Data Harmonization, Integration & Analytics

We assess existing databases, information systems and reporting workflows to establish their readiness for integration, research and analytics. Activities include data inventories, source-system profiling, metadata review, identification of quality gaps, and development of implementation roadmaps with agreed priorities, resource requirements and deliverables. Contact us if you need to screen your set up and draw gaps.

We standardise heterogeneous datasets using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and other data structures suited to the intended use. Drawing on the Observational Health Data Sciences and Informatics (OHDSI) ecosystem, we develop source-to-target mappings, align clinical concepts, reconcile units and dates, and document transformation decisions. Terminology mapping includes clinical review of local codes and their relationships to established vocabularies and classifications.

We design and maintain extract, transform and load (ETL) pipelines that connect electronic medical records, laboratory systems, registries, surveys and programme databases. Sources may include OpenMRS, OpenClinic GA and other health information systems. Services include automated updates, interface development, migration-data validation and authorised record linkage. Where appropriate, we implement interfaces using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR).

We evaluate completeness, consistency, plausibility, duplication and conformity with agreed data specifications. Using tools such as the OHDSI Data Quality Dashboard and Achilles, alongside clinical review and source-data reconciliation, we identify problems and support corrective action. Deliverables include quality reports, monitoring dashboards and documented limitations relevant to each intended analytical use.

We support study planning, cohort definition, statistical analysis and interpretation of clinical and population-health data. Applications include disease surveillance, healthcare utilisation, treatment outcomes, programme evaluation and observational research. We develop reproducible analytical workflows, interactive dashboards and scientific reports, with clearly defined indicators, denominators and uncertainty.

We develop and evaluate machine-learning models for defined research and health-system questions, including risk prediction, forecasting and anomaly detection. Services cover dataset preparation, feature development, model comparison, calibration, validation across populations and assessment of performance differences between groups. Where models enter operational use, we support monitoring and periodic reassessment.

We help institutions establish analytical networks in which participating organisations retain control of their local datasets. Services include configuring analytical nodes, coordinating distributed studies and implementing federated learning across participating institutions. Approved analyses operate under agreed access controls, disclosure checks and governance arrangements, with review of both analytical results and model updates before sharing.

We support data dictionaries, catalogues, provenance records, access procedures and appropriate de-identification workflows. Technical services include deployment and maintenance of analytical databases and OHDSI environments, including Broadsea, ATLAS and WebAPI where appropriate. We maintain pipelines, update vocabularies and mappings, monitor data refreshes, troubleshoot failures and document changes in collaboration with institutional data owners and infrastructure teams.

We provide practical training in OMOP CDM, terminology mapping, ETL development, data quality, analytics and federated research. Training can combine workshops, supervised implementation and continuing mentorship. Handover includes agreed source code, configuration documentation, operating procedures and maintenance guidance, helping partner institutions sustain and extend their own capabilities.