Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System
Peer reviewed, Journal article
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Original versionNyameino, J. N., Ebbesvik, B.-R., Rabbi, F., Were, M. C., & Lamo, Y. (2020). Model-driven automatic question generation for a gamified clinical guideline training system. In E. Damiani, G. Spanoudakis, & L. A. Maciaszek (Eds.), Evaluation of Novel Approaches to Software Engineering (pp. 227–245). Springer International Publishing. 10.1007/978-3-030-40223-5_11
Clinical practice guidelines (CPGs) are a cornerstone of modern medical practice since they summarize the vast medical literature and provide care recommendations 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 model-driven approach to design and develop a gamified e-learning system for clinical guidelines where the training questions are generated automatically. We also present the prototype developed using this approach. We use models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game engine to generate and manage the training sessions. We employ gamification to increase user motivation and engagement in the training of guideline content. We conducted a limited formative evaluation of the prototype system and the users agreed that the system would be a useful addition to their training. 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.
This is an author's accepted manuscript version (postprint) of an article published by Springer. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-40223-5_11.