What Is Agentic AI in Healthcare?
Why Agentic AI Fails Without Interoperability and How FHIR Solves It
Four FHIR Capabilities Enabling Agentic Workflows
Why Structured Clinical Data and Context Matter for Safe AI Automation
Human-in-the-Loop: Why Agentic AI Requires Approval Workflows
CDS Hooks and SMART on FHIR: EHR Integration Standards for Agentic Workflows
Building Toward Agentic AI: Governance and Implementation Readiness
Key Takeaways
FAQ
What Is Agentic AI in Healthcare?
Agentic AI has become a prominent topic in healthcare technology discussions, but the term often lacks clarity in clinical contexts. If you’re evaluating where AI fits in your product roadmap or considering EHR integration consulting to support AI features, understanding what agentic AI actually means is essential for sound architectural decisions.
In its simplest form, agentic AI refers to systems capable of taking autonomous action toward a goal, rather than simply answering questions or generating text. A chatbot that responds to patient questions is not agentic. A system that identifies a patient at risk for readmission, drafts a care plan, schedules a follow-up, and notifies the care team without requiring a human to initiate each step would be agentic. That’s the vision many technology leaders describe. The clinical reality, however, looks quite different.
The Gap Between Vision and Reality
Today’s clinical AI remains fundamentally reactive and advisory. Most implementations fall into narrow categories: chatbots that triage symptoms, documentation assistants that summarize notes, risk models that flag patients for review, and clinical decision support alerts. These tools provide value, but the human clinician still makes every decision and takes every action. The AI suggests – the clinician executes. True agentic AI would invert this: the AI would execute, and the clinician would approve or supervise, creating entirely different technical, regulatory, and safety requirements.
Healthcare presents unique challenges. An AI that books an incorrect flight creates inconvenience. An AI that orders an inappropriate medication or misses a drug interaction can cause serious patient harm. Clinical context is messy and incomplete, with the “right” action often depending on preferences, circumstances, and nuances not captured in structured data. Accountability is legally defined, and regulatory frameworks don’t provide clear answers for autonomous systems making consequential decisions. This doesn’t mean agentic AI is impossible – it means the path forward requires careful architecture. FHIR provides a foundation that enables thoughtful clinical automation.
Why Agentic AI Fails Without Interoperability and How FHIR Solves It
Autonomous AI systems require reliable data access and predictable mechanisms for taking action – capabilities healthcare has historically lacked. Consider what an agentic workflow requires: an AI identifies declining kidney function in a diabetic patient and needs to pull current medications from the EHR, check for contraindicated drugs, draft an order modification, and document the rationale. Each step requires reading from or writing to clinical systems through consistent interfaces. Without standardization, every deployment becomes bespoke: you cannot build scalable workflows when every hospital requires different API calls and data formats.
FHIR is what turns EHR connectivity from one-off custom work into repeatable building blocks—so agentic workflows can scale beyond a single health system. For a practical roadmap (discovery → build → validation), see FHIR integration for digital health companies.
Consistent data structures and API patterns in FHIR enable workflows that function beyond a single implementation. Standardization enables three critical capabilities:
- portable logic where decision rules reference standard resources across Epic, Cerner, or MEDITECH;
- auditable actions where reads and writes log consistently for regulators;
- incremental automation where you can progress from read-only recommendations to write capabilities without architectural rewrites.
Consultant’s Tip: Before building any agentic capability, map your entire workflow to FHIR resource types. If a step can’t be represented as a standard FHIR operation, that’s where your integration will break when you scale.
Four FHIR Capabilities Enabling Agentic Workflows
Autonomous clinical systems need data they can reliably interpret and act upon. FHIR provides the technical foundation that makes this possible.
Standardized Data Models
FHIR defines consistent resource structures: Patient, Observation, Condition, MedicationRequest, CarePlan. When your AI evaluates a patient’s state, it queries predictable fields and relationships.
This matters because autonomous decision-making requires reliable interpretation. An AI can’t safely act on lab results if “abnormal” means different things in different data feeds. FHIR’s standardized value sets provide a consistent semantic foundation.
Consistent RESTful APIs
FHIR specifies interaction patterns, not just data structures. Create, Read, Update, Delete operations follow standard HTTP conventions. Search parameters work consistently across resource types.
For agentic AI, this means “works anywhere FHIR is implemented.” Your workflow uses the same API calls to check allergies, whether the data lives in Epic or Cerner. Reading and writing use the same patterns – an AI that queries Observations to detect risk can later create Observations to document findings.
