# Module 1 Narration

## Opening

Open with the professional setting: a clinical informatics committee evaluating decision support before adding it to a clinician workflow. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.

## Middle

Move through the module in four passes:

1. Define **Clinical decision support foundations** in the context of AI for Clinical Decision Support.
2. Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.
3. Compare a baseline with an AI-enabled or more sophisticated alternative.
4. Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.

## Closing

Close by returning to the module artifact: **clinical decision support safety case with escalation, override, and monitoring requirements focused on clinical decision support foundations: Classify decision support use cases by risk.**. Students should leave knowing exactly what artifact they are producing and how it will be judged.