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

Jeremy Kramer, Tetiana Kravchenko, Beatrice Kaufmann, Friederike J. S. Thilo, Mascha Kurpicz-Briki
9/27/2024
cs.CL

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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.

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Cite this paper

@article{kramer2024local,
  title  = {Local Transcription Models in Home Care Nursing in Switzerland: an Interdisciplinary Case Study},
  author = {Jeremy Kramer and Tetiana Kravchenko and Beatrice Kaufmann and Friederike J. S. Thilo and Mascha Kurpicz-Briki},
  year   = {2024},
  eprint = {2409.18819},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.18819v1}
}

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