Authors
Paul Landes, Sitara Rao, Barbara Di Eugenio, Aaron Chaise
Published in
Proceedings. IEEE International Conference on Healthcare Informatics. Volume 2026. Pages 93-102. Epub Aug 12, 2026.
Abstract
Discharge summaries are lengthy medical documents that summarize a hospital in-patient visit. Automatically generating them can reduce documentation burden and return clinician time to patient care. Whereas Large Language Model (LLMs) could be used for this task, their Achilles heel is hallucinations, which can have drastic consequences for clinical documentation. We present an evidence-driven alignment framework for discharge summarization at the clinical encounter level, that treats provenance as a first-class constraint, using semantic graphs and deep learning models. Each summary sentence is selected and organized via cross-document semantic alignment and is accompanied by explicit evidence links to its source spans. We show our results on two corpora: a publicly available corpus (MIMIC-III) and clinical notes written by physicians at the University of Illinois Hospital (UIC Health). Additionally, we make source code and trained models available.
PMID:
42788028
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.
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