Agentic AI: The Missing Link Between Trust and Automation in Primary Care

In primary care today, we face a paradox: while digital health technologies hold immense promise to alleviate administrative burden, free up clinician time, and improve patient outcomes, many clinicians remain wary when it comes to full-scale automation. It’s a healthy skepticism, and one that is grounded in a deep understanding of the personal, relational nature of care provision.

Enter Agentic AI, a newly emerging class of artificial intelligence that may hold the key to resolving this tension. Unlike traditional AI models, which are often narrowly task-specific and reactive, Agentic AI systems are goal-directed, capable of autonomously planning and executing complex workflows in collaboration with human users. These systems don’t just respond; they anticipate, decide, and act, while being able to explain their reasoning and accept course corrections from clinicians.

What Makes Agentic AI Different?

Where rule-based systems or even more advanced machine learning models often require exact prompts or predefined pathways, Agentic AI introduces the possibility of co-pilots that understand broader objectives (i.e., optimizing a clinician’s schedule or identifying high-risk patients for early intervention) and can take initiative to carry out multi-step processes toward those goals.

Imagine an AI agent that can:

• Identify patients due for chronic disease monitoring, contact them with appropriate messages, and schedule them in slots optimized around a clinician’s existing calendar.
• Prepare comprehensive visit summaries ahead of appointments, pulling structured and unstructured data together into a clear, clinician-ready snapshot.
• Monitor incoming data (labs, notes, remote patient monitoring) and alert care teams only when meaningful action is needed, reducing noise and improving signal.

In each case, the clinician remains in control, setting parameters and reviewing outcomes, but the heavy lift is done by the AI. This would leave more time for clinicians to focus on what only they can do: build relationships, make complex decisions, and provide human-centred care.

Proceeding with Care: Privacy, Security, and Trust

As with any emerging technology, caution is warranted. Agentic AI systems are complex, and their ability to act independently raises legitimate concerns about privacy, security, and accountability.

Primary care environments must be especially vigilant:

• How is patient data being accessed, processed, and stored?
• Can we verify how decisions are made by the AI?
• Are these systems compliant with provincial and national privacy laws?
• What safeguards are in place to prevent inappropriate actions or recommendations?

Trust must be earned, and it must be built into the design, evaluation, and deployment of these tools from the outset. This includes transparency about system capabilities and limitations, clear human oversight pathways, and rigorous testing in real-world settings before full adoption.

Our Role: Supporting Evaluation and Change Management

Our team is actively tracking developments in Agentic AI and engaging with both vendors and clinical teams to ensure any future deployments are:

• Clinically relevant and usable, not just theoretically promising.
• Safe and compliant, with robust data governance.
• Change-managed, with training, workflow integration, and ongoing support to ensure successful adoption.

We’re here to partner with clinicians and primary care leaders in evaluating opportunities for Agentic AI, ensuring that every new tool serves a clear purpose: making care better for patients, and more sustainable for those who provide it.

Let’s explore this future together – deliberately, ethically, and with our eyes wide open.