ArticleJMIR medical informatics2021
A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utilization of EHRs for clinical trials: a systematic review.BMC medical research methodology · 2024Pooled it
- User Requirements for an Electronic Patient Recruitment System: Semistructured Interview Analysis After First Implementation in 3 German University Hospitals.JMIR human factors · 2024Article
- Standardizing Corneal Transplantation Records Using openEHR: Case Study.JMIR medical informatics · 2024Article
- Clinical Information Retrieval: A Literature Review.Journal of healthcare informatics research · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria.
objectiveThe study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance.
methodsA patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research.
resultsIn total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67%).
conclusionsWe developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers.
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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.