Clinical Modeling Core Principles

Clinical Modeling Core Principles

Stakeholders

• Healthcare Providers
• Terminologists
• Analysts, Modelers
• Vendors, Interface Specialists
• Research

Ten threshold concept of Basic Clinical Modeling

• You are not alone! – Teamwork
• Start talk talkative! – Data elements basics
• Divide and conquer! – Grouping
• Describe the kind of data! – Data types
• Guide to reality! – Examples, examples, examples
• Tell me why! – Evidence
• Foster proper answers! – Operationalization
• Profit from other art! – Re-usability and inheritance
• Document your expectation! – Cardinality and more
• Cycle through life! – Status and Versioning

Work based on a paper:��Team competencies and educational threshold concepts for clinical information modelling. / Scott, Philip; Heitmann, Kai. Decision Support Systems and Education. ed. / John Mantas; Zdenko Sonicki; Mihaela Crişan-Vida; Kristina Fišter; Maria Hägglund; Aikaterini Kolokathi; Mira Hercigonja-Szekeres. IOS Press, 2018. p. 252-256 (Studies in Health Technology and Informatics; Vol. 255). https://researchportal.port.ac.uk/portal/files/11936801/�Team_competencies_and_educational_threshold_concepts.pdf http://ebooks.iospress.nl/publication/50513

You are not alone! – Teamwork

Active participation of clinicians necessary but requires (communication, soft, …) skills Collaborative environment with multi-stakeholder communication Healthcare providers must learn how to communicate well with non-clinical experts and vv Add Process / content analysts and terminologists Perform team-building activities

Start talk talkative! – Data elements basics

• The data elements shall have a short name that allows other to gather what this item means.
• This needs to be simple text in natural language, not acronyms or abbreviations.
  • Examples: Date of birth, Body weight, Type of specimen, Anatomic site, Specimen Collection Time
• Data elements shall always include a description that precisely explains in additional words what the subject of the item is.
• Examples
  • The date and time the specimen was collected
  • The reason why the immunization was refused
  • The administrative gender of a person
• Introduce an identifier (short number for example) for each data element, at least unique within the data set, eventually globally unique; this makes it much easier later to refer to the data element in further discussions.
• Agreement about naming conventions so that you are consistent