ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
The Data Gap in the EHR for Clinical Research Eligibility Screening.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2026Article
- Reusing data from HL7 CDA-based shared EHR systems for clinical trial conduct: a method for analyzing feasibility.BMC medical informatics and decision making · 2025Article
- Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Contrasting rule and machine learning based digital self triage systems in the USA.NPJ digital medicine · 2024Review
- Evaluation of Eligibility Criteria Relevance for the Purpose of IT-Supported Trial Recruitment: Descriptive Quantitative Analysis.JMIR formative research · 2024Article
- Clinical researchers' insights on key data for eligibility screening in clinical studies.Journal of clinical and translational science · 2024Article
- Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment.International journal of medical informatics · 2023Article
- Principal Investigators' Perceptions on Factors Associated with Successful Recruitment in Clinical Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening.ACR open rheumatology · 2021Article
- Article
- Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study.JMIR mental health · 2021Article
- A knowledge base of clinical trial eligibility criteria.Journal of biomedical informatics · 2021Article
- Cognitive Function Characterization Using Electronic Health Records Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records.Frontiers in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Much effort has been devoted to leverage EHR data for matching patients into clinical trials. However, EHRs may not contain all important data elements for clinical research eligibility screening. To better design research-friendly EHRs, an important step is to identify data elements frequently used for eligibility screening but not yet available in EHRs. This study fills this knowledge gap. Using the Alzheimer's disease domain as an example, we performed text mining on the eligibility criteria text in Clinicaltrials.gov to identify frequently used eligibility criteria concepts. We compared them to the EHR data elements of a cohort of Alzheimer's Disease patients to assess the data gap by usingthe OMOP Common Data Model to standardize the representations for both criteria concepts and EHR data elements. We identified the most common SNOMED CT concepts used in Alzheimer 's Disease trials, andfound 40% of common eligibility criteria concepts were not even defined in the concept space in the EHR dataset for a cohort of Alzheimer 'sDisease patients, indicating a significant data gap may impede EHR-based eligibility screening. The results of this study can be useful for designing targeted research data collection forms to help fill the data gap in the EHR.
Identifiers
29888090PMC5961795What 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.