte H.M., Goode E.S., Anders S.H., Lehmann C.U. Electronic Health Record Interactions through Voice: A Review. Appl Clin Inform. 2018 ; 9(3): 541–552. doi : 10.1055/s-0038-1666844.
4. Tsou A.Y., Lehmann C.U., Michel J., Solomon R., Possanza L., Gandhi T. Safe Practices for Copy and Paste in the EHR. Systematic Review, Recommendations, and Novel Model for Health IT Collaboration. Appl Clin Inform. 2017 ; 8(1): 12–34. doi : 10.4338/ACI-2016–09-R-0150.
5. Thielke S., Hammond K., Helbig S. Copying and pasting of examinations within the electronic medical record. Int J Med Inform. 2007 ; 76 ( 1 ) : 122–8.
6. Weis J.M., Levy P.C. Copy, paste and cloned notes in electronic health records: prevalence, benefits, risks, and best practice recommendations. Chest. 2014; 145(3): 632–8. doi : 10.1378/chest.13–0886.
7. Grisaffe D.B. Questions about the ultimate question: conceptual considerations in evaluating Reichheld’s net promoter score (NPS). Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 2007 ; 20 : 36.
8. Keiningham T.L. et al. A longitudinal examination of net promoter and firm revenue growth. Journal of Marketing. 2007 ; 71 ( 3 ): 39–51.
9. Shanafelt T.D., Balch C.M., Bechamps G., Russell T., Dyrbye L., Satele D. et al. Burnout and medical errors among American surgeons. Ann Surg. 2010 ; 251(6): 995–1000. doi : 10.1097/SLA.0b013e3181bfdab3.
10. Dyrbye L.N., Shanafelt T.D., Sinsky C.A. et al. Burnout among health care professionals: a call to explore and address this underrecognized threat to safe, high-quality care. NAM Perspectives: discussion paper, National Academy of Medicine, Washington, DC. Available at: https://nam.edu/burnout-among-health-care-professionals. Last a ccessed on: 5 July , 2017.
11. Vogel M., Kaisers W., Wassmuth R., Mayatepek E. Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial. Med Internet Res. 2015 ; 17(11): e247.
12. Ajami S. Use of speech-to-text technology for documentation by healthcare providers. Natl Med J India. 2016 ; 29(3): 148–152.
13. Wilder J.L., Nadar D., Gujral N., Ortiz B., Stevens R., Holder-Niles F. et al. Pediatrician Attitudes toward Digital Voice Assistant Technology Use in Clinical Practice. Appl Clin Inform. 2019 ; 10(2): 286–294.
14. Parente R., Kock N., Sonsini J. An analysis of the implementation and impact of speech-recognition technology in the healthcare sector. Perspect Health Inf Manag. 2004 ; 1:5.
15. Saxena K., Diamond R., Conant R.F., Mitchell T.H., Gallopyn I.G., Yakimow K.E. Provider Adoption of Speech Recognition and its Impact on Satisfaction, Documentation Quality, Efficiency, and Cost in an Inpatient EHR. AMIA Jt Summits Transl Sci Proc. 2018: 186–195.
16. Kauppinen T., Koivikko M.P., Ahovuo J. Improvement of report workflow and productivity using speech recognition – a follow-up study. J Digit Imaging. 2008 ; 21(4): 378–82. doi : 10.1007/s10278 008 9121 4.
17. Rosenthal D.I., Chew F.S., Dupuy D.E., Kattapuram S.V., Palmer W.E., Yap R.M. et al. Computer-based speech recognition as a replacement for medical transcription. AJR Am J Roentgenol. 1998 ; 170(1): 23–5.
18. Hart J.L., McBride A., Blunt D., Gishen P., Strickland N. Immediate and sustained benefits of a «total» implementation of speech recognition reporting. Br J Radiol. 2010 ; 83(989): 424–7. doi : 10.1259/ bjr /58137761.
19. Blackley S.V., Huynh J., Wang L., Korach Z., Zhou L. Speech recognition for clinical documentation from 1990 to 2018: a systematic review. J Am Med Inform Assoc. 2019 ; 26(4): 324–338. doi : 10.1093/ jamia /ocy179.
20. Hammana I., Lepanto L., Poder T., Bellemare C., Ly M.S. Speech recognition in the radiology department: a systematic review. Health Inf Manag. 2015 ; 44(2): 4–10.
21. Pezzullo J.A., Tung G.A., Rogg J.M., Davis L.M., Brody J.M., Mayo-Smith W.W. Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging. 2008 ; 21(4):384–9.
22. Bhan S.N., Coblentz C.L., Norman G.R., Ali S.H. Effect of voice recognition on radiologist reporting time. Can Assoc Radiol J. 2008 ; 59(4):203–9.
2. White K.S. Speech recognition implementation in radiology. Pediatr Radiol. 2005 ; 35: 841–846.
3. Kumah -Crystal Y.A., Pirtle C.J., Whyte H.M., Goode E.S., Anders S.H., Lehmann C.U. Electronic Health Record Interactions through Voice: A Review. Appl Clin Inform. 2018 ; 9(3): 541–552. doi : 10.1055/s-0038-1666844.
