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Related Experiment Video

Updated: May 31, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

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Creating ISO/EN 13606 archetypes based on clinical information needs.

Christoph Rinner¹, Michael Kohler, Gudrun Hübner-Bloder

¹Medical University Vienna, Austria. chrostoph.rinner@meduniwien.ac.at

Studies in Health Technology and Informatics

|June 21, 2011

Summary

This summary is machine-generated.

This study introduces a method for creating archetypes, which are models for electronic health record (EHR) content. The approach automates the generation of electronic case report forms from these archetypes.

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Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Health Informatics
Clinical Informatics
Healthcare Standards

Background:

Archetypes are fundamental to modeling electronic health record (EHR) content within the ISO/EN 13606 EHR architecture.
The dual-model approach relies on archetypes for structuring and representing clinical information.
Standardization in EHRs is crucial for interoperability and data consistency.

Purpose of the Study:

To present an iterative development process for creating EHR archetypes.
To demonstrate the automated generation of electronic case report forms (eCRFs) directly from archetypes.
To evaluate the proposed archetype development approach within a specific clinical domain.

Main Methods:

An iterative development methodology was employed for archetype creation.
Archetypes were systematically developed to represent clinical information items.
A process for automated generation of eCRFs from the developed archetypes was implemented.

Main Results:

128 archetypes were successfully developed.
The developed archetypes cover 446 distinct clinical information items.
The focus domain for this evaluation was diabetes care.

Conclusions:

The presented iterative approach is effective for developing EHR archetypes.
Automated eCRF generation from archetypes streamlines clinical data collection processes.
The methodology is validated by its application to the complex diabetes domain.