Splinter group update – Authoring Patterns; 'SDMN vs Data sources' Principles for SDMN modeling
Actions: catalog of questions / candidates, shared glossary (discuss action, lead, timeline) ODEF – follow up conversation? Stories of success – who is using and what value are they getting? Specific examples Sales and marketing – who is the market, who is competition
| | SDMN impacts Authoring Strategy, Data Modeling under Authoring…? ….depends; Reference of google slides - info to wiki from Thursday meetings to be completed, coming soon! ODEF - Mike C - post info, please review Success Stories - what do we mean by success? Authoring perspective - model use, what made them good models? Interoperability, speed, How do we define success? no implementation of models yet (in use, practical, clinical); models have been presented; getting to the point of success, implemented in use in practice; convince leadership of organization they should pay attention to (moving down the pathway to end state); iterative path; depends on the goal to be achieved (documenting, analyze and improve, enabling how things are done, machine automation); perhaps a better question for Implementation Group BPM+ has been more successful (language) at modeling, modeling methodology achieved success over other possible alternatives Good to see (this group) what has been presented from Authoring perspective, to see what could be improved on in the model; critique and feedback from authoring perspective; get a few presentations going and have authoring perspective POV set of critiques; gives some successes for authoring perspective; look at examples to determine what to improve; set up place in Confluence to view the models; example from Vista hard-coded workflows (to identify); success story here regarding the implementation (potential issue with NDAs) for models; examples from CDS Library (of success, BPM+), perhaps use a few examples from there to do the critique process / feedback; examples (discuss pros/cons) boundaries, binding model to specific data source, clinical model (types of models); data modeling, then move to clinical data, separation of concerns (knowledge modelers vs data platform integrators, data modelers) - connection is what needs to be discussed
|