Every healthcare unit is experiencing tremendous pressure to improve its practice quality across several dimensions. Multiple bodies of literature support the importance of establishing community of practice (CoP) to enrich the professional practice and add the expert context on the patient cases. The CoP emphasizes the importance of qualitative social learning and connectivity as preferred sources of knowledge updates to guide the practice rather than using the mere direct quantitative evidence. Social learning and connectivity in CoP is a complex sociotechnical process that takes an abstract idea through a cycle of participation and reification to derive more thickened context and refined knowledge that will help largely the accuracy of decision making. This process is not a straightforward one requiring the use of suitable hyper structure for representing the contextual evolving knowledge as well as a flexible infrastructure to enable CoP learning from experts, agents and connected services as well as other sources of data and knowledge. This article focuses on using the notion of workflow as the hyper structure and Node-RED as the platform that can facilitate CoP learning and connectivity. The focus is on using the CoP Node-RED workflows in healthcare settings to provide basic collaboration and connectivity as well as extensions to facilitate higher participation, learning, and connectivity to arrive at reification of the practice experience. With Node-RED workflows ideas can be represented as flows and sub flows where it can be shared with other CoP members as JSON hyper structure for further improvement, analytics and decision making.