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Related Concept Videos

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Related Experiment Video

Updated: May 24, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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Published on: May 15, 2020

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
PubMed
Summary
This summary is machine-generated.

Cohort identification in hospitals is difficult. CohortMailer automates patient cohort extraction from openEHR using Archetype Query Language (AQL), improving clinical workflow efficiency.

Keywords:
AQLautomated recruitmentclinical cohort identificationinfection surveillanceopenEHRpatient screening

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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.