Explicit Clinical Semantics
FHIR resources reference standard terminologies: SNOMED CT for findings, LOINC for labs, RxNorm for medications. Your AI interprets coded values against established ontologies rather than parsing free text.
For autonomous action, coded data enables programmatic safety checks. Before drafting a medication order, the AI verifies against the patient’s coded allergy list. That verification is only possible with consistent terminology.
Auditability and Governance
FHIR includes provenance tracking: where data came from, how it was modified, by whom, and when. When an AI takes action, that action becomes part of the auditable medical record.
This isn’t optional for clinical AI. Regulators and risk managers will reconstruct what happened when something goes wrong. FHIR’s native provenance support makes that reconstruction possible without custom audit infrastructure.
Why Structured Clinical Data and Context Matter for Safe AI Automation
AI decision quality depends entirely on input data quality. FHIR’s resource model encourages structured, coded data. When the AI evaluates blood pressure control, it applies deterministic logic to coded values with LOINC codes, units, and reference ranges. It doesn’t parse “BP running a bit high lately” from clinical notes.
Effective decisions also require longitudinal context. A single elevated reading means different things for a patient with 20-year hypertension history versus a healthy 30-year-old. FHIR enables assembling context by querying across resource types and time periods. In practice, data quality varies enormously – some fields are reliably coded, others are frequently missing. AI designed for autonomous action must handle quality issues gracefully: refusing to act when critical context is missing, flagging low-confidence recommendations for review.
Before you automate anything, you need reliable inputs, consistent coding, and an audit trail you can defend. Treat that as integration work – find the sequencing outlined in AI readiness roadmap for healthcare startups.
Human-in-the-Loop: Why Agentic AI Requires Approval Workflows
Recommendations vs. Actions: The Essential Distinction
This distinction is fundamental to how agentic AI should function clinically.
Recommendations are AI output: “This patient should receive a flu vaccine,” “Consider adjusting this dose.”
Actions are changes to the medical record: the order entered, the prescription modified.
In properly designed workflows, AI generates recommendations and prepares corresponding actions, but humans approve before execution. The AI performs cognitive work identifying what should happen; the clinician validates and authorizes.
This “recommend and prepare, then approve” model isn’t a temporary compromise until AI gets smarter. It’s a fundamental design principle. Clinicians bring contextual knowledge AI cannot access, professional judgment from training and experience, and legal accountability that cannot be delegated to software. AI brings consistency, comprehensive data review, and ability to surface overlooked information. Both contributions are essential.
Designing Effective Approval Workflows
Effective approval design follows key principles:
- Make approval fast for routine cases. One-click confirmation when the recommendation is clearly correct.
- Force attention for edge cases. Require explicit acknowledgment when confidence is low or risk factors exist.
- Provide easy override paths. Clinicians must modify or reject without friction.
- Document every decision. Approved, modified, or rejected – it becomes part of the record.
Current regulatory frameworks assume human decision-making. Clinicians are licensed and accountable; AI systems are tools they use. This means write-capable clinical AI operates under significant constraints. Even when the AI drafts an order, a licensed clinician typically must sign it.
Common Mistake: Treating human approval as an annoying constraint to minimize. Clinician approval is your safety mechanism and regulatory compliance pathway. Design it as a core feature, not an obstacle.
CDS Hooks and SMART on FHIR: EHR Integration Standards for Agentic Workflows
FHIR provides the data layer. Two complementary specifications – CDS Hooks and SMART on FHIR – provide integration patterns that make AI-assisted workflows practical within EHR systems.
CDS Hooks: Triggering AI at the Right Moment
CDS Hooks defines standardized mechanisms for invoking external decision support at specific workflow points. When clinicians open charts, place orders, or schedule appointments, the EHR calls your AI service, passing clinical context and receiving structured recommendations.
CDS Hooks provides the critical “when” of clinical intervention. Your AI activates precisely when clinicians make decisions, with full context about what they’re doing and which patient they’re treating. Standard hook points include:
- patient-view: opening a record, enabling alerts, and care gap identification
- order-select: beginning an order, enabling real-time guidance
- order-sign: before signing, enabling safety checks and alternatives
- appointment-book: scheduling, enabling coordination recommendations
Each hook expects “cards”: structured recommendations displayed within the workflow. Cards include alerts, suggested actions, and links to SMART applications. Recommendations appear at the decision moment, not in separate dashboards. Clinicians accept suggestions with minimal friction rather than manually re-entering orders.
