By Kippi Bordowitz
Healthcare data is both a challenge and an opportunity. Across the world, hospitals, clinics, insurers, and research organizations are all grappling with the same problem: fragmented information spread across countless systems. At the same time, the demand for timely insights is growing. Physicians need access to complete patient histories. Public health agencies need accurate, up-to-date population data. Researchers need large-scale datasets to identify trends and accelerate medical discoveries. Meeting these diverse needs requires more than storing data. It requires structuring and transforming it into a format that works across operational and analytical contexts.
Two standards dominate this space: FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership). Each has become essential in its own domain. FHIR powers interoperability and patient-centric care by enabling data to move seamlessly between health systems. OMOP provides a common data model for research, making it possible to analyze massive datasets consistently and accurately. But as organizations invest in both, many discover the difficulty of running separate projects, each demanding resources, mapping, and governance. Outburn believes there is a better way.
What is FHIR?
Just in case this is the first time you’ve come across this: FHIR is the emerging global standard for exchanging healthcare data. It provides a consistent way to structure and share clinical information so that different systems can communicate. In Israel, as in much of the world, FHIR adoption is accelerating because it aligns with regulatory requirements and supports patient empowerment. With FHIR, a medication list from one hospital can be understood by a primary care physician, and a lab result generated in a private clinic can flow directly into a national health repository.
Yet FHIR has its limits. While it excels at interoperability, care coordination, and patient-facing applications, it was not designed as a model for large-scale research. FHIR resources capture detail, context, and real-time data flow. What they lack is the population-wide harmonization and analytical structure that researchers depend on.
What is OMOP?
OMOP is the leading common data model for observational research. It was created to standardize disparate clinical datasets so that researchers could study outcomes, compare across populations, and apply advanced analytics such as AI. By normalizing medical concepts, terminologies, and relationships into a consistent format, OMOP enables powerful population health studies, pharmacovigilance, and clinical trial acceleration.
The challenge with OMOP is that it tends to lag behind the operational flow of healthcare. Data is often extracted only once every few months, transformed into OMOP, and then analyzed. For real-world decision-making, this delay can limit its impact. Researchers may see the big picture, but they don’t see it in time to affect what is happening at the bedside.
FHIR vs. OMOP: Complementary, Not Competing
Too often, organizations treat FHIR and OMOP as separate initiatives. The IT team may implement FHIR APIs for interoperability while the research arm builds an OMOP warehouse. Each team creates its own mappings, terminology libraries, and validation processes. This results in duplicated effort, inconsistent data quality, and significant cost.
The truth is that FHIR and OMOP are not competing models. They are complementary. FHIR ensures information flows smoothly between providers, patients, and regulators. OMOP ensures that the same information can be analyzed to drive discovery and policy. The real opportunity lies in bridging them.
Why Bridging Matters
Imagine a scenario where a hospital detects early signs of adverse reactions to a new therapy. With traditional OMOP pipelines, it could take months for this data to be aggregated, harmonized, and studied. By then, the signal may be diluted or missed. By contrast, if FHIR and OMOP are bridged, the hospital could generate near real-time OMOP data from the same FHIR feed used for care delivery. Researchers and clinicians could see the trend as it emerges, potentially preventing harm and adjusting treatment protocols immediately.
Or consider population health management. Ministries of health often need to track chronic disease trends across millions of patients. If every interaction generates high-quality FHIR data that is also ready for OMOP, agencies gain both real-time operational insight and the ability to run long-term studies on the same foundation. The result is faster policy response, reduced duplication of testing, and improved continuity of care.
How FUME Bridges the Gap
This is where FUME, Outburn’s specialized FHIR toolbox, comes in. Instead of forcing organizations to choose between separate FHIR and OMOP projects, FUME enables a two-stage approach:
First, FUME converts fragmented data into FHIR resources enriched with OMOP-friendly terminology. This means that the interoperability layer already contains the analytical context required later. Data is standardized once, at the point of transformation, rather than in two parallel projects.
Second, FUME makes converting FHIR into OMOP straightforward. Because the mappings and terminologies are aligned from the start, the translation is smooth, and OMOP datasets can be generated much closer to real time. Instead of waiting for quarterly extracts, organizations can maintain research-ready datasets updated daily or even continuously.
The impact is significant. Data quality improves across both standards. Duplication of effort is reduced. And organizations finally achieve a unified data foundation that supports both operational care and analytical research without compromise.
The Outburn Approach
At Outburn, we have seen firsthand how difficult it can be to deliver true interoperability while also supporting research. Our team has decades of experience working with Israeli health organizations and implementing FHIR-based projects. We know that without a strategic approach, projects risk becoming silos, wasting investment and delaying results.
We builte FUME to prevent this. By embedding OMOP considerations into FHIR workflows, it ensures that data serves every stakeholder: patients who want access, providers who need context, regulators who demand compliance, and researchers who depend on accuracy and scale. Bridging FHIR and OMOP is not just a technical exercise. It is the foundation for a healthcare system that learns continuously, adapts quickly, and delivers better outcomes.
Conclusion
The future of healthcare data is not about choosing FHIR or OMOP. It is about enabling both, from the same trusted foundation. By unifying interoperability and analytics, healthcare organizations can move beyond fragmented initiatives and toward a vision of seamless, patient-centered, and data-driven care.
FUME makes this possible today. With its structured approach, advanced transformation tools, and built-in terminology alignment, FUME empowers organizations to bridge FHIR and OMOP efficiently and effectively. For health systems, ministries, and researchers alike, this is the path to actionable insights, faster discovery, and improved patient outcomes.
