![]() Rich transcriptions including speaker segmentation, speaker role labeling, Transducer (RNN-T) model tailored for long-form audio, which can produce We describe a transformer-based Recurrent Neural Network Relevant information from medical conversations which will be availableĪs Google APIs. In this paper, we describe novel components for extracting clinically 3– 5 Although it takes somewhat longer to dictate and edit outpatient notes on the computer, compared with dictating and editing them for human transcription, 1, 4, 5 with currently available hardware and software computerized dictation can dramatically decrease delays associated with transcription and may substantially decrease the cost of producing transcribed documents.Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction Hagen Soltau, Mingqiu Wang, Izhak Shafran, Laurent El Shafey Computerized speech recognition technology is no longer just a promising technology but rather a clinically useful and economically viable tool. Compared with human transcription, the computerized dictation system was associated with a dramatic decrease (of more than five days) in the total time to complete notes. The total time the author spent dictating and editing notes was approximately 54 sec, or 15 percent, more with the computerized dictation system than with human transcription. These data indicate that computerized speech recognition may be an acceptable alternative to human medical transcription for producing outpatient notes. In contrast, all 549 notes produced by the computer where completed on the day of the visit. It took an average of 5.26 ± 3.48 days to complete notes produced by human transcription, and 139 (24 percent) of these notes were completed within 48 hours of the visit. Notes produced by human transcription were slightly shorter than notes produced by computer (50.41 ± 16.73 lines vs. Therefore, the total time spent dictating and editing notes created by computer was significantly greater than for human transcription (379.81 ± 132.69 sec vs. ![]() Similarly, the average time spent editing a note created by computer was significantly greater than with human transcription (176.72 ± 78.41 sec vs. The average time the author spent dictating a note for a human transcriptionist was significantly less than for computer transcription (179.58 ± 61.42 sec vs. The distribution of visit types between the two groups was not significantly different ( P = 0.4998). Of these, 426 (38 percent) were initial consultations and 703 (63 percent) were follow-up visits. There were 1,129 outpatient visits during the study period. Differences were considered significant if the P value was less than 0.05. Distribution of visit types was compared using the Fisher exact test. Comparisons between groups were performed with unpaired t-tests. Using a stopwatch and log book, the author recorded the date of visit, type of visit (initial consultation or follow-up), date the dictation was performed, time spent dictating, time spent editing, date the note was completed, and length of the final note in 65-character lines. ![]() All finished notes were formatted in an identical manner. For both computer-based and human transcription, all text was dictated in its entirety the author did not use any preformatted text or macros. For human transcription, all dictations were recorded on a hand-held recorder and transcribed by the same secretary. Prior to using the computerized dictation system, the author completed a 50-minute training period to familiarize the computer with his speech patterns. Computer transcription was performed on a Dell laptop computer with a 500 MHz Pentium processor, 256 MB RAM, ESS Maestro sound card, and Andrea NC-61 microphone, using IBM ViaVoice Millennium software. For the first three months, dictations were performed by a human transcriptionist, and for the remaining three months, with voice-recognition software. During a six-month period, patients seen by the author in the Pediatric Gastroenterology Clinic at the University of Virginia were assigned to human or computer-based transcription.
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