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

TL;DR

This study evaluates the effectiveness of open-source transcription models for Swiss home care nursing documentation, addressing challenges like dialects and privacy, and finds promising initial results for practical use.

Contribution

It provides an empirical assessment of transcription tools in a specific medical domain, highlighting their potential and limitations for future development.

Findings

OpenAI Whisper performs adequately out-of-the-box for Swiss German dialects.

Transcription models show promise for automating nursing documentation.

Challenges like dialects and privacy are manageable with current models.

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.