ArticleLearning health systems2019
Use of EHRs data for clinical research: Historical progress and current applications.
Article in Learning health systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses 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
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review.Journal of medical Internet research · 2024Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
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- Digital Transformation in Health Care: Are We on the Right Track?Journal of medical Internet research · 2026Article
- Exploring patient motivations and preferences for medical data sharing with researchers: a simulation study using the iAgree platform.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Enabling interoperability across disparate health data sources using Large Language Models.PLOS digital health · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- A computational phenotype for pediatric asthma exacerbations requiring hospitalization using electronic health record data.JAMIA open · 2026Article
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Characterization and validation of electronic medical record data for pharmacoepidemiologic research.Frontiers in pharmacology · 2026Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Generative AI models: the next anaesthetic agent?British journal of anaesthesia · 2025Article
- The Potential to Leverage Real-World Data for Pediatric Clinical Trials: A Proof-of-Concept Study.Journal of medical Internet research · 2025Article
- ACHealthChain blockchain framework for access control and privacy preservation in healthcare.Scientific reports · 2025Article
- Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based Application in Multisite Cancer Clinical Trials.Journal of the Society for Clinical Data Management · 2025Article
- Article
- Integrated electronic health record tools to access real-world data in oncology research.JAMIA open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The benefits of reusing EHR data for clinical research studies are numerous. They portend the opportunity to bring new therapies to patients sooner, potentially at a lower cost, and to accelerate learning health cycles-through faster data acquisition in clinical research studies. Metrics have proven that time can be saved, workflow and processes streamlined, and data quality increased significantly. Pilot projects and now actual investigational trials used for regulatory submissions have shown that these benefits support the transformation of clinical research by leveraging EHRs for research. Panelists at a recent collaborative focused on bridging clinical research and clinical care offered varying perspectives on how the latest standards and technologies could be leveraged to facilitate data transfer from EHR systems into clinical research databases, as well as the associated improvements in data quality. Panelists also discussed other avenues to leverage EHR in clinical research. Improvements and exciting possibilities notwithstanding, much work remains. Data ownership and access, attention to metadata and structured data for data sharing, and broader adoption of global standards are key areas for collaboration. With the steady increase in adoption of EHRs around the world, this is an excellent time for all stakeholders to work together and create an environment such that EHRs can be used more readily for research. The capacity for research can thus be increased to provide more high-quality information that will contribute to rapid continuous learning health systems from which all patients can benefit.
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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.