Introduction to FHIR for Statistical Programmers

FHIR (Fast Healthcare Interoperability Resources) is changing how healthcare systems exchange, store, and analyze data. As digital health and data science grow, statistical programmers in clinical research will benefit from knowing FHIR.1 3

What is FHIR?

• FHIR is a modern interoperability standard developed by HL7 (Health Level Seven).4
• It provides a standard way to represent health data (clinical, administrative) and a standard API (web-based) to exchange that data.4 1
• FHIR uses familiar web standards — RESTful HTTP, JSON or XML formats — which makes it easier for engineers and data folks to work with.4 1
• At its core, data in FHIR is modeled as Resources (modular units like Patient, Observation, Medication).4

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Core FHIR Concepts

• Resources: Basic building blocks (Patient, Observation, Encounter, etc.)
• API Operations: Each resource can be created, read, updated, deleted, searched, etc. via REST.
• References / Links: Resources may refer to each other via URLs (e.g. an Observation links to a Patient).
• Profiles / Extensions: To support local needs, FHIR allows customizing resources via profiles or extensions.
• Terminologies / Code systems: FHIR often uses standard codes (e.g. LOINC, SNOMED) inside resources to ensure semantic clarity.

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Simple Examples

Here’s how a Patient resource might look in JSON:

{
  "resourceType": "Patient",
  "id": "patient-001",
  "name": [
    {
      "use": "official",
      "family": "Doe",
      "given": ["Jane"]
    }
  ],
  "gender": "female",
  "birthDate": "1990-06-21"
}

And an Observation resource (say, body temperature):

{
  "resourceType": "Observation",
  "status": "final",
  "code": {
    "coding": [
      {
        "system": "http://loinc.org",
        "code": "8310-5",
        "display": "Body temperature"
      }
    ]
  },
  "subject": {
    "reference": "Patient/patient-001"
  },
  "effectiveDateTime": "2025-10-05T09:20:00Z",
  "valueQuantity": {
    "value": 37.1,
    "unit": "°C"
  }
}

If you want all observations for that patient, you might call:

GET /Observation?subject=Patient/patient-001

And the server returns a Bundle (list) of Observation resources matching your query.

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Why a Statistical Programmer Should Learn FHIR

1. Direct, structured access to clinical data Instead of getting CSVs from IT, you can query FHIR servers to fetch only the data you need, in structured form.

2. Less data wrangling & mapping overhead With a standard schema, you reduce the amount of custom cleaning, column-mapping, and interpretation.

3. Better integration of real-world data FHIR is widely adopted in EHR systems. Knowing it allows combining trial data with live patient records.2

4. Stronger collaboration with informatics / IT teams If you understand FHIR, you can talk “the same language” with EHR vendors, engineers, etc.

5. Future relevance & job growth FHIR adoption is growing: in 2025, 71% of respondents said FHIR was actively used (in at least some use cases) in their country.6

6. Better analytics & tooling There is work around analytics over FHIR (e.g. Pathling) to support cohort selection, aggregations, etc. for researchers.7

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Notes, Limitations & Challenges (Don’t ignore)

• Not all clinical systems fully implement FHIR; many support partial subsets or older versions.
• Local extensions and profiles may deviate from “base” FHIR — you have to learn those for your domain.
• Large data volumes may require paging, batching, or bulk FHIR data methods.
• Dealing with code systems (LOINC, SNOMED) is nontrivial — interpreting codes, value sets, etc.
• Security, consent, and privacy constraints in real-world health systems are complex.

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References

1. “Fast Healthcare Interoperability Resources (FHIR)” — Wikipedia, overview of standard and core concepts (Wikipedia)
2. “The Fast Health Interoperability Resources (FHIR) Standard” — NCBI / PMC article, goals & advantages (PMC)
3. Introduction / Glossy guide from HL7 / build.fhir.org (FHIR Build)
4. HealthIT.gov: FHIR as open standard using web technologies (HealthIT.gov)
5. Transforming Healthcare Analytics with FHIR (framework article) (PMC)
6. “State of FHIR in 2025” — adoption statistics (fire.ly)
7. “Pathling: analytics on FHIR” — analytics support for FHIR in research settings (jbiomedsem.biomedcentral.com)