Automated generation of discharge summaries: leveraging large language models with clinical data

Matthias Ganzinger, Nicola Kunz, Pascal Fuchs, Cornelia K Lyu, Martin Loos, Martin Dugas, Thomas Moritz Pausch

Scientific Reports 2025 article cited by 53

Source Open-access PDF More in Medicine & Health

importance 4/10 ai assisted Artificial Intelligence in Healthcare and Education clinical documentation stage: research open-source model

Problem

Writing discharge summaries is time-consuming for physicians; this study tested automating generation of German discharge summaries from structured clinical data.

Why it matters

Discharge summaries are essential for safe care continuity and consume significant physician time.

What the AI did

LLaMA3 was prompted to generate summaries from structured, de-identified data of 25 patients, with outputs optimized via prompt engineering and rated by physicians.

Results

Error rate 2.84 mistakes per summary; ROUGE-1 0.25; BERTScore 0.64; physician rating 3.7 (scale not stated in source).

Impact

Produces editable draft summaries but with errors requiring physician review; not a finished automation.

Record

Venue	Scientific Reports
Published	2025
Work type	article
Authors	Matthias Ganzinger, Nicola Kunz, Pascal Fuchs, Cornelia K Lyu, Martin Loos, Martin Dugas, Thomas Moritz Pausch
Citations	53 (OpenAlex, at import time)
References	29
Model or method	LLaMA3
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-025-01618-7
Open access	gold
Keywords	discharge summaries, LLaMA3, large language models, EHR, clinical documentation

Importance rating

4 / 10

real but narrower advance

Discharge summary automation saves physician time and supports care continuity, but abstract frames it as assistive drafts requiring review; narrow single-site scope, no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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