SynthesisJournal of the American Medical Informatics Association : JAMIA2026
Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification.JMIR medical informatics · 2026Article
- Natural language processing to develop a standardized lexicon for precision medicine in oncology.Journal of managed care & specialty pharmacy · 2026Article
- Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.Mayo Clinic proceedings. Digital health · 2026Article
- Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer types.medRxiv : the preprint server for health sciences · 2026Article
- Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.NPJ precision oncology · 2026Article
- Can NLP Detect Loneliness in Electronic Health Records? A Proof-of-Concept Study.Studies in health technology and informatics · 2026Article
- Large language models for zero-shot procedure extraction in orthopedic surgery: a comparative evaluation.Scientific reports · 2026Article
- External Validation of Diagnosis Codes to Identify Pediatric Mental Health Emergency Department Visits for Aggression.Pediatric emergency care · 2026Article
- Evaluating Large Language Models for Transparent Quality-of-Care Measurement in Children with ADHD.medRxiv : the preprint server for health sciences · 2026Article
- Lightweight open-source large language models versus cTAKES for information extraction from discharge summaries: tobacco smoking status test case.JAMIA open · 2026Article
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- Retrieval-enhanced drafting of ClinicalTrials.gov data elements from clinical protocols.Journal of clinical and translational science · 2026Article
- Locally deployed large language model for real-world lecanemab eligibility pre-screening.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveClinical registries advance healthcare by tracking patient outcomes and intervention safety. Manually extracting information from clinical text for registries is labor- and resource-intensive and often inaccurate. Therefore, this systematic review aims to evaluate the use and effectiveness of natural language processing (NLP) methods in extracting information from clinical text for populating clinical registries. MATERIALS AND
methodsPubMed, Embase, Scopus, Web of Science, and ACM Digital Library were systematically searched. Studies were included if they used NLP techniques to populate clinical registries. The extracted data included details of the registry, the clinical text, the registry data elements extracted, the NLP methods used, and how their performance was evaluated.
resultsFifteen articles were included in the review. Since 2020, the use of NLP methods for extracting information to populate clinical registries has been increasing steadily. Initially, rule-based NLP methods dominated the field, but machine learning-based approaches have gradually gained popularity. However, only one of the included studies employed generative large language models (LLMs). The diversity of clinical text and extracted data elements posed challenges to the generalizability of the NLP methods.
conclusionTo date, the application of NLP methods to clinical text for populating clinical registries has been limited in both the number of published studies and the scope of implementation. The NLP methods used thus far face significant challenges in effectively managing the complexity and diversity of clinical text and data elements. Moreover, the performance of the NLP methods varied significantly. This review underscores the need for a robust and adaptable NLP framework. Generative LLMs may provide direction for future research, but their use must account for challenges such as accuracy, cost, privacy, and limited supporting evidence.
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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.