Healthcare organizations are increasingly exploring clinical AI, but not all use cases carry the same level of risk, complexity, or readiness for adoption. The challenge is not awareness, it is where to begin.
This guide outlines practical starting points for clinical AI and how to generate early value while managing risk appropriately. Suggested KPIs are drawn from the KPI Library and focus on measures that are feasible in low-data settings and appropriate for Minimum Viable Evaluation.
Starting Point: Focus on Workflow-Supporting AI
The most successful early implementations of clinical AI in Canadian healthcare are typically those that support existing workflows, reduce administrative burden, and can be evaluated using a small number of observable indicators. These use cases generally deliver early value with lower implementation risk and are well suited to Minimum Viable Evaluation.
Recommended Starting Points
Documentation and Clinical Note Support
AI tools that assist with documentation, transcription, or note generation are often strong starting points for organizations beginning their clinical AI journey. These tools typically involve lower implementation risk, minimal integration requirements, and observable impact on clinician time and experience.
Suggested starting KPIs may include task completion time, hours saved, user satisfaction, clinician-reported value, user adoption rate, and observed safety concerns. These measures are generally feasible to capture in low-data environments and align well with early-stage evaluation.
Workflow Automation and Task Support
AI tools that support triage routing, scheduling, inbox management, or task coordination can generate operational improvements while remaining relatively manageable to implement. Although these tools often require somewhat greater workflow integration, their impact is usually visible through operational efficiency and workflow fit.
Organizations may wish to focus on measures such as task completion time, hours saved, clinical workflow fit, user adoption, user satisfaction, and any observed safety concerns.
Triage and Prioritization
AI tools that support risk stratification or prioritization of patients, tasks, or requests may offer meaningful operational and clinical value, but they also require greater attention to consistency, safety, and unintended consequences because they influence prioritization decisions within clinical workflows.
Early evaluation may include operational measures such as time to assessment or intervention, along with clinician-reported value, AI recommendation override rates, reported equity concerns where relevant, and observed safety issues.
Use Cases Requiring Greater Caution
Clinical Decision Support and Diagnostic Applications
AI tools that influence diagnosis, treatment decisions, or clinical interpretation generally involve higher clinical and medico-legal risk. These applications typically require stronger evidence, increased oversight, and more rigorous monitoring approaches.
Organizations should continue to apply core minimum KPIs, including measures related to accuracy, error rates, and safety, while considering Expanded Evaluation approaches where clinical impact or organizational risk is higher.
How This Connects to the Toolkit
Organizations are encouraged to begin with one or two lower-risk, workflow-supporting use cases and apply Minimum Viable Evaluation to generate early signals. A small subset of KPIs from the KPI Library can then be selected based on local context, feasibility, and organizational priorities. Directional ROI can support early decision-making, with evaluation depth expanding only where additional rigor is warranted.
Key Takeaway
A successful starting point for clinical AI is not defined by technical sophistication, but by clear workflow fit, observable impact, manageable implementation risk, and the ability to support a confident early decision.
Starting small, learning quickly, and scaling selectively will generally deliver more value than attempting to implement complex, high-risk AI solutions too early.
Explore the Clinical AI Toolkit
Explore the Clinical AI Toolkit to apply a structured, low-burden approach to evaluating your first clinical AI implementation.
Contribute to Shared Learning
Many organizations are facing similar questions as they begin evaluating clinical AI. If you have applied the Clinical AI Toolkit, please share a summary of your experience with chief@digitalhealthcanada.com.
Include a brief description with slides or internal materials, or simply share a high-level use case with KPIs, signals, decisions, and lessons learned. Of particular interest: what you chose to measure, what signals emerged early, and how those signals informed your decision-making. Submissions can be anonymized.