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Der Radiologe

, Volume 45, Issue 8, pp 735–742 | Cite as

Spracherkennung: Auswirkung auf Workflow und Befundverfügbarkeit

  • C. GlaserEmail author
  • C. Trumm
  • S. Nissen-Meyer
  • M. Francke
  • B. Küttner
  • M. Reiser
EDV-Systeme in der Radiologie

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.

Schlüsselwörter

Spracherkennung RIS-PACS-Integration Befundverfügbarkeit Workflow Erkennungsrate 

Speech recognition: impact on workflow and report availability

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.

Keywords

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

Notes

Interessenkonflikt:

Keine Angaben

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Copyright information

© Springer Medizin Verlag 2005

Authors and Affiliations

  • C. Glaser
    • 1
    • 2
    Email author
  • C. Trumm
    • 1
  • S. Nissen-Meyer
    • 1
  • M. Francke
    • 1
  • B. Küttner
    • 1
  • M. Reiser
    • 1
  1. 1.Institut für Klinische RadiologieKlinikum Großhadern der Ludwig-Maximilians-Universität München
  2. 2.Institut für Klinische RadiologieKlinikum Großhadern der Ludwig-Maximilians-UniversitätMünchen

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