Skip to main content

Spracherkennung: Auswirkung auf Workflow und Befundverfügbarkeit

Speech recognition: impact on workflow and report availability

Der Radiologe volume 45, pages 735–742 (2005)Cite this article

Zusammenfassung

Mit der voranschreitenden technischen Entwicklung werden Spracherkennungssysteme (SES) — gerade vor dem Hintergrund der aktuell unabweisbaren Kostenreduktion bei gleichbleibender Qualität in der Patientenversorgung — eine zunehmend attraktive Alternative zur traditionellen Befunderstellung.

Die 2 Hauptkomponenten eines SES sind das akustische und das Sprachmodell. Merkmale kontinuierlicher SES mit Realtimeerkennung umfassen vorformulierbare Befund(blöck)e, Standardbefundvorlagen und Sprachkommandos (Navigation im Text, Steuerung von SES und RIS). Sinnvoll für eine optimale Nutzung des SES-Potenzials ist die Integration von SES, RIS und PACS. Wichtige Leistungsparameter eines SES sind Befundverfügbarkeit und Zeiteffizienz des Befundungsprozesses (Erkennungsrate, Editier- und Korrekturaufwand, Wortschatzpflege) für den Radiologen.

In der Praxis wird die Erkennungsrate über die Fehlerrate (Einheit „Wort“) abgeschätzt. Fehlerraten liegen zwischen 4 und 28%. Etwa 20% davon sind Wortschatzfehler, die u. U. zu einer falschen Befundinterpretation führen können. Sie unterstreichen die Notwendigkeit einer sorgfältigen Textkorrektur und Wortschatzpflege.

Die Einführung eines SES erbringt eine drastische Verbesserung der Befundverfügbarkeit. Dagegen nimmt der individuelle ärztliche Zeitbedarf bei digitaler Befunderstellung um ca. 20–25% (Projektionsradiographie, CR) bzw. ca. 30% (CT, MRT) zu. Die Entlastung des Schreibbüros (Hintergrunddiktat) hängt von dessen Qualifikation ab. Das Onlinediktat führt zu einer Umverteilung von Arbeitsschritten vom Schreibbüro auf den Befunder.

Abstract

With ongoing technical refinements speech recognition systems (SRS) are becoming an increasingly attractive alternative to traditional methods of preparing and transcribing medical reports.

The two main components of any SRS are the acoustic model and the language model. Features of modern SRS with continuous speech recognition are macros with individually definable texts and report templates as well as the option to navigate in a text or to control SRS or RIS functions by speech recognition. The best benefit from SRS can be obtained if it is integrated into a RIS/RIS-PACS installation. Report availability and time efficiency of the reporting process (related to recognition rate, time expenditure for editing and correcting a report) are the principal determinants of the clinical performance of any SRS.

For practical purposes the recognition rate is estimated by the error rate (unit “word”). Error rates range from 4 to 28%. Roughly 20% of them are errors in the vocabulary which may result in clinically relevant misinterpretation. It is thus mandatory to thoroughly correct any transcribed text as well as to continuously train and adapt the SRS vocabulary.

The implementation of SRS dramatically improves report availability. This is most pronounced for CT and CR. However, the individual time expenditure for (SRS-based) reporting increased by 20–25% (CR) and according to literature data there is an increase by 30% for CT and MRI. The extent to which the transcription staff profits from SRS depends largely on its qualification. Online dictation implies a workload shift from the transcription staff to the reporting radiologist.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Abb. 1
Abb. 2
Abb. 3

Literatur

  1. Antiles S, Couris J, Schweitzer A et al. (2000) Project planning, training, measurement and sustainment: the successful implementation of voice recognition. Radiol Manage 22:18–31; quiz 32–16

    Google Scholar 

  2. Arndt H, Petersein J, Stockheim D et al. (1999) [The use of automated speech recognition in diagnostic radiology]. Rofo Fortschr Geb Rontgenstr Neuen Bildgeb Verfahr 171:400–404

    Article  PubMed  Google Scholar 

  3. Callaway EC, Sweet CF, Siegel E et al. (2002) Speech recognition interface to a hospital information system using a self-designed visual basic program: initial experience. J Digit Imaging 15:43–53

    PubMed  Google Scholar 

  4. Devine EG, Gaehde SA, Curtis AC (2000) Comparative evaluation of three continuous speech recognition software packages in the generation of medical reports. J Am Med Inform Assoc 7:462–468

    PubMed  Google Scholar 

  5. Ernst R, Carpenter W, Torres W et al. (2001) Combining speech recognition software with digital imaging and communications in medicine (DICOM) workstation software on a Microsoft Windows platform. J Digit Imaging 14:182–183

    Google Scholar 

  6. Gale B, Safriel Y, Lukban A et al. (2001) Radiology report production times: voice recognition vs. transcription. Radiol Manage 23:18–22

    Google Scholar 

  7. Hayt DB, Alexander S (2001) The pros and cons of implementing PACS and speech recognition systems. J Digit Imaging 14:149–157

