Health work depends on information moving clearly and securely between people, facilities, partners, systems, and decisions. We can engineer those critical systems.
Surveillance systems for programmes and public health teams that need to detect changes early: outbreaks, unusual case patterns, missed follow-up, late reporting, stock-related risks, or places where services are starting to fall behind. This can include case reporting, event tracking, alerts, dashboards, data quality checks, and links to laboratory, facility, community, or national reporting data.
Electronic medical record systems and the workflows around them: patient records, clinical forms, visit histories, service data, appointment flows, reporting outputs, and data quality checks. We can support EMR design, implementation, extension, integration, or reporting layers, including the link between care delivery and programme reporting.
Health data integration across systems that need to work together: EMRs, registries, laboratory systems, reporting platforms, research databases, mobile tools, partner systems, data warehouses, and enterprise architectures. This work can include APIs, data flows, interoperability layers, health information exchange, controlled access, and reporting-ready datasets.
Health research often needs structured systems around study data, cohorts, sites, records, follow-up, and analysis. We can support research databases, cohort tracking, data extraction, cleaning workflows, study dashboards, analysis-ready datasets, and reporting tools for research teams working across facilities, programmes, or populations.
Programme data, reporting, and follow-up in one place. We build systems that support indicator tracking, site submissions, supervision visits, implementation reviews, donor reporting, cohort monitoring, and multi-site programme management, with views that help teams see where programmes are off track, where patients may be at risk, and where follow-up is needed.
AI in the practical parts of health data work: summarising records, reviewing free-text notes, classifying incoming information, searching guidelines or programme documents, supporting data quality review, extracting information from documents, or helping teams find the right information faster. The AI layer works best when it is built into the workflow around the data.