Global Health Data Standards Take Center Stage in Pandemic Intelligence Push

A new meeting report published in BMC Proceedings argues that the world’s ability to detect and respond to the next pandemic depends heavily on something far less visible than vaccines or genome sequencers: shared data standards. The report, summarizing a session of the World Health Organization’s Pandemic and Epidemic Intelligence Forum co-hosted with the WHO Department of Surveillance Systems, examines how standardized frameworks enable seamless data exchange, real-time information sharing, and better global coordination during public health emergencies. Its authors, drawn from institutions including the Berlin Institute of Health at University Hospital Charité, the PATH Program’s Digital Square initiative, the Africa Population and Health Research Center, and the Swiss Tropical and Public Health Institute, conclude that the COVID-19 pandemic gave unprecedented urgency to interoperability problems that health systems had already identified long before 2020.

The core argument is technical as much as political. When health data moves between hospitals, laboratories, ministries, and research networks, it must retain its correct meaning — its semantics — while also conforming to consistent rules of expression, or syntax, and remaining retrievable when needed. The report highlights a small set of established standards that together provide this foundation. SNOMED CT offers a validated way to represent clinical information, LOINC facilitates the exchange of clinical results, and the WHO’s International Classification of Diseases ensures semantic consistency across countries. On top of these terminologies sits HL7 FHIR, the Fast Healthcare Interoperability Resources specification, which handles data exchange syntax and in turn builds on general web formats such as JSON and XML. Because these standards are layered and modular, the report argues, they can be adapted to specific needs such as disease surveillance or clinical data exchange without each system having to invent its own custom solution.

The pandemic made the cost of missing standards concrete. According to the report, COVID-19 exposed substantial deficiencies in early detection, response coordination, and resource allocation, largely attributable to the lack of standardized data and interoperable systems both within and across countries. The authors frame the lesson not as a call for new technology but as a demand for full implementation of what already exists, combined with continued strengthening of those frameworks for future emergencies. Standardization, they write, underpins accurate assessment of population health needs, quality data for individualized care across the care continuum, and the evaluation and improvement of health services, while technical platforms that adhere to established protocols can help overcome political and logistical barriers to data sharing.

Several international initiatives illustrate what implementation looks like in practice. The Joint Initiative Council for Global Health Informatics Standardization, formed by multiple Standards Development Organizations, has collaborated with WHO since 2017 to advance global health data standards, and is a member of the Global Digital Health Partnership. One of its flagship projects is the International Patient Summary, which defines a core dataset and format for patient summaries supporting continuity of care and unplanned cross-border healthcare. The report stresses that the IPS is not meant to replace full electronic health records; rather, it serves as a complementary tool for settings where complete record access is limited. Adoption is growing among low- and middle-income countries, with organizations such as Africa CDC recommending its use where appropriate. Separately, the Global Alliance for Genomics and Health is developing standards for collecting, storing, analyzing, and sharing genomic data — an effort its participants describe as building an “internet of genomics.”

National and regional examples show the standards being put to work. In Europe, FHIR is being promoted to support the European Health Data Space, enabling data exchange while maintaining regulatory compliance. Kenya’s Ministry of Health has integrated FHIR into its national digital health strategy to ensure interoperability across systems, which the report cites as evidence of high-level commitment to standards and enterprise architecture. In Germany, the Berlin Institute of Health at University Hospital Charité applies FHIR, SNOMED CT, LOINC, and ICD-11 to keep health data structured and interoperable across systems. Charité also developed the German Corona Consensus Dataset, or GECCO, during the pandemic — a standardized COVID-19 research dataset defined by a multidisciplinary expert board using international standards and terminologies. That experience fed into the microbiology module of Germany’s Medical Informatics Initiative, a nationwide project linking all German university hospitals with research institutions, industry, insurers, and patient groups through a network of data integration centres built on existing standards and a core dataset based on the IPS specification.

Germany’s ecosystem extends further. The country contributed the “phenopackets” specification, a schema for exchanging computable, longitudinal, case-level phenotypic information used in diagnosing and researching diseases ranging from complex genetic conditions and cancer to infectious diseases, with laboratory, imaging, and physiological findings included. The NFDI4HEALTH initiative has built a comprehensive inventory of German epidemiological, public health, and clinical trial data, with a centralized catalogue, advanced search, data access management, and an analysis toolbox, all under stringent privacy requirements. Portals such as the German Portal for Medical Research Data allow scientists to query data availability and request usage, with approvals reviewed by access committees at each participating university hospital — a governance model the report presents alongside the technical infrastructure as equally essential.

