Healthcare organizations are increasingly piloting clinical AI tools, but a common question remains: What does a practical evaluation actually look like? Digital Health Canada’s Clinical AI Toolkit is designed to support early-stage evaluation using a small number of indicators and directional signals. This example illustrates how that approach can be applied in a real-world setting.
Use Case: AI-Supported Clinical Documentation
A healthcare organization introduced an AI documentation assistant in a primary care setting to reduce administrative burden and improve workflow efficiency. A short Minimum Viable Evaluation (MVE) was conducted using a small number of KPIs drawn from the Clinical AI Toolkit’s KPI Library, including:
- Task Completion Time / Time to Decision
- Hours Saved
- Clinician-Reported Clinical Value
- User Satisfaction
- Observed Harm or Safety Concerns
Early signals suggested improvements in documentation time and clinician experience, with some variability in note quality and continued reliance on clinician review. The Use Case exampe reflects a typical early-stage evaluation using the Clinical AI Toolkit. A detailed, anonymized version of this evaluation, including context, metrics, and interpretation, is available at the link below.
How the signals were interpreted
Early-stage evaluation produces directional signals across a small number of KPIs. These signals are interpreted together rather than in isolation. In this case, Operational signals suggested improved efficiency; Experience signals were generally positive, but not uniform; Safety signals did not indicate harm, but reinforced the need for clinician oversight; and Directional ROI signals (for example, time saved) were positive, but modest and variable. No single KPI determined the outcome. The assessment focused on the overall pattern across domains.
Next steps
Based on these early signals, the organization chose to continue use of the tool, refine implementation (including clinician guidance and expectations for review), and monitor variability in note quality and workflow integration. The Clinical AI Tool was not immediately scaled broadly. Instead, the organization prioritized learning and adjustment before expansion.
What this example demonstrates
This example reflects several key principles: A small number of KPIs can generate meaningful signals. Early evaluation is directional and context-dependent. Workflow fit and user experience are as important as efficiency. Safety can be monitored using simple checks in early stages. Decisions can be made without waiting for comprehensive evidence.
Using this approach
Organizations do not need advanced analytics or long evaluation periods to begin evaluating clinical AI. A practical approach is to select one use case, measure a small number of KPIs (typically two to four), interpret signals across domains and, finally, make a decision to continue, refine, scale, or stop.
Explore the Clinical AI Toolkit
The Clinical AI Toolkit provides a structured approach to applying this type of evaluation in practice. The Toolkit includes instructions for conducting a Minimum Viable Evaluation (MVE), a KPI Library for selecting indicators, directional ROI guidance, and decision support for early-stage implementations. Explore the toolkit at the link below.
Key Point
Effective clinical AI adoption does not depend on perfect evidence. It depends on the ability to generate early signals, interpret them in context, and make timely, informed decisions. Organizations that can do this consistently will move faster, learn more quickly, and scale what works.
Contribute to shared learning
Many organizations are facing similar questions as they begin evaluating clinical AI. If your organization has applied the Clinical AI Toolkit, consider sharing a short summary of your approach and what you observed. We are particularly interested in:
- What you chose to measure
- What signals emerged early
- How those signals informed your decision
Contributions may be anonymized and used to develop practical examples and future guidance. Share your experience at chief@digitalhealthcanada.com.