ArticleJournal of the American Medical Informatics Association : JAMIA2023
A method to automate the discharge summary hospital course for neurology patients.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: systematic review.Frontiers in public health · 2025Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Patient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment.Journal of medical Internet research · 2024Trial
- Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.JMIR human factors · 2026Article
- Article
- Assessing the quality of AI-generated and physician-written discharge summaries: evaluation of an EHR-integrated tool in a Dutch academic hospital.EBioMedicine · 2026Article
- Physician-Reported Safety Outcomes of AI-Generated Hospital Course Summaries.JAMA network open · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- Shifts in emergency physicians' attitudes toward large language model-based documentation: a pre- and post-implementation study.Scientific reports · 2025Article
- Large Language Model Assistant for Emergency Department Discharge Documentation.JAMA network open · 2025Article
- Evaluating Hospital Course Summarization by an Electronic Health Record-Based Large Language Model.JAMA network open · 2025Article
- Verifiable Summarization of Electronic Health Records Using Large Language Models to Support Chart Review.medRxiv : the preprint server for health sciences · 2025Article
- Evaluating large language models for drafting emergency department encounter summaries.PLOS digital health · 2025Article
- Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.Journal of medical Internet research · 2025Article
- Striking the right balance between accountability and quality improvement: a discharge summary timeliness tale.BMJ open quality · 2025Article
- Exploring the Potential of ChatGPT for the Summarization of Patient Medical Histories: A Pilot Study.Cureus · 2025Article
- A dataset and benchmark for hospital course summarization with adapted large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Developing and Evaluating Large Language Model-Generated Emergency Medicine Handoff Notes.JAMA network open · 2024Article
- Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.JAMA network open · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveGeneration of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models. MATERIALS AND
methodsWe fine-tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center.
resultsThe approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, 2 board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically. DISCUSSION AND
conclusionTo our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.