In Africa, the Kenya-based Africa Population and Health Research Center, with a regional office in Senegal, is working to harmonize healthcare data across the continent through its data science program, which builds platforms and architectures for data ecosystems, harmonizes datasets, and trains researchers in data management while exploring big data analytics, machine learning, and generative AI in healthcare. A key initiative is the INSPIRE Network — the Implementation Network for Sharing Population Health Information from Research Entities — which uses demographic and health surveillance sites across Africa to collect and analyze health data, applying FAIR principles to make data findable, accessible, interoperable, and reusable. INSPIRE integrates Ministry of Health data, disease registries, and research projects into a common data model using SNOMED CT, LOINC, and ICD-11. During the pandemic, APHRC also ran the PEACH Project, creating an integrated platform for harmonized COVID-19 data from Kenya, Uganda, and Malawi using the OMOP Common Data Model, while navigating diverse data protection frameworks and advocating federated approaches adapted to the African context.

The report is candid about the obstacles. Standards must be adaptable because healthcare processes and data requirements vary widely between and within countries, and low- and middle-income countries often face resource constraints that make adoption costly, with steep learning curves. Building bespoke solutions may serve short-term goals but undermines long-term interoperability and data consistency. The authors also draw a sharp technical distinction between integration and interoperability: integration creates direct point-to-point connections between systems that are complex and costly to maintain, since every update requires adjusting each connection, whereas interoperability uses standards and middleware to move data without direct connections, reducing complexity and cost at national, regional, and global levels. Achieving it spans technical, syntactic, semantic, and organizational dimensions, currently organized around the ISO Open Systems Interconnection model, whose application-level layer is where HL7 operates.

Operational support comes from initiatives designed for exactly these constraints. PATH’s Digital Square matches supply and demand for digital public goods and health solutions, promotes open-source tools through its Global Goods Guidebook, and applies the Principles for Digital Development to avoid fragmented pilot projects. The WHO’s SMART Guidelines, developed with collaboration from PATH and the Swiss Tropical and Public Health Institute, provide a comprehensive framework standardizing both semantic and syntactic aspects of healthcare data, defining essential data elements and standardized activities such as patient registration. The report notes that transforming clinical guidance into digital components is complex, lengthy, and error-prone, and that fully detailed adaptation materials remain limited despite WHO support for national adaptations. On the clinical side, the authors emphasize a “collect once, use many” approach: quality surveillance data originates in clinical interactions, yet in resource-constrained settings often only minimal information is recorded, and standardized clinical decision support systems could improve both guideline adherence and the completeness and comparability of data reused for surveillance.

Looking forward, the report identifies artificial intelligence and machine learning integrated into clinical decision support as a promising avenue for public health surveillance, potentially supporting earlier outbreak detection and timely interventions by leveraging large datasets and real-time inputs. It also highlights ICD-11’s enhanced digital compatibility, expanded classification, and greater granularity as well positioned to address the interoperability challenges discussed. The overarching conclusion is that resilient pandemic intelligence requires implementing global data standards and interoperable systems, leveraging frameworks such as the FAIR principles and the International Patient Summary, while confronting persistent challenges of scalability, privacy, and security — particularly in low- and middle-income countries where digital health infrastructure is still developing. Standardized roles, from implementers and clinical authors to architects and project managers, and sustained capacity building, including APHRC’s push to incorporate Africa-specific data concepts into international vocabularies across a continent with more than 2,000 languages, are presented as prerequisites for turning standards on paper into data that actually moves.

Subject of Research: Data standards and interoperability for global pandemic and epidemic intelligence

Article Title: Data standards and interoperability for health

Article References: Tornimbene, B., Leiva Rioja, Z. B., Beynon, F., Costa Teixeira, J., Kiragga, A., Krause, G., Ochola, M., Thun, S., Veltsos, P., & Morgan, O. (2026). Data standards and interoperability for health. BMC Proceedings, 19(S4), Article 39. https://doi.org/10.1186/s12919-026-00396-y

Image Credits: AI Generated

DOI: 10.1186/s12919-026-00396-y

Keywords: data standards, interoperability, pandemic intelligence, HL7 FHIR, SNOMED CT, LOINC, ICD-11, FAIR principles, International Patient Summary, digital health, WHO, global health