This lifecycle describes how a healthcare operations team can evaluate reusable AI assistant patterns before broader use.
1. Intake
Define the workflow problem, intended users, expected inputs, expected outputs, and known data sensitivity concerns.
2. Classification
Classify the example data and decide whether the pattern can be tested with synthetic or de-identified material. If the workflow involves sensitive data, pause until local review requirements are clear.
3. Prototype
Create a simple assistant pattern using generic source material. The prototype should make clear that outputs are drafts for human review.
4. Evaluation
Test against sample cases and score:
- Completeness
- Accuracy against source material
- Missing information handling
- Clarity
- Risky or unsupported output
5. Pilot
Allow a small group to test the assistant pattern with reviewed example inputs. Collect time, quality, and usability feedback.
6. Review
Before any broader use, review the pattern against local policy, legal, privacy, security, and compliance requirements.
7. Maintain
Assign an owner, refresh source material, review feedback, and retire patterns that are no longer accurate or useful.
Non-Negotiable Design Principles
- Human accountability stays with the user.
- Example data should be synthetic or de-identified unless local controls support otherwise.
- Assistants should ask for missing information instead of inventing it.
- Outputs should clearly separate facts from assumptions.