Learning From eHealth Implementations Through "Implementomics": A Multidimensional Annotation Model Applied to...

Area of Science:

• Digital Health
• Health Informatics
• Knowledge Management

Background:

• Global increase in digital health technologies generates vast data.
• Many digital health projects suffer from "pilotitis," failing to scale or share knowledge effectively.
• Need for structured documentation of digital health implementation to avoid redundancy and optimize investments.

Purpose of the Study:

• Introduce the RAFT annotation model for semantically describing digital health outputs and projects.
• Demonstrate the model's utility as a proof of concept for the Implementome knowledge system.
• Facilitate knowledge documentation and sharing within the RAFT network and the broader digital health domain.

Main Methods:

• Development of the RAFT annotation model for semantic description of implementation elements.
• Application of the model to annotate outputs and projects within the RAFT network.
• Conceptualization of the Implementome as an interconnected, multidimensional knowledge system.

Main Results:

• The RAFT annotation model enables semantic description of diverse implementation project elements.
• The model facilitates knowledge documentation and sharing for digital health initiatives.
• Proof of concept established for the Implementome, a system for navigating annotated projects and information.

Conclusions:

• The RAFT annotation model is a valuable tool for structuring and sharing digital health implementation knowledge.
• The model can be extended to encompass the entire digital health domain for enhanced knowledge management.
• The Implementome, powered by such models, can foster consensus, best practices, and guidelines in digital health.