Product design question
Design the clinician experience for a chat-based CDS assistant that can be used before, during, and after a visit. What should be proactive vs. on-demand, where should guidance appear inside the workflow, how should evidence and uncertainty be shown, and what interaction patterns would you use so clinicians can act quickly without disrupting patient care?
- Abridge
- Product design
- Hard
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What this question tests
Tests designing a clinical assistant experience across the care journey, deciding proactive versus on-demand behavior and how to present evidence and uncertainty without disrupting patient care.
How to approach it
- Before the visit, use proactive prep summaries of relevant history and flagged items, since the clinician has time to review them.
- During the visit, keep the assistant on-demand only, since proactive interruptions risk disrupting the patient interaction.
- After the visit, allow both: proactive suggestions for documentation gaps, and on-demand lookup for specific questions, since bandwidth is higher.
- Place guidance inside the existing charting workflow rather than a separate app, so clinicians don't context-switch to get an answer.
- Show evidence inline, a short citation next to any suggestion with a one-click path to the source, and flag uncertainty explicitly when evidence is limited or conflicting.
- Design for speed: short, scannable suggestions with a clear accept, dismiss, or investigate-further action, so a clinician can act in seconds.
What a strong answer includes
- Splits proactive versus on-demand by care moment, since interrupting an active visit carries real risk that pre- or post-visit interruption does not.
- Embeds guidance inside the existing charting workflow, avoiding a context-switch cost clinicians would resist.
- Shows evidence and uncertainty inline and explicitly, respecting that clinicians need to calibrate trust per suggestion.
Common mistakes
- Making the assistant proactive during the actual patient visit, risking disruption to the interaction.
- Presenting all suggestions with uniform confidence, hiding when evidence is weak or conflicting.
- Requiring clinicians to leave their workflow to verify a suggestion, adding friction that discourages use.
Likely follow-up questions
- How would you handle a suggestion that conflicts with what the clinician already documented?
- What would you change if clinicians say post-visit suggestions feel like too much?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop