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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

TL;DR

This case study investigates the case of home care nursing documentation in Switzerland and assesses different transcription tools and models, and conducts several experiments with OpenAI Whisper, indicating that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.

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.

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

TL;DR

This case study investigates the case of home care nursing documentation in Switzerland and assesses different transcription tools and models, and conducts several experiments with OpenAI Whisper, indicating that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.

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.
Paper Structure (37 sections, 1 figure, 2 tables)

This paper contains 37 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Model Selection Process
  4. Experimental Setup
  5. Speaker Profiles
  6. Sample Sentences
  7. Results
  8. Standard German Without Particular Accent (Hd)
  9. Client Sentences:
  10. Community Nurse Sentences:
  11. Standard German with Russian Accent (Hda)
  12. Client Sentences:
  13. Community Nurse Sentences:
  14. Swiss German Dialect 1 (Basel Region) (Swiss_dil1)
  15. Client Sentences:
  16. ...and 22 more sections

Figures (1)

  • Figure 1: Simple web interface of the Streamlit web application used for recording the transcripts.