ArticleBMC medical informatics and decision making2016
Introduction of digital speech recognition in a specialised outpatient department: a case study.
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Speech recognition for clinical documentation from 1990 to 2018: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Speech Recognition for Medical Dictation: Overview in Quebec and Systematic Review.Journal of medical systems · 2018Pooled it
- Analysis of Digital Documentation Speed and Sequence Using Digital Paper and Pen Technology During the Refugee Crisis in Europe: Content Analysis.JMIR mHealth and uHealth · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSpeech recognition software might increase productivity in clinical documentation. However, low user satisfaction with speech recognition software has been observed. In this case study, an approach for implementing a speech recognition software package at a university-based outpatient department is presented.
methodsMethods to create a specific dictionary for the context "sports medicine" and a shared vocabulary learning function are demonstrated. The approach is evaluated for user satisfaction (using a questionnaire before and 10 weeks after software implementation) and its impact on the time until the final medical document was saved into the system.
resultsAs a result of implementing speech recognition software, the user satisfaction was not remarkably impaired. The median time until the final medical document was saved was reduced from 8 to 4 days.
conclusionIn summary, this case study illustrates how speech recognition can be implemented successfully when the user experience is emphasised.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.