A patient-screening tool for clinical research based on electronic health records using openEHR: Development study

Abstract

Background: The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data forclinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria arenot 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-specificlanguage 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 solveconcept mismatch and improve query performance.Methods: A patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information modelsand EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templateswere chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derivedconcepts 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, werealized a patient-screening tool for clinical research.Results: In total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the ChineseClinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospitalin 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 andcoarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performanceamong 4 cases (66.67%).Conclusions: We developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improvesquery performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use ofEHR data using openEHR, which can be referenced by other researchers.