SMART on FHIR: Embedded Applications
SMART on FHIR specifies how applications launch from within EHRs with appropriate authorization. While CDS Hooks delivers focused recommendations, SMART enables richer experiences, presenting complex recommendations with evidence, multi-step approval workflows, and collecting additional input. CDS Hooks triggers AI at the right moment; SMART apps launch with full context when clinicians need deeper interaction.
The hard part of SMART is operational, not conceptual: aligning scopes, permissions, and data availability with the actual clinical workflow. For the issues we see most often, see common pitfalls in SMART on FHIR implementation.
From Advisory to Autonomous Workflows
Together, these standards enable progression from advisory toward automation. It does require some steps, each having a distinct human role in sight.
Most healthcare AI operates at Stage 1 or 2. Moving further requires demonstrated safety and organizational trust. Standards provide foundation; validation and governance determine how far you can responsibly go.
Consultant’s Tip: Start CDS Hooks with read-only, advisory capabilities even if your goal is deeper automation. This validates clinical logic, builds trust, gathers evidence of recommendation quality, and refines workflow integration before taking on write-capable complexity.
Building Toward Agentic AI: Governance and Implementation Readiness
The question isn’t when you’ll deploy fully automated workflows – that timeline remains uncertain. If you’re building clinical AI with an eye toward future autonomous capabilities, what matters is what you should do now.
Technical Readiness
Before pursuing any agentic capability, validate these foundations:
- FHIR read access for reliable data input
- FHIR write access for future automation
- CDS Hooks integration for real-time workflow triggering
- SMART app capability for interactive approvals
- Provenance tracking for audit trails
- Terminology mapping for consistent interpretation
Governance and Implementation Sequencing
Before deploying AI-influenced clinical decisions, establish governance:
- scope boundaries documenting what AI can recommend
- monitoring for poor recommendations or drift
- escalation paths for situations outside confidence thresholds
- failure remediation processes
- human accountability
A realistic path focuses on foundations:
Phase 1 builds robust FHIR read integration, validates data quality, and implements advisory AI through CDS Hooks.
Phase 2 adds capabilities for AI to prepare actions with approval workflows requiring genuine review.
Phase 3 designs AI-ready data architecture: normalized storage, provenance tracking, consent-aware handling.
Phase 4 expands AI involvement based on demonstrated safety, maintaining human oversight.
Clinical AI automation isn’t purely technical with a predictable timeline. It depends on regulatory evolution, organizational comfort, and accumulated safety evidence. Building foundations positions you when the broader environment is ready.
Key Takeaways
FHIR provides the foundation
Standardized data models, consistent APIs, explicit semantics, and built-in auditability give AI systems the interoperability clinical automation requires.
Agentic AI should recommend and prepare; humans should approve and execute
This isn’t a limitation – it’s a human-in-the-loop design principle reflecting regulatory reality and sound clinical practice.
CDS Hooks and SMART on FHIR enable integration
These specifications let AI activate at the right moment and deliver recommendations within workflows.
Start advisory, earn autonomy
Prove recommendations are good before automating execution. Build monitoring and governance before you need them.
An experienced healthcare integration consultant can help design architecture supporting today’s advisory features while keeping the path to future automation open. The startups building responsible agentic infrastructure now will be positioned to deliver autonomous capabilities when the technology and regulatory frameworks catch up.
FAQ
Traditional automation follows fixed rules (“if X, then do Y”) and usually runs on a narrow dataset. Agentic AI can plan and execute multi-step tasks toward a goal (e.g., identify a gap, gather context, propose an action, route for approval, document the result). In healthcare, the key shift is workflow ownership: the system doesn’t just suggest – it prepares the next steps in the actual clinical workflow.
Advisory AI generates recommendations that humans must manually act upon, like suggesting a patient needs a flu vaccine. Write-capable AI can create or modify clinical data: drafting orders, updating care plans, documenting findings. The AI prepares the action in the EHR; the clinician reviews and approves with a click rather than re-entering everything manually.
CDS Hooks provide the “when” – triggering your AI at specific workflow moments like opening a chart or placing an order. The EHR sends clinical context; your AI returns structured recommendations displayed exactly where clinicians make decisions.
SMART on FHIR provides the “how” for complex interactions – launching embedded applications when clinicians need deeper engagement, like reviewing batched recommendations or providing additional input. Together, they let AI integrate into existing workflows rather than forcing clinicians into separate systems.
In current real-world deployments, licensed clinicians and the organization remain accountable for clinical decisions. Your product should make that operationally true: clear approval steps, transparent rationale, and audit trails that show what was recommended, what was approved, and why.