The World Health Organization considers health information systems one of six essential building blocks of any functioning health system, alongside service delivery, financing, workforce, governance, and medical products. A health information system collects data from the health sector and other relevant sectors, ensures data quality and timeliness, and converts raw numbers into information that drives health-related decisions. The other five building blocks all depend on it: you can’t fund what you can’t measure, train the right workers without workforce data, or deliver services without knowing where gaps exist.
Four Core Functions
WHO defines four key functions that any health information system must perform: data generation, compilation, analysis and synthesis, and communication and use. Data generation happens at the point of care, in population surveys, and through civil registration systems that record births and deaths. Compilation brings those scattered data points together into usable datasets. Analysis and synthesis turn the compiled data into meaningful patterns, like disease trends or coverage rates. Communication and use is where the information actually reaches the people making decisions, from local clinic managers to national policymakers.
This isn’t just a theoretical framework. Countries that have weak links in any of these four functions end up making policy in the dark. A country might generate enormous amounts of data at health facilities but never compile it in a way that allows anyone to spot a disease outbreak or a vaccine coverage gap.
The SCORE Framework
To help countries strengthen their health data, WHO developed a technical package called SCORE. Each letter represents a specific intervention:
- Survey populations and health risks
- Count births, deaths, and causes of death
- Optimize health service data
- Review progress and performance
- Enable data use for policy and action
The “Count” component addresses one of the most fundamental gaps in many low- and middle-income countries: millions of births and deaths go unregistered each year, which means governments literally don’t know how many people they serve or what’s killing them. “Optimize” focuses on routine health service data, the kind generated every day in clinics and hospitals, which is often incomplete or inconsistent. “Enable” targets the final step where many systems fail: making sure the data actually reaches decision-makers in a format they can act on.
Routine Health Information Systems
Most of the data flowing through a national health information system comes from routine reporting: the numbers that clinics, hospitals, and community health workers submit on a regular schedule. WHO evaluates the quality of these routine health information systems using three core criteria: completeness (are all facilities reporting?), timeliness (are reports arriving when they should?), and consistency (do the numbers make sense when compared across time periods and locations?).
A platform called DHIS2 has become the dominant tool for managing this routine data. WHO has provided technical support for DHIS2 implementation in countries across multiple regions. In São Tomé and Príncipe, for example, the government adopted DHIS2 as its sole health information platform for policymaking and planning. The system links health service data with social registries to monitor access for vulnerable populations. During the COVID-19 pandemic, the country used DHIS2 for daily case reporting and tracking of vaccinations and adverse effects. Development partners also rely on DHIS2 data to identify priority investments and monitor their results.
Data Standards and Classification
For health data to be useful across borders, countries need a shared language for classifying diseases, injuries, and causes of death. WHO’s International Classification of Diseases serves that purpose, and its latest version, ICD-11, is being implemented across more than 120 countries as of 2024. WHO has rolled out extensive implementation resources, including tutorial videos and comprehensive integration guides, to support the transition.
Standardized classification matters because it allows meaningful comparison. When two countries both report a rise in diabetes-related deaths, standardized coding ensures they’re actually talking about the same thing. Without it, global health monitoring breaks down.
Data Governance and Privacy
Health data is inherently sensitive, and WHO has established principles for how it should be handled. The first principle treats data as a public good: WHO pushes for data to be open and accessible unless there’s a legitimate reason to restrict it. For personal data, individual consent is the preferred basis for processing. However, for routine public health surveillance, informed consent is not always required, a practical acknowledgment that disease tracking systems would collapse if every data point needed explicit permission.
The second relevant principle focuses on protecting trust. WHO requires that any data shared with it has been collected in accordance with national laws, including data protection laws aimed at safeguarding identifiable individuals. The organization commits to upholding the right to privacy for both personal data and aggregated data about groups of individuals. This balancing act, between maximum transparency and individual protection, runs through every aspect of health information system design.
How Data Drives Policy
WHO created a dedicated division, Data, Analytics and Delivery for Impact (DDI), specifically to connect health information systems to real-world outcomes. The division works toward three objectives: improving measurement by strengthening country data systems and governance, focusing on results through dynamic analytics that inform public health performance, and delivering impact by using data-driven reviews to accelerate progress toward the Sustainable Development Goals.
In practice, this means WHO conducts routine “delivery stocktakes” where countries review their own data, identify barriers to progress, and adjust course. The goal is that every country has access to timely, reliable, and disaggregated data to drive equitable policy. Disaggregation is key here: national averages can hide the fact that certain regions, age groups, or income levels are being left behind. A health information system that only reports country-level numbers may look adequate on paper while masking severe inequities.
The DDI division also works to ensure that data standards and governance are consistent enough across countries to enable global tracking of health targets. WHO’s Triple Billion initiative, which aims for one billion more people with universal health coverage, one billion more protected from health emergