Designing reviewable AI workflows for clinical environments

Architecture principle

AI may assist the workflow, but authorization and human review determine what becomes an approved clinical or operational record.

Control implications

• Keep AI-generated drafts distinct from approved records
• Authorize access to patient context explicitly
• Create escalation paths for uncertainty and exceptions

Architecture and implementation guidance

• Separate AI-generated drafts from final approved records.
• Log prompts, context sources, outputs, review actions, and final decisions where appropriate.
• Avoid broad access to clinical or patient data unless authorization is explicit and necessary.
• Design exception paths for uncertainty, escalation, and manual review.

Design tradeoffs

• Clinical efficiency versus review burden
• Context completeness versus sensitive-data minimization
• Automation convenience versus record integrity

Evidence to design for

• Prompt and context provenance where appropriate
• Review actions
• Final approval events
• Access and exception records

Implementation artifacts

• Clinical workflow architecture
• Data-flow and trust-boundary model
• Human-review control design
• Audit evidence requirements

Continue from architecture thinking to action

Use the related service to understand engagement scope, the industry path to add operating context, and Evidence to inspect how IKEVAR turns architecture ideas into reviewable technical artifacts.