4. Tsou A.Y., Lehmann C.U., Michel J., Solomon R., Possanza L., Gandhi T. Safe Practices for Copy and Paste in the EHR. Systematic Review, Recommendations, and Novel Model for Health IT Collaboration. Appl Clin Inform. 2017 ; 8(1): 12–34. doi : 10.4338/ACI-2016–09-R-0150.
5. Thielke S., Hammond K., Helbig S. Copying and pasting of examinations within the electronic medical record. Int J Med Inform. 2007 ; 76 ( 1 ) : 122–8.
6. Weis J.M., Levy P.C. Copy, paste and cloned notes in electronic health records: prevalence, benefits, risks, and best practice recommendations. Chest. 2014; 145(3): 632–8. doi : 10.1378/chest.13–0886.
7. Grisaffe D.B. Questions about the ultimate question: conceptual considerations in evaluating Reichheld’s net promoter score (NPS). Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 2007 ; 20 : 36.
8. Keiningham T.L. et al. A longitudinal examination of net promoter and firm revenue growth. Journal of Marketing. 2007 ; 71 ( 3 ): 39–51.
9. Shanafelt T.D., Balch C.M., Bechamps G., Russell T., Dyrbye L., Satele D. et al. Burnout and medical errors among American surgeons. Ann Surg. 2010 ; 251(6): 995–1000. doi : 10.1097/SLA.0b013e3181bfdab3.
10. Dyrbye L.N., Shanafelt T.D., Sinsky C.A. et al. Burnout among health care professionals: a call to explore and address this underrecognized threat to safe, high-quality care. NAM Perspectives: discussion paper, National Academy of Medicine, Washington, DC. Available at: https://nam.edu/burnout-among-health-care-professionals. Last a ccessed on: 5 July , 2017.
11. Vogel M., Kaisers W., Wassmuth R., Mayatepek E. Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial. Med Internet Res. 2015 ; 17(11): e247.
12. Ajami S. Use of speech-to-text technology for documentation by healthcare providers. Natl Med J India. 2016 ; 29(3): 148–152.
13. Wilder J.L., Nadar D., Gujral N., Ortiz B., Stevens R., Holder-Niles F. et al. Pediatrician Attitudes toward Digital Voice Assistant Technology Use in Clinical Practice. Appl Clin Inform. 2019 ; 10(2): 286–294.
14. Parente R., Kock N., Sonsini J. An analysis of the implementation and impact of speech-recognition technology in the healthcare sector. Perspect Health Inf Manag. 2004 ; 1:5.
15. Saxena K., Diamond R., Conant R.F., Mitchell T.H., Gallopyn I.G., Yakimow K.E. Provider Adoption of Speech Recognition and its Impact on Satisfaction, Documentation Quality, Efficiency, and Cost in an Inpatient EHR. AMIA Jt Summits Transl Sci Proc. 2018: 186–195.
16. Kauppinen T., Koivikko M.P., Ahovuo J. Improvement of report workflow and productivity using speech recognition – a follow-up study. J Digit Imaging. 2008 ; 21(4): 378–82. doi : 10.1007/s10278 008 9121 4.
17. Rosenthal D.I., Chew F.S., Dupuy D.E., Kattapuram S.V., Palmer W.E., Yap R.M. et al. Computer-based speech recognition as a replacement for medical transcription. AJR Am J Roentgenol. 1998 ; 170(1): 23–5.
18. Hart J.L., McBride A., Blunt D., Gishen P., Strickland N. Immediate and sustained benefits of a «total» implementation of speech recognition reporting. Br J Radiol. 2010 ; 83(989): 424–7. doi : 10.1259/ bjr /58137761.
19. Blackley S.V., Huynh J., Wang L., Korach Z., Zhou L. Speech recognition for clinical documentation from 1990 to 2018: a systematic review. J Am Med Inform Assoc. 2019 ; 26(4): 324–338. doi : 10.1093/ jamia /ocy179.
20. Hammana I., Lepanto L., Poder T., Bellemare C., Ly M.S. Speech recognition in the radiology department: a systematic review. Health Inf Manag. 2015 ; 44(2): 4–10.
21. Pezzullo J.A., Tung G.A., Rogg J.M., Davis L.M., Brody J.M., Mayo-Smith W.W. Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging. 2008 ; 21(4):384–9.
22. Bhan S.N., Coblentz C.L., Norman G.R., Ali S.H. Effect of voice recognition on radiologist reporting time. Can Assoc Radiol J. 2008 ; 59(4):203–9.
For citation
Kudryavtsev N. D., Sergunova K. A., Ivanova G. V., Semenov D. S., Khoruzhaya A.N., Ledikhova N. V., Vladzymyrskyy A. V., Morozov S. P. Evaluation of the effectiveness of the implementation of speech recognition technology for the preparation of radiological protocols. Medical doctor and information technology. 2020; S1: 58-64. (In Russ.). doi : 10.37690/1811-0193-2020-S1-58-64.
Documents
Keywords