CES 2017 show in Las Vegas where new devices with voice were either launched or announced. Seven in 100 words in unedited clinical documents created with SR technology involve errors and 1 in 250 words contains clinically significant errors. The comparatively low error rate in signed notes highlights the crucial role of manual editing and review in the SR-assisted documentation process. Because of the time-intensive nature of the annotation task, we calculated interannotator agreement using only a small subset (33 of 651 [5.0%]), rather than requiring both individuals to annotate the full set of notes. This subset also included primarily notes that had been edited by MTs (26 of 33 [78.7%]), owing to the fact that errors in these notes are often more difficult to identify and may generate more disagreement. In general, health information technology and the EHR have introduced a number of potential sources for error.
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At all stages, deletion was the most prevalent general type (34.7%), followed by insertion (27.0%). Medication was the most common clinical semantic type in the original SR transcriptions, while diagnosis was most common in the transcriptionist-edited and signed versions. The effect of human review on note accuracy becomes more pronounced when considering just those errors that are clinically significant, rather than treating all errors as equally meaningful.
Following transcriptionist revision, this number decreased to 129 (58.1%), and by the time notes were signed, only 92 (42.4%) contained errors. Analyses were conducted in R statistical software 19 with t tests used to identify significant differences in mean error rates at each stage by sex, specialty, and note type.
However, the proportion of errors involving clinical information increased from 15.8% to 26.9% after transcriptionist revision, although it decreased slightly to 25.9% in SNs. Similarly, the proportion of errors that were clinically significant increased from 5.7% in the original SR transcriptions to 8.9% after being edited by an MT, then decreased to 6.4% in SNs.
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Detailed results of our error Viber download analysis are shown in Table 2 and Table 3. Errors were prevalent in original SR transcriptions, with an overall mean error rate of 7.4% (4.8%). The rate of errors decreased substantially following revision by MTs, to 0.4%. Errors were further reduced in SNs, which had an overall error rate of 0.3%. The number of notes containing at least 1 error also decreased with each processing stage. Of the 217 original SR transcriptions, 209 (96.3%) had errors.
In addition, these findings indicate a need not only for clinical quality assurance and auditing programs, but also for clinician training and education to raise awareness of these errors and strategies to reduce them. Table 3 shows the number and proportion of each error type across the 3 processing stages for each note type and for all notes combined.
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Prior to human revision, 138 of 217 notes (63.6%) had at least 1 clinically significant error, with a mean of 2.2 (2.7) errors per note. After being edited by an MT, 32 notes (14.7%) had clinically significant errors, and only 17 SNs (7.8%) contained such errors.
Once the prototype was ready, teams from across the Logitech prepared the skill for launch. Amazon reports that building from prototype to production-level skill took less than two weeks, according to Logitech. No other details or numbers were provided in this case study. Hitting this sales number would make Echo the largest selling voice assistant in the US, according to Forrester. Amazon Alexa site, more than 13,000 smart home devices from over 2,500 brands can be controlled with Alexa. , which the company says aims to make the technology behind Alexa ubiquitous available to manufacturers of various smart and wearable device.