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.

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Published
2024-11-26
DOI
https://doi.org/10.1109/fllm63129.2024.10852496
Primary Topic
Radiology practices and education
Type
article
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article

From Rehab to Recap: Generating Discharge Reports for Rehabilitation Clinics with LLMs

Dario Antweiler, Barry E. Stein, Sabine Kugler
Radiology practices and education
article

From Rehab to Recap: Generating Discharge Reports for Rehabilitation Clinics with LLMs

Dario Antweiler, Barry E. Stein, Sabine Kugler
article en

Abstract

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.

Fraunhofer Institute for Intelligent Analysis and Information Systems (DE)
Gender equality
Openalex Percentile: Top 57%
Radiology practices and education
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From Rehab to Recap: Generating Discharge Reports for Rehabilitation Clinics with LLMs — Dario Antweiler, Barry E. Stein, et al. · (2024) | TGRS Research Map | TGRS