Applied Outcomes: Designing CME for Learner Action

You already know how to write learning objectives. You reference Bloom’s taxonomy. You understand Moore’s outcomes framework.

But here’s the real question:

When you write a learning objective, can you clearly identify the two to three specific clinical tasks that must happen for that objective to be achieved?

In this episode—based on a webinar I participated in with the Good CME Practice Group—we go deeper than frameworks. We unpack what actually sits underneath a learning objective and how that layer determines whether your CME changes practice… or simply delivers information.

What We Explore in This Episode

1. Why learning objectives are signposts—not the design itself
2. How to break each objective into 2–3 concrete clinical tasks
3. The role of workflow, format, and audience context in determining granularity
4. How learning science (cognitive load, retrieval practice, feedback) strengthens action-focused design
5. Where CME programs most commonly lose alignment between need, content, assessment, and outcomes

Key Takeaway

If you can’t name the specific clinical actions required to meet an objective, the content won’t drive behavior change.

Design lives underneath the objective.

Next Step

If this episode resonated, try this:

Take one learning objective from a current project and ask:

1. What are the two or three specific clinical actions underneath it?
2. Where do those actions appear in the content?
3. Where are they assessed?

That exercise alone will elevate your design work.

And if you want structured practice applying this level of thinking—with feedback, live coaching, and a community of CME professionals—explore WriteCME Pro.

This is where writers become design partners.

Resources

Good CME Practice Group

Mentioned in this episode:

AI Practice Lab

Build a Practical, Safe, Repeatable AI-assisted Workflow in Just 4 Weeks. March 9 - April 2 Move beyond experimenting with AI. In this 4-week practice lab, work hands-on with Núria Negrão to build a documented, repeatable AI workflow for research, drafting, and quality control—one you can confidently explain to clients and teams.

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