Method
From healthcare need to evaluated AI workflow
Each stage reduces a different uncertainty, from choosing the right problem through testing, ownership, launch and ongoing monitoring.
- 1
Discover
- Define the healthcare problem before choosing a model
- Map the current workflow and responsible people
- Identify available data and system constraints
- Set the baseline and intended outcome
- 2
Design
- Choose the smallest useful AI-assisted workflow
- Define inputs, outputs, roles and integrations
- Design human review and escalation points
- Agree success and acceptance criteria
- 3
De-risk
- Assess clinical, privacy, operational and technical risks
- Use the minimum necessary data
- Test expected, edge and failure cases
- Document boundaries and fallback behaviour
- 4
Deliver
- Build or integrate the agreed workflow
- Run structured evaluation and user testing
- Prepare training, ownership and support
- Launch only against agreed acceptance criteria
- 5
Monitor
- Track quality, adoption, errors and incidents
- Review human overrides and escalations
- Compare performance with the baseline
- Improve or stop the workflow based on evidence