One Size Doesn’t Fit All: Transactional FHIR, Bulk FHIR, and Agentic Exchange
Healthcare interoperability is entering a new phase. As adoption of standards such as HL7® FHIR® matures, the conversation is shifting from whether healthcare data can be exchanged to how it should be exchanged for different use cases. At the same time, agentic AI is introducing another possibility. AI agents can potentially identify what information is needed, retrieve it from multiple sources, interpret it, and take action based on the task at hand.
That raises an interesting question: If AI can increasingly navigate and make sense of complex healthcare data, do we still need standardized approaches, such as FHIR?
The answer is yes depending on use case. The future of interoperability is bigger than any single exchange method. Transactional FHIR, Bulk FHIR, and emerging agentic approaches solve different problems. Healthcare organizations need an interoperable data foundation that allows them to use the right approach for the right use case.
Transactional FHIR: The Right Data at the Right Moment
Transactional FHIR is designed for targeted exchanges of specific information in real-time, typically in response to a request, event, or workflow. For example, a provider may need information about an individual patient during a clinical encounter. Instead of moving an entire population’s data, the exchange retrieves the most up-to-date information limited to what is needed for that interaction.
Best Suited For | Individual patient interactions, point-of-care workflows, prior authorization, patient and provider access, and other targeted data requests |
Strengths | Standardized, precise, integrated with workflow, and well suited for targeted access to the most up-to-date information |
Considerations | Individual requests become inefficient when organizations need data across a large population of patients |
Bulk FHIR: Moving Data at Population Scale
Bulk FHIR is designed to efficiently exchange large volumes of standardized healthcare data for populations of patients. Instead of making thousands of individual requests, an organization can exchange data for a defined population in bulk. This is especially valuable when organizations need specific information across many patients refreshed with a regular cadence rather than information about a single patient at a single moment. While newer AI-driven approaches are attracting attention, the need for efficient population-scale exchange is not going away.
Best Suited For | Population health initiatives, quality measurement, care gap workflows, value-based contract attribution, eligibility, analytics, risk adjustment, and other population-based use cases |
Strengths | Efficient at scale, standards-based, and well suited for workflows that require large datasets across defined populations refreshed routinely, e.g., monthly |
Considerations | Bulk FHIR tends to exchange in batches, so it’s not the best option when data updated daily or hourly is required. Not efficient when large amounts of data is needed for a specific patient as opposed to an entire population. |
The choice between transactional and bulk FHIR methods is not about which technology is better but about what data is needed and for what purpose.
Agentic Exchange: Adding Intelligence to Data Access
Agentic AI introduces a different model. An AI agent can determine what information is needed for a task, where to retrieve it, and what actions to take next. An agent can query multiple sources, reconcile information, interpret context, and initiate subsequent actions based on what it finds. It may seem that AI could eventually eliminate the need to standardize healthcare data at all. If an intelligent agent can decipher different formats and map information dynamically, why invest in standards?
The better question may be: When is it efficient to create an agent for a data exchange use case that FHIR standards solve at scale?
AI can help interpret inconsistent information. However, trusted, standardized data gives an agent clearer context and reduces the risk of misinterpretation when an AI agent is addressing an actual business or clinical problem. Therefore, FHIR and agentic AI should be viewed as complementary: FHIR provides a trusted data foundation, while agents add intelligence, orchestration, and adaptability on top of it.
Best Suited For | Dynamic workflows where the information required may change based on context, multi-step processes, intelligent data discovery and retrieval, and emerging AI-enabled healthcare workflows |
Strengths | Flexible, adaptive, and capable of navigating more complex tasks that don’t always follow a predetermined sequence |
Considerations | Data quality, identity, security, governance, accuracy, and appropriate human oversight remain critical. More autonomy doesn’t eliminate the need for trustworthy data |
It Isn’t Either/Or
The evolution toward agentic AI doesn’t mean transactional or Bulk FHIR is becoming obsolete. These three approaches work together. An AI agent could use transactional FHIR to retrieve specific information at one point in a workflow and leverage population-level data through Bulk FHIR at another. The agent provides intelligence and orchestration; interoperability infrastructure provides reliable access to high-quality data. The more sophisticated interoperability strategy isn’t to pick a winner. It’s to build a data architecture that leverages all three.
Building for What Comes Next
At Opala, we believe the future of healthcare interoperability requires flexibility. The Opala HealthSynq™ platform creates a trusted, standardized data foundation that supports transactional FHIR, population-scale Bulk FHIR, and emerging AI-driven applications.
HealthSynq™ for AI extends that foundation to intelligent applications and agents, giving organizations the flexibility to use the right approach for the right use case.
Transactional FHIR delivers targeted, real-time data. Bulk FHIR moves standardized data efficiently at scale for large populations of patients. Agentic exchange adds intelligence and adaptability.
