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Introduction of digital speech recognition in a specialised outpatient department: a case study

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2016
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Title
Introduction of digital speech recognition in a specialised outpatient department: a case study
Published in
BMC Medical Informatics and Decision Making, October 2016
DOI 10.1186/s12911-016-0374-4
Pubmed ID
Authors

Christoph Ahlgrim, Oliver Maenner, Manfred W. Baumstark

Abstract

Speech 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. Methods 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. As 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. In summary, this case study illustrates how speech recognition can be implemented successfully when the user experience is emphasised.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 15%
Researcher 3 11%
Student > Ph. D. Student 3 11%
Student > Bachelor 2 7%
Lecturer > Senior Lecturer 2 7%
Other 5 19%
Unknown 8 30%
Readers by discipline Count As %
Nursing and Health Professions 3 11%
Computer Science 2 7%
Psychology 2 7%
Engineering 2 7%
Medicine and Dentistry 2 7%
Other 4 15%
Unknown 12 44%