Comparison of Voice-Automated Transcription and Human Transcription in Generating Pathology Reports

2003 ◽  
Vol 127 (6) ◽  
pp. 721-725
Author(s):  
Maamoun M. Al-Aynati ◽  
Katherine A. Chorneyko

Abstract Context.—Software that can convert spoken words into written text has been available since the early 1980s. Early continuous speech systems were developed in 1994, with the latest commercially available editions having a claimed accuracy of up to 98% of speech recognition at natural speech rates. Objectives.—To evaluate the efficacy of one commercially available voice-recognition software system with pathology vocabulary in generating pathology reports and to compare this with human transcription. To draw cost analysis conclusions regarding human versus computer-based transcription. Design.—Two hundred six routine pathology reports from the surgical pathology material handled at St Joseph's Healthcare, Hamilton, Ontario, were generated simultaneously using computer-based transcription and human transcription. The following hardware and software were used: a desktop 450-MHz Intel Pentium III processor with 192 MB of RAM, a speech-quality sound card (Sound Blaster), noise-canceling headset microphone, and IBM ViaVoice Pro version 8 with pathology vocabulary support (Voice Automated, Huntington Beach, Calif). The cost of the hardware and software used was approximately Can $2250. Results.—A total of 23 458 words were transcribed using both methods with a mean of 114 words per report. The mean accuracy rate was 93.6% (range, 87.4%–96%) using the computer software, compared to a mean accuracy of 99.6% (range, 99.4%–99.8%) for human transcription (P < .001). Time needed to edit documents by the primary evaluator (M.A.) using the computer was on average twice that needed for editing the documents produced by human transcriptionists (range, 1.4–3.5 times). The extra time needed to edit documents was 67 minutes per week (13 minutes per day). Conclusions.—Computer-based continuous speech-recognition systems in pathology can be successfully used in pathology practice even during the handling of gross pathology specimens. The relatively low accuracy rate of this voice-recognition software with resultant increased editing burden on pathologists may not encourage its application on a wide scale in pathology departments with sufficient human transcription services, despite significant potential financial savings. However, computer-based transcription represents an attractive and relatively inexpensive alternative to human transcription in departments where there is a shortage of transcription services, and will no doubt become more commonly used in pathology departments in the future.

1993 ◽  
Vol 32 (01) ◽  
pp. 33-46 ◽  
Author(s):  
C. E. Wulfman ◽  
M. Rua ◽  
C. D. Lane ◽  
E. H. Shortliffe ◽  
L. M. Fagan

Abstract:This paper describes three prototypes of computer-based clinical record-keeping tools that use a combination of window-based graphics and continuous speech in their user interfaces. Although many of today’s commercial speech-recognition products achieve high rates of accuracy for large grammars (vocabularies of words or collections of sentences and phrases), they can only “listen for” (and therefore recognize) a limited number of words or phrases at a time. When a speech application requires a grammar whose size exceeds a speech-recognition product’s limits, the application designer must partition the large grammar into several smaller ones and develop control mechanisms that permit users to select the grammar that contains the words or phrases they wish to utter. Furthermore, the user interfaces they design must provide feedback mechanisms that show users the scope of the selected grammars. The three prototypes described were designed to explore the use of window-based graphics as control and feedback mechanisms for continuous-speech recognition in medical applications. Our experiments indicate that window-based graphics can be effectively used to provide control and feedback for certain classes of speech applications, but they suggest that the techniques we describe will not suffice for applications whose grammars are very complex.


2000 ◽  
Vol 13 (S1) ◽  
pp. 211-212 ◽  
Author(s):  
Kalpana M. Kanal ◽  
Nicholas J. Hangiandreou ◽  
Anne-Marie G. Sykes ◽  
Heidi E. Eklund ◽  
Philip A. Araoz ◽  
...  

Author(s):  
C.-H. Lee ◽  
E. Giachin ◽  
L. R. Rabiner ◽  
R. Pieraccini ◽  
A. E. Rosenberg

Sign in / Sign up

Export Citation Format

Share Document