ArticleJournal of the American Medical Informatics Association : JAMIA2015
Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.
Article 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 60 papers, 4 of them syntheses that pooled it.
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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
60 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The usage of population and disease registries as pre-screening tools for clinical trials, a systematic review.Systematic reviews · 2024Pooled it
- A systematic review on natural language processing systems for eligibility prescreening in clinical research.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Provider Interaction With an Electronic Health Record Notification to Identify Eligible Patients for a Cluster Randomized Trial of Advance Care Planning in Primary Care: Secondary Analysis.Journal of medical Internet research · 2023Trial
- Automating eligibility assessment and enrollment for sugammadex administration within an integrated perioperative workflow.JAMIA open · 2026Article
- An Intelligent Trial Eligibility Screening Tool Using Natural Language Processing With a Block-Based Visual Programming Interface: Development and Usability Study.JMIR medical informatics · 2025Article
- Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study.JMIR medical informatics · 2025Article
- Large Language Model Symptom Identification From Clinical Text: Multicenter Study.Journal of medical Internet research · 2025Article
- Article
- Validating an Electronic Health Record Algorithm for Diabetes Screening Eligibility in the Emergency Department.The western journal of emergency medicine · 2025Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study.BMC cancer · 2024Article
- Article
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
- Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.JCO clinical cancer informatics · 2023Article
- Clinical Trial Technologies for Improving Equity and Inclusion in Cardiovascular Clinical Research.Cardiology and therapy · 2023Article
- Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.BMC medical research methodology · 2023Observational
- Gross motor function prediction using natural language processing in cerebral palsy.Developmental medicine and child neurology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectives(1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. DATA AND
methodsWe collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial-patient matches and a reference standard of historical trial-patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed.
resultsCompared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial-patient matches. DISCUSSION AND
conclusionBy exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.
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