RESEARCH & SECONDARY USE CONNECTORS
Overview
1 of 6Enable governed secondary use of health data for research, real-world evidence and analytics with privacy, provenance and controlled outputs.
Secondary UseFHIROMOPCDISCMSISMQL (future)Secure Environments
Accelerate Innovation
Support clinical research, RWE and population studies with trusted data.
Compliant by Design
Use privacy, authorization and governance from the start.
Real-World Impact
Enable evidence, policy and innovation from approved use.
Interoperable
Use FHIR, OMOP, CDISC and semantic harmonization through MSIS.
From Clinical Care to Research Insights
1
Data Sources
EHR, HIS, LIS, PACS, wearables, pharmacy, public health
2
Authorization
Purpose, legal basis, approvals and scope
3
Cohort & Data Selection
Minimized approved population/data
4
Pseudonymization
Protect identifiers and reduce re-identification risk
5
MSIS Harmonization
Normalize concepts, units and quality
6
Secure Processing
Controlled research environment
7
Results & Export
Governed aggregate/approved outputs
Primary vs Secondary Use
Primary Use — Clinical Care
Direct patient care
Individual patient benefit
Identifiable data when needed for care
Clinical authorization/consent as applicable
Operational care workflow
Secondary Use — Research / Public Interest
Research, epidemiology, policy or innovation
Separate purpose and legal basis
Minimized/pseudonymized data
Controlled environments and outputs
Additional governance and authorization
Supported Use Cases
Clinical Research
Interventional/non-interventional studies, feasibility and site selection.
Real-World Evidence
Effectiveness, safety, comparative and long-term outcomes.
Epidemiology & Public Health
Incidence, prevalence, risk factors and trends.
Health Services Research
Care pathways, resource use and quality.
AI / Model Development
Governed training/validation of approved models.
Policy & Planning
Health-system planning and scenario modelling.
Key Standards & Models
| Model / standard | Purpose | Mediloop role |
|---|---|---|
| HL7 FHIR R4/R5 | Interoperable clinical exchange and extraction | Source/operational model and Bulk Data where applicable |
| OMOP CDM | Observational research / RWE | Research transformation target |
| CDISC SDTM/ADaM | Clinical-study/regulatory data | Research/export target |
| SNOMED CT / LOINC / ICD / UCUM | Clinical semantics | Mapped/validated via MSIS |
| MQL | Healthcare-native query/orchestration DSL | Future/planned; governed, not unrestricted SQL |
Research Access Principles
Every dataset/query is tied to an approved request/purpose
Researchers receive only authorized data and tools
Bring computation to data where required instead of exporting raw data
Record dataset version, transformations and lineage
Use disclosure control before output leaves secure environment
Revoke/expire access when authorization ends
Developer Integration Surface
How developers interact with this capability
The research surface must never imply unrestricted access to Mediloop production databases. Access is purpose-bound, authorized, scoped and executed through governed APIs/environments.
| Surface | Operation / resource | Use | Status |
|---|---|---|---|
| Research API | request / cohort / job / output workflow | Secondary-use orchestration | Planned/versioned |
| FHIR Bulk Data | $export / NDJSON where authorized | Large-scale interoperable extraction | FHIR capability when implemented |
| OMOP/CDISC export | approved transformed datasets | Research/regulatory workflows | Planned/evolving |
| MQL API | governed cohort/query operations | Healthcare-native querying | Planned |
| SDKs | Python/JS/Java helpers | Research integration | Published versions only |
Next Steps
Submit/approve research purpose
Choose data model and cohort approach
Run extraction/transformation in secure environment
Validate quality/privacy
Review and approve outputs