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Updated: Feb 4, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Team Competencies and Educational Threshold Concepts for Clinical Information Modelling.

Philip J Scott1, Kai U Heitmann2

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|October 12, 2018
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Effective healthcare interoperability relies on robust semantic models. A collaborative team approach, involving diverse experts, is crucial for successful clinical information modeling, moving beyond

Keywords:
clinical information systemseducationhealth information exchangeinteroperabilitymodelsteam dynamics

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Area of Science:

  • Health Informatics
  • Clinical Informatics
  • Information Science

Background:

  • Healthcare interoperability is essential for safe and reliable health information exchange.
  • Current clinical information modeling approaches may be suboptimal.
  • Semantic models are foundational for effective data exchange.

Purpose of the Study:

  • To advocate for a collaborative, multidisciplinary approach to clinical information modeling.
  • To challenge the utility of 'separation of concerns' in this context.
  • To define fundamental concepts for effective technology-agnostic information modeling.

Main Methods:

  • Conceptual analysis and synthesis of principles for information modeling.
  • Argumentation for a collaborative team structure.
  • Identification and presentation of six core concepts for collaboration.

Main Results:

  • Clinical information modeling necessitates a team including healthcare professionals, process/content analysts, and terminologists.
  • 'Separation of concerns' is identified as potentially unhelpful in this collaborative process.
  • Six fundamental concepts are presented to guide collaborative, technology-agnostic information modeling.

Conclusions:

  • A collaborative, integrated team approach is superior for clinical information modeling.
  • Understanding core concepts is vital for successful technology-agnostic information modeling.
  • This framework supports enhanced healthcare interoperability through better semantic models.