Authors:
Job N. Nyameino
1
;
Fazle Rabbi
2
;
Ben-Richard Ebbesvik
2
;
Martin C. Were
3
and
Yngve Lamo
2
Affiliations:
1
Department of Informatics, University of Bergen, Bergen, Norway, Institute of Biomedical Informatics, Moi University, Eldoret and Kenya
;
2
Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen and Norway
;
3
Institute of Biomedical Informatics, Moi University, Eldoret, Kenya, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, U.S.A., Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, TN and U.S.A
Keyword(s):
Clinical Practice Guidelines, Model Driven Engineering, Gamification.
Related
Ontology
Subjects/Areas/Topics:
Cross-Feeding between Data and Software Engineering
;
Model-Driven Engineering
;
Software Engineering
;
Software Engineering Methods and Techniques
Abstract:
Clinical practice guidelines (CPGs) play a fundamental role in modern medical practice since they summarize the vast medical literature and provide distilled recommendations on care based on the current best evidence. However, there are barriers to CPG utilization such as lack of awareness and lack of familiarity of the CPGs by clinicians due to ineffective CPG dissemination and implementation. This calls for research into effective and scalable CPG dissemination strategies that will improve CPG awareness and familiarity. We describe a formal model-driven approach to design and implement a gamified e-learning system for clinical guidelines. We employ gamification to increase user motivation and engagement in the training of guideline content. Our approach involves the use of models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game model to manage the training sessions. A game engine instantiates a trai
ning session by coupling the workflow and entity models to automatically generate questions based on the data in the model instances. Our proposed approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.
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