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The implementation of digital health technologies has increased globally, producing substantial amounts of information and knowledge. While there are still areas in digital health that are understudied, concurrently there is an exponential increase in published articles, guidelines, methods, projects, and experiences, many of which fail to reach critical mass (pilotitis). Semantically describing and documenting this implementation knowledge and the effectiveness of these tools will help to avoid the duplication of efforts, to reduce preventable implementation obstacles, and to assure that investments are targeted to the most important technological innovations. The RAFT annotation model, presented in this paper, enables to semantically describe all elements of various outputs and implementation projects that were developed, are used, or are part of the RAFT network. This model was initially developed to annotate various implementations and outputs of the RAFT network to facilitate knowledge documentation and sharing, and to be used as a proof of concept for the  Implementome . The  Implementome  will be an interconnected knowledge system that enables the user to navigate on multiple dimensions through metadata annotated projects, people, and information, and can serve as base for consensus building, best practices and guidelines. The RAFT annotation model can be further developed to enable the annotation of outputs, implementations, people, initiatives, and projects of the digital health domain in general.

Learning From eHealth Implementations Through “Implementomics”: A Multidimensional Annotation Model Applied to eHealth Projects of the RAFT Network