    Article  Google Scholar 

  8. 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

    PubMed  Google Scholar 

  9. Holman BL, Aliabadi P, Silverman SG et al. (1994) Medical impact of unedited preliminary radiology reports. Radiology 191:519–521

    PubMed  Google Scholar 

  10. Hundt W, Stark O, Scharnberg B et al. (1999) Speech processing in radiology. Eur Radiol 9:1451–1456

    Article  PubMed  Google Scholar 

  11. 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

    Article  PubMed  Google Scholar 

  12. Lai J, Vergo J (1997) Med speak: report creation with continuous speech recognition. American Association for Computing Machinery Special Interest Group — Computers 97 conference proceedings, March 1997, pp 431–438

    Google Scholar 

  13. Langer SG (2002) Impact of tightly coupled PACS/speech recognition on report turnaround time in the radiology department. J Digit Imaging 15 [suppl 1]:234–236

    Google Scholar 

  14. Langer S (2002) Radiology speech recognition: workflow, integration, and productivity issues. Curr Probl Diagn Radiol 31:95–104

    Article  PubMed  Google Scholar 

  15. Leeming BW, Porter D, Jackson JD et al. (1981) Computerized radiologic reporting with voice data-entry. Radiology 138:585–588

    PubMed  Google Scholar 

  16. Makhoul J, Schwartz R (1995) State of the art in continuous speech recognition. Proc Natl Acad Sci U S A 92:9956–9963

    PubMed  Google Scholar 

  17. Mehta A (2002) Voice recognition. In: Dreyer K, Mehta A, Thrall J (eds) PACS: a guide to the digital revolution. Springer, Berlin Heidelberg New York, pp 281–302

  18. Mehta A (2003) The Internet for radiology practice. Springer, Berlin Heidelberg New York

  19. Mehta A, McLoud TC (2003) Voice recognition. J Thorac Imaging 18:178–182

    Article  PubMed  Google Scholar 

  20. Ramaswamy MR, Chaljub G, Esch O et al. (2000) Continuous speech recognition in MR imaging reporting: advantages, disadvantages, and impact. AJR Am J Roentgenol 174:617–622

    PubMed  Google Scholar 

  21. Reed RA (1992) Voice recognition for the radiology market. Top Health Rec Manage 12:58–63

    Google Scholar 

  22. Robbins AH, Horowitz DM, Srinivasan MK et al. (1987) Speech-controlled generation of radiology reports. Radiology 164:569–573

    PubMed  Google Scholar 

  23. Robbins AH, Vincent ME, Shaffer K et al. (1988) Radiology reports: assessment of a 5000-word speech recognizer. Radiology 167:853–855

    PubMed  Google Scholar 

  24. Rosenthal DI, Chew FS, Dupuy DE et al. (1998) Computer-based speech recognition as a replacement for medical transcription. AJR Am J Roentgenol 170:23–25

    PubMed  Google Scholar 

  25. Seltzer SE, Kelly P, Adams DF et al. (1997) Expediting the turnaround of radiology reports in a teaching hospital setting. AJR Am J Roentgenol 168:889–893

    PubMed  Google Scholar 

  26. Sferrella SM (2003) Success with voice recognition. Radiol Manage 25:42–49

    Google Scholar 

  27. Vorbeck F, Ba-Ssalamah A, Kettenbach J et al. (2000) Report generation using digital speech recognition in radiology. Eur Radiol 10:1976–1982

    Article  PubMed  Google Scholar 

  28. Zafar A, Overhage JM, McDonald CJ (1999) Continuous speech recognition for clinicians. J Am Med Inform Assoc 6:195–204

    PubMed  Google Scholar 

  29. Zafar A, Mamlin B, Perkins S et al. (2004) A simple error classification system for understanding sources of error in automatic speech recognition and human transcription. Int J Med Inform 73:719–730

    Article  PubMed  Google Scholar 

Download references

Interessenkonflikt:

Keine Angaben

Author information

Affiliations

  1. Institut für Klinische Radiologie, Klinikum Großhadern der Ludwig-Maximilians-Universität München

    C. Glaser, C. Trumm, S. Nissen-Meyer, M. Francke, B. Küttner & M. Reiser

  2. Institut für Klinische Radiologie, Klinikum Großhadern der Ludwig-Maximilians-Universität, Marchioninistraße 15, 81377 , München

    C. Glaser

Authors
  1. C. Glaser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Trumm
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Nissen-Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Francke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. B. Küttner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Reiser
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Glaser.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Glaser, C., Trumm, C., Nissen-Meyer, S. et al. Spracherkennung: Auswirkung auf Workflow und Befundverfügbarkeit. Radiologe 45, 735–742 (2005). https://doi.org/10.1007/s00117-005-1253-7

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00117-005-1253-7

Schlüsselwörter

  • Spracherkennung
  • RIS-PACS-Integration
  • Befundverfügbarkeit
  • Workflow
  • Erkennungsrate

Keywords

  • Speech recognition
  • RIS-PACS integration
  • Report turnaround time
  • Workflow
  • Recognition rate