Linking health information systems
Routine data from DHIS2 instances, laboratory systems and logistics systems are mapped to shared facility identifiers and to terminologies such as ICD-11, with every transformation logged.
The Global Centre for Risk and Innovation (GCRI)
Data engineering, interoperability and stewardship decide whether health evidence can be combined at all. In this field they cover the facility identifiers, terminologies, exchange standards, provenance and access controls that let surveillance, service, supply and environmental data be linked lawfully, reproducibly and without exposing individuals.
GCRI · Established · service level R0
Health data interoperability is the ability to combine health data from different systems with the same meaning, lawfully and reproducibly. It rests on facility identifiers, terminologies, exchange standards such as HL7 FHIR, provenance and access controls, so surveillance, service, supply and environmental data can be linked without exposing individuals.
Data engineering, interoperability and stewardship cover the pipelines, standards and governance that make data usable and trustworthy. They determine whether information from different sectors can be combined for joint risk assessment. In health and biosecurity, sound data engineering allows hazard, exposure and financial data to be linked consistently. It supports transparent and repeatable analysis of interconnected risk. The canonical description is on the STEM Commons.
Routine data from DHIS2 instances, laboratory systems and logistics systems are mapped to shared facility identifiers and to terminologies such as ICD-11, with every transformation logged.
Indicators are aggregated, small cells suppressed and disclosure risk assessed before release, so evidence can be shared without identifying patients or small communities.
Each dataset carries metadata on source, licence, collection method and evidence label, following the FAIR principles, so every value can be traced back to its origin.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| HL7 FHIR | A standard for exchanging health information as defined resources over web interfaces, maintained by HL7 International. |
| Data provenance | A record of where data came from and how it was changed on its way to an analysis. |
| Statistical disclosure control | Methods that reduce the risk of identifying individuals in published data, such as suppressing small counts. |
How far each division sits from a large city, with the roads and airports that carry patients and supplies, shown for Southern Africa as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
Datasets and records used by data engineering, interoperability and stewardship on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| National master facility lists | Shared identifiers for linkage | OFFICIAL |
| DHIS2 metadata and data values | Routine reporting inputs | REPORTED |
| WHO Global Health Observatory API | Reference indicator definitions and published values | OFFICIAL |
| HL7 FHIR resources from source systems | Standardised exchange of records | REPORTED |
Pipelines are tested with automated quality checks, schema validation and lineage audits. Reconciliation against source systems confirms completeness.
| Check | How it is done |
|---|---|
| Reconciliation against source | Totals after transformation are compared with source systems and every difference explained |
| Schema and terminology validation | Records are validated against agreed schemas and code lists before loading |
| Disclosure review | Published outputs are tested for re-identification risk before release |
| Licence and consent audit | Each dataset's licence and permitted uses are checked before linkage |
Incompatible standards, missing metadata and restrictive licences block integration. Legacy systems and undocumented transformations introduce silent errors.
Data and Compute stewards data engineering, interoperability and stewardship for the Consortium and reviews how it is applied across platforms.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.