BACKGROUND: The nationwide data infrastructure project HiGHmed strives for achieving semantic interoperability through the use of openEHR archetypes. Therefore, a knowledge governance framework defining collaborative modelling processes has been established. For long-sustained success and the creation of high-quality archetypes, continuous monitoring is vital. OBJECTIVES: To present an update on archetype modelling and governance framework establishment in HiGHmed. METHODS: Qualitative and quantitative analyses of the progress in establishing modelling groups, roles and users, realizing modelling workflows, and modelling archetypes. RESULTS: Currently, 25 modellers and 17 domain experts are participating. 79 archetypes have been identified, from which 69 are pre-existing and internationally published; completion rates of review rounds are satisfying but improvable. CONCLUSIONS: The governance framework is valuable to make the activities manageable and to accelerate modelling. Combined with highly engaged data stewards and clinicians, a reasonable number of archetypes have already been developed.
BACKGROUND: The nationwide data infrastructure project HiGHmed strives for achieving semantic interoperability through the use of openEHR archetypes. Therefore, a knowledge governance framework defining collaborative modelling processes has been established. For long-sustained success and the creation of high-quality archetypes, continuous monitoring is vital. OBJECTIVES: To present an update on archetype modelling and governance framework establishment in HiGHmed. METHODS: Qualitative and quantitative analyses of the progress in establishing modelling groups, roles and users, realizing modelling workflows, and modelling archetypes. RESULTS: Currently, 25 modellers and 17 domain experts are participating. 79 archetypes have been identified, from which 69 are pre-existing and internationally published; completion rates of review rounds are satisfying but improvable. CONCLUSIONS: The governance framework is valuable to make the activities manageable and to accelerate modelling. Combined with highly engaged data stewards and clinicians, a reasonable number of archetypes have already been developed.
Authors: Kim K Sommer; Ali Amr; Udo Bavendiek; Felix Beierle; Peter Brunecker; Henning Dathe; Jürgen Eils; Maximilian Ertl; Georg Fette; Matthias Gietzelt; Bettina Heidecker; Kristian Hellenkamp; Peter Heuschmann; Jennifer D E Hoos; Tibor Kesztyüs; Fabian Kerwagen; Aljoscha Kindermann; Dagmar Krefting; Ulf Landmesser; Michael Marschollek; Benjamin Meder; Angela Merzweiler; Fabian Prasser; Rüdiger Pryss; Jendrik Richter; Philipp Schneider; Stefan Störk; Christoph Dieterich Journal: Life (Basel) Date: 2022-05-18
Authors: Alexander Kiel; Raphael W Majeed; Julian Gruendner; Noemi Deppenwiese; Michael Folz; Thomas Köhler; Björn Kroll; Hans-Ulrich Prokosch; Lorenz Rosenau; Mathias Rühle; Marc-Anton Scheidl; Christina Schüttler; Brita Sedlmayr; Alexander Twrdik Journal: JMIR Med Inform Date: 2022-05-25