Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System

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.acceptedVersio

Gamification To Promote Guideline Training In Health Care

Clinical practice guidelines are recipes for how clinician can identify a specific medical condition in a patient, how to manage and provide treatment for such a patient. These are evidence based statements, which includes recommendations to optimize patient care. Well defined guidelines has shown the effect of improving the quality of health care at a lower cost, as well as reducing practice variability. Despite the positive effects of clinical practice guideline, they have shown a limited effect on changing the clinicians practice methods. Some clinician don't know that such guidelines exists, they are not familiar with the guideline content, they lack the self-confidence to execute the recommended treatment, the previous practice methods make it difficult to adapt to the new recommendations, or the guideline format itself is to cumbersome to read and use. The purpose of this master thesis is to address some of the reasons why the clinical practice guidelines haven't been put more into use. We propose a serious game which will contribute to awareness of the guidelines and training in the guideline content itself. We do present four models. A guideline model, which describes the workflow of the clinical practice guideline. A domain or entity model, which describes the patient, his symptoms, diagnosis and how the clinicians have managed his medical condition. A student learning model which keeps track of the student's performance at different quizzes. A game model which holds information about game elements and the ordering of the game/learning material. By using information from the game model and the student learning model, we can make the game adaptable to the knowledge and progression of the student, as well as flexible such that the student can choose different paths through the learning material

Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System

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

Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System

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