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.
PurposeMental illness presents a huge individual, societal and economic challenges, currently accounting for 20% of the worldwide burden of disease. There is a gap between the need for and access to services. Digital technology has been proven effective in e-mental health for preventing and treating mental health problems. However, there is a need for cross-disciplinary efforts to increase the impact of e-mental health services. This paper aims to report key challenges and possible solutions for cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.Design/methodology/approachThe key challenges and possible solutions will be discussed in light of the literature on effective cross-disciplinary research teams.FindingsSix topics have been key challenges in our cross-disciplinary and cross-sectorial research team: to develop a shared understanding of the domain; to establish a common understanding of key concepts among the project participants; to involve the end-users in the research and development process; to collaborate across sectors; to ensure privacy and security of health data; and to obtain the right timing of activities according to project dependencies.Research limitations/implicationsThis study focuses to increase knowledge and training in cross-disciplinary and cross-sectorial research, as this is often referred to as an important tool when developing sustainable solutions for major societal challenges.Practical implicationsThis study needs to include theory and skills training in cross-disciplinary research in research training.Social implicationsCross-disciplinary teams have the potential to address major societal challenges, including more perspectives and more stakeholders than single disciplinary research teams.Originality/valueMajor societal challenges require complex and sustainable solutions. However, there is a lack of knowledge about how cross-disciplinary and cross-sectorial research teams may work productively to solve these challenges. This paper shares experiences regarding the challenges and possible solutions for productive collaboration in cross-disciplinary and cross-sectorial research teams within the domain of e-mental health services.
Machine learning has recently attracted a lot of attention in the healthcare domain. The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, e.g., hospitals. One main difficulty lies in analyzing such data without compromising personal information, which is a primary concern in healthcare applications. Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about data subjects. In this paper, we propose a distributed extremely randomized tree algorithm for learning with privacy preservation. We present the implementation of our technique on a cloud platform and demonstrate its performance based on medical data, including the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project.
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