ReviewJournal of the American Medical Informatics Association : JAMIA2015
Automated methods for the summarization of electronic health records.
Review in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of information systems in emergency department decision-making-a literature review.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care.Translational psychiatry · 2023Trial
- Optimizing generative artificial intelligence for clinical summarization: a blinded comparison study of automated versus human care planning synopses.JAMIA open · 2026Article
- Automated Brief Hospital Course Summarization in Cardiac Surgery Using a Lightweight Large Language Model-Based Framework: Development and Evaluation Study on the Medical Information Mart for Intensive Care-IV.Journal of medical Internet research · 2026Article
- Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.JMIR human factors · 2026Article
- Patient-Centered Summarization Framework for AI Clinical Summarization: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study.Journal of medical Internet research · 2026Article
- Identification of Moonlighting Proteins from Published Literature Using Natural Language Processing and AI.The protein journal · 2026Article
- Large language model-generated clinical summaries in emergency departments: A blinded comparison study.PLOS digital health · 2026Article
- Development of a digital tool to support overview in complex patient cases: Which information elements support the clinical workflow?BMC health services research · 2026Article
- Article
- Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Natural Language Processing for Automated Extraction of Continuous Glucose Monitoring Data.Diabetes care · 2025Article
- Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Verifiable Summarization of Electronic Health Records Using Large Language Models to Support Chart Review.medRxiv : the preprint server for health sciences · 2025Article
- Continuous multimodal data supply chain and expandable clinical decision support for oncology.NPJ digital medicine · 2025Article
- Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study.JMIR AI · 2025Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectivesThis review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation. TARGET AUDIENCE: The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems. SCOPE: Automated summarization has been a long-studied subject in the fields of natural language processing and human-computer interaction, but the translation of summarization and visualization methods to the complexity of the clinical workflow is slow moving. We assess work in aggregating and visualizing patient information with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge. We identify and discuss open challenges critical to the implementation and use of robust EHR summarization systems.
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What OpenQuestion holds
Registered trials
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.