RESEARCH & SECONDARY USE CONNECTORS

Use Cases & Research Data Models

2 of 6

Enable governed secondary use of health data for research, real-world evidence and analytics with privacy, provenance and controlled outputs.

Clinical StudiesRWEEpidemiologyFHIROMOP CDMCDISCMSIS

Enable Discovery

Power research with high-quality governed data.

Fit for Purpose

Use the right model for each use case.

Trust & Compliance

Apply GDPR/EHDS/national governance with auditability.

Greater Impact

Support evidence, policy and improved outcomes.

Key Use Cases

Clinical Studies

Feasibility, site selection and approved study datasets.

Real-World Evidence

Effectiveness, safety and comparative outcomes.

Epidemiology & Public Health Research

Population trends, risks and disease burden.

Health Services Research

Care pathways, utilization, quality and cost-effectiveness.

Disease & Clinical Registries

Condition-specific longitudinal research datasets.

Population Analytics

Stratification, inequalities and population insight.

Policy Making & Planning

Scenario modelling and resource planning.

AI & Model Development

Governed training and validation in controlled environments.

Research Data Models

Model	Best fit	Examples
FHIR R4/R5	Interoperable clinical data and extraction	Cohort discovery, source exchange, Bulk Data
OMOP CDM	Observational/RWE analytics	Multi-site comparative research
CDISC SDTM/ADaM	Clinical study/regulatory outputs	Study standardization and submission
Study-specific model	Approved bespoke analysis	Specialized datasets
MQL (future)	Governed healthcare-native query layer	Cross-model cohort/query orchestration

Data Requirements

Approved purpose and population/data scope

Documented source systems and freshness

Clinical terminology/value-set requirements

Temporal observation window

Required data-quality thresholds

Pseudonymization/disclosure-control requirements

Output/export restrictions

Mappings & Interoperability

Source Data

FHIR, LIS, imaging, pharmacy, devices, registries

MSIS

Terminology mapping and harmonization

Transform

FHIR ↔ OMOP/CDISC/study model under approved rules

Research Environment

Controlled analysis

Outcomes

Approved results and evidence

Examples

httpCopy

# Illustrative research request
POST /v1/research/requests
Authorization: Bearer <token>

{
  "title":"Diabetes Outcome Study",
  "purpose":"research",
  "dataTypes":["Condition","MedicationRequest","Observation"]
}

Illustrative Research API route until the public contract is frozen.

Best Practices

Choose model based on research purpose, not convenience alone

Preserve source provenance through every transformation

Use standard concepts before custom variables

Version mappings and derivations

Keep cohort definition reproducible

Separate analytical dataset from production clinical database

Developer Integration Surface

Surface	Operation / resource	Use	Status
FHIR	Resources / Bulk Data	Interoperable source/extraction	FHIR
OMOP	CDM dataset/export	Observational research	Planned/evolving
CDISC	SDTM / ADaM export	Study/regulatory data	Planned/evolving
Research API	requests / jobs / datasets	Governance orchestration	Planned/versioned
MQL	query/cohort DSL	Cross-model querying	Future/planned

Next Steps

Select use case and target data model

Define approved data requirements

Map source semantics through MSIS

Create reproducible transformation

Validate with representative data