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Automatic generation of computable implementation guides from clinical information models.

Diego Boscá1, José Alberto Maldonado2, David Moner1

  • 1Instituto Universitario de Aplicaciones de las Tecnologías de la Información y Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain.

Journal of Biomedical Informatics
|April 26, 2015
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Summary
This summary is machine-generated.

This study introduces a new method for creating clinical information models. It uses archetypes to automatically generate human-readable and computer-processable implementation guides, improving Electronic Health Record data validation.

Keywords:
ArchetypeClinical information modelData validationImplementation guideNatural Rule Language

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Area of Science:

  • Health Informatics
  • Computer Science

Background:

  • Clinical information models describe Electronic Health Record (EHR) data.
  • Implementation guides define these models but are human-readable only, requiring manual conversion for computer processing.
  • This manual conversion is complex and prone to errors.

Purpose of the Study:

  • To develop a methodology for generating implementation guides that are both human-readable and computer-processable.
  • To bridge the gap between human-oriented specifications and machine-executable rules for clinical information models.

Main Methods:

  • Utilized archetypes as the foundation for generating implementation guides.
  • Developed a process to create formal rules in Natural Rule Language (NRL) from archetypes.
  • Generated Schematron rules from NRL for data validation.
  • Implemented the methodology in the LinkEHR platform.

Main Results:

  • Successfully generated NRL rules and implementation guides from archetypes (EN ISO 13606, openEHR, HL7 CDA).
  • Demonstrated the automated generation of computer-processable validation rules (Schematron) from human-readable specifications.
  • The LinkEHR platform facilitates this novel generation process.

Conclusions:

  • The proposed archetype-based methodology enables the creation of implementation guides that are easily understood by humans and directly processable by computers.
  • This approach streamlines the development and validation of clinical information models for EHR systems.
  • Automating the generation of implementation guides reduces errors and enhances efficiency in health data management.