Overview
Agentic AI represents a new frontier in healthcare innovation, offering proactive support without undermining clinicians’ autonomy or confidence. This article explores how to integrate these systems in ways that promote trust, clarity, and collaboration—by putting human needs at the core of AI design.
What’s the story
As agentic AI begins to emerge in healthcare, organizations must grapple with a fundamental question:
How do we integrate AI systems that act autonomously—without removing the autonomy, identity, or confidence of our clinicians?
Agentic AI is defined by its ability to act proactively, optimize toward a goal, and respond to context without explicit human prompting. Unlike traditional role-based AI systems that are narrow and reactive, agentic AI can pursue complex objectives, such as improving patient outcomes, streamlining workflows, or enhancing patient experience—sometimes without direct clinician involvement.
That evolution raises powerful questions for implementation and adoption—especially in clinical environments built on trust, safety, and shared decision-making.
Self-Determination Theory: A Human Lens on AI Adoption
To understand how to design and implement agentic AI responsibly, we can turn to Self-Determination Theory (SDT) by Deci and Ryan—also expanded on by Paula Davis in her work on burnout and resilience.
SDT outlines three psychological needs that, when met, foster motivation, engagement, and well-being:
- Autonomy
- Belonging
- Competence
These aren’t just personal preferences—they are foundational to how clinicians relate to their work and whether they’ll engage with AI as a partner or resist it as a threat.
1. Autonomy: Supporting Clinical Judgment, Not Replacing It
Nurses and providers want agency in decision-making. AI adoption fails when systems are implemented top-down with rigid rules or black-box outputs.
Agentic AI must be designed to augment, not override, clinical judgment. That means:
- Showing how the system reached its conclusions
- Allowing clinicians to modify or override recommendations based on patient-specific factors
- Reinforcing the why behind AI-supported care plans
Autonomy doesn’t mean “do it alone”—it means clinicians still feel ownership and control in their work, even as they partner with AI.
2. Belonging: Clarifying the Clinician’s Role in an AI-Augmented Team
As AI agents begin to take on more tasks—from triaging patients to answering FAQs to flagging risks—clinicians may ask:
“Where do I fit into this new system?”
This sense of role clarity and connection is essential. Belonging comes from:
- Involving clinicians in AI design, testing, and feedback loops
- Clearly defining how AI supports the mission of care teams—not replaces them
- Creating shared goals across human and digital teammates
When AI supports shared purpose, clinicians are more likely to feel included, not sidelined.
3. Competence: Building Skill and Confidence in Working With AI
Agentic AI shifts workflows. It may recommend clinical pathways, suggest medication changes, or enhance patient communication.
But do clinicians feel confident in those interactions?
Adoption requires:
- Training that builds AI literacy, not just software use
- Scaffolded learning—introducing AI with guardrails and supervision, then gradually expanding autonomy as comfort grows
- Opportunities to develop adjacent skills, like motivational interviewing or complex communication—skills AI may support, but humans must still lead
Clinicians need to know when to trust the AI, when to question it, and how to explain its outputs to patients with empathy and accuracy.
What Makes Agentic AI Different—and What Still Applies
Unlike traditional AI, agentic systems:
- Can act proactively based on a user’s goals and context
- Combine machine learning, automation, and natural language processing
- Are designed to reduce hallucinations by applying reasoning and sourcing validation
- Can learn and reflect organizational values—from clinical standards to communication tone
But even with these advances, the basics remain:
- You still need clear goals and metrics
- You still need to understand the problem you’re solving
- You still need strong feedback loops to guide iterative improvement
Agentic AI doesn’t eliminate the need for change management—it raises the stakes.
Where to Start in Healthcare
Not every application of agentic AI needs to begin with clinical decision support. In fact, non-clinical workflows—such as scheduling, medication refills, or responding to social determinants of health—offer excellent low-risk entry points.
These use cases:
- Allow teams to build trust in the system
- Reduce cognitive burden without increasing risk
- Free up time for clinicians to focus on high-touch care
Final Thought:
Agentic AI will only transform care if it’s implemented with humans at the center. By honoring clinicians’ need for autonomy, belonging, and competence, healthcare organizations can make agentic AI not just a technological upgrade—but a cultural one.
