ArticleJournal of medical Internet research2025
Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.Mayo Clinic proceedings. Digital health · 2026Article
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Maternal Vulnerability Indicators Derived from Free-Text Clinical Documentation in Adolescent Pregnancy.Medicina (Kaunas, Lithuania) · 2026Article
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Article
- Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical Records.Research square · 2026Article
- Article
- Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation.Digestive diseases and sciences · 2026Article
- Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.JMIR research protocols · 2026Article
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
- Lightweight open-source large language models versus cTAKES for information extraction from discharge summaries: tobacco smoking status test case.JAMIA open · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Topic Modeling of Nursing Documentation in Hemodialysis Units: A Mixed-Methods Study of Nursing Surveillance Activities.Journal of nursing management · 2026Observational
- Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging reddit data for context-enhanced synthetic health data generation to identify low self esteem.Frontiers in psychiatry · 2025Article
- Privacy-, linguistic-, and information-preserving synthesis of clinical documentation through generative agents.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic health records (EHRs) consist of both structured data (eg, diagnostic codes) and unstructured data (eg, clinical notes). It is commonly believed that unstructured clinical narratives provide more comprehensive information. However, this assumption lacks large-scale validation and direct validation methods.
objectiveThis study aims to quantitatively compare the information in structured and unstructured EHR data and directly validate whether unstructured data offers more extensive information across a patient population.
methodsWe analyzed both structured and unstructured data from patient records and visits in a large Dutch primary care EHR database between January 2021 and January 2024. Clinical concepts were identified from free-text notes using an extraction framework tailored for Dutch and compared with concepts from structured data. Concept embeddings were generated to measure semantic similarity between structured and extracted concepts through cosine similarity. A similarity threshold was systematically determined via annotated matches and minimized weighted Gini impurity. We then quantified the concept overlap between structured and unstructured data across various concept domains and patient populations.
resultsIn a population of 1.8 million patients, only 13% of extracted concepts from patient records and 7% from individual visits had similar structured counterparts. Conversely, 42% of structured concepts in records and 25% in visits had similar matches in unstructured data. Condition concepts had the highest overlap, followed by measurements and drug concepts. Subpopulation visits, such as those with chronic conditions or psychological disorders, showed different proportions of data overlap, indicating varied reliance on structured versus unstructured data across clinical contexts.
conclusionsOur study demonstrates the feasibility of quantifying the information difference between structured and unstructured data, showing that the unstructured data provides important additional information in the studied database and populations. The annotated concept matches are made publicly available for the clinical natural language processing community. Despite some limitations, our proposed methodology proves versatile, and its application can lead to more robust and insightful observational clinical research.
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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.