From Rehab to Recap: Generating Discharge Reports for Rehabilitation Clinics with LLMs
Healthcare professionals spend a large part of their working time on documentation. Discharge reports from rehabilitation clinics require an extensive amount of time to write, review and finalize by a team of clinicians and administrative staff. Natural Language Generating (NLG) can be used to speed up this process, while maintaining a high content quality. Due to a pre-specified structure of discharge reports, high variability of content and heterogeneous information types, a custom approach is necessary. Our main contribution is a hybrid framework that combines a locally deployed Large Language Model (LLM) and a context-specific prompting approach with conventional control elements inside a web interface accessed by practitioners and administrative staff, tied together in an assistant application. We compare multiple LLM architectures and prompting aspects regarding linguistic styles in detail. Based on a user study, we showcase how our approach achieves a high satisfaction by healthcare professionals.
Authors
- Dario Antweiler (ORCID: https://orcid.org/0000-0002-2238-9753)
- Barry E. Stein (ORCID: https://orcid.org/0000-0001-8089-1779)
- Sabine Kugler (ORCID: https://orcid.org/0009-0003-3352-2809)
Institutions
- Fraunhofer Institute for Intelligent Analysis and Information Systems (DE)
Publication Details
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1109/fllm63129.2024.10852496
- Primary Topic
- Radiology practices and education
- Type
- article
- Field-Weighted Citation Impact
- 0.00