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.
Highlights
Clinical research is a scientific research activity that considers patients as the main research object and focuses on the diagnosis, treatment, and prognosis of diseases
OpenEHR templates used for electronic health record https (EHR) are obtained from the template repository and parsed by tools provided by the openEHR community [36]
Since templates used by the electronic medical record system are for routine delivery of health care, part of them is not required for clinical research
Summary
Clinical research is a scientific research activity that considers patients as the main research object and focuses on the diagnosis, treatment, and prognosis of diseases. Researchers collected eligible patients by asking clinicians or manually issuing recruitment ads This is a labor-intensive and time-consuming task and can be helpful in small clinical research. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria. Objective: The study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance. Methods: A 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. Conclusions: We developed a patient-screening tool using openEHR It helps solve concept mismatch and improves query performance to reduce the burden on researchers. We demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
