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  4. Local Transcription Models in Home Care Nursing in Switzerland: an Interdisciplinary Case Study
 

Local Transcription Models in Home Care Nursing in Switzerland: an Interdisciplinary Case Study

URI
https://arbor.bfh.ch/handle/arbor/44536
Version
Submitted
Date Issued
2024
Author(s)
Kramer, Jeremy
Kravchenko, Tetiana
Kaufmann, Beatrice  
Thilo, Friederike J.S.  
Kurpicz-Briki, Mascha  
Type
Working Paper
Language
English
Subjects

cs.CL

nursing

nursing informatics

local transcription

digitalization

digital transformatio...

Abstract
Latest advances in the field of natural language processing (NLP) enable new use cases for different domains, including the medical sector. In particular, transcription can be used to support automation in the nursing documentation process and give nurses more time to interact with the patients. However, different challenges including (a) data privacy, (b) local languages and dialects, and (c) domain-specific vocabulary need to be addressed. In this case study, we investigate the case of home care nursing documentation in Switzerland. We assessed different transcription tools and models, and conducted several experiments with OpenAI Whisper, involving different variations of German (i.e., dialects, foreign accent) and manually curated example texts by a domain expert of home care nursing. Our results indicate that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.
DOI
https://doi.org/10.24451/dspace/11360
Publisher DOI
10.48550/arXiv.2409.18819
Publisher URL
https://arxiv.org/abs/2409.18819
Organization
Gesundheit  
Pflege  
G / Innovationsfeld Technologie und Gesundheit  
Technik und Informatik  
IDAS / Applied Machine Intelligence  
Institute for Data Applications and Security (IDAS)  
Project(s)
2022-142.039.629
Submitter
Schmid, Tabea
Citation apa
Kramer, J., Kravchenko, T., Kaufmann, B., Thilo, F. J. S., & Kurpicz-Briki, M. (2024). Local Transcription Models in Home Care Nursing in Switzerland: an Interdisciplinary Case Study. https://doi.org/10.24451/dspace/11360
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