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Timely, AQL-Driven Clinical Cohort Identification in openEHR Infrastructures
Johnson Bankole1,2, Michael Anywar3, Jan Heykendorf4
1Institute for Medical Informatics and Artificial Intelligence, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Cohort identification in hospitals is difficult. CohortMailer automates patient cohort extraction from openEHR using Archetype Query Language (AQL), improving clinical workflow efficiency.
Area of Science:
- Health Informatics
- Clinical Data Management
- Medical Record Systems
Background:
- Timely patient cohort identification is hindered by fragmented hospital systems, delayed medical coding, and insufficient IT resources.
- Manual screening of patient data for research or clinical purposes is time-consuming and resource-intensive.
- Efficient patient cohort identification is crucial for clinical research, quality improvement, and real-time healthcare delivery.
Purpose of the Study:
- To introduce CohortMailer, an automated tool for extracting patient cohorts from openEHR repositories.
- To evaluate the performance and feasibility of CohortMailer in a real-world hospital setting.
- To demonstrate the benefits of standards-based automation in reducing manual screening efforts for clinical workflows.
Main Methods:
- CohortMailer was developed to automate patient cohort extraction using Archetype Query Language (AQL) from openEHR data.
- The tool was deployed at two sites of the University Hospital Schleswig-Holstein (UKSH) for a 30-day evaluation period.
- The system executed structured queries, applied rule-based filters, and delivered case lists via scheduled email to study teams.
Main Results:
- CohortMailer demonstrated 83.3% availability during the 30-day evaluation, successfully identifying 274 unique patients.
- For the respiratory cohort, the tool achieved a positive predictive value (PPV) of 62.9% and a sensitivity of 50.3%.
- The system effectively reduced manual screening effort, supporting real-time clinical workflows.
Conclusions:
- Modular, standards-based automation, exemplified by CohortMailer, can significantly improve patient cohort identification processes.
- Automated tools leveraging openEHR and AQL can enhance the efficiency of clinical research and healthcare delivery.
- CohortMailer offers a viable solution to overcome challenges associated with manual patient data screening in hospital settings.
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