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A Report on Archetype Modelling in a Nationwide Data Infrastructure Project.

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The HiGHmed project established a knowledge governance framework to improve semantic interoperability using openEHR archetypes. This framework supports collaborative modeling, leading to the development of numerous high-quality archetypes with engaged experts.

Keywords:
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Area of Science:

  • Health Informatics
  • Digital Health Infrastructure
  • Semantic Interoperability

Background:

  • The HiGHmed project aims to achieve semantic interoperability via openEHR archetypes.
  • A knowledge governance framework has been established to guide collaborative modeling processes.
  • Continuous monitoring is crucial for long-term success and high-quality archetype development.

Purpose of the Study:

  • To provide an update on archetype modeling progress within HiGHmed.
  • To report on the establishment and effectiveness of the governance framework.

Main Methods:

  • Qualitative and quantitative analyses were employed.
  • Progress was assessed in establishing modeling groups, roles, and user engagement.
  • Modeling workflows and archetype development were evaluated.

Main Results:

  • Currently, 25 modelers and 17 domain experts are involved.
  • 79 archetypes have been identified, with 69 being pre-existing and internationally published.
  • Review round completion rates are satisfactory but show room for improvement.

Conclusions:

  • The governance framework effectively manages activities and accelerates archetype modeling.
  • High engagement from data stewards and clinicians has facilitated the development of a substantial number of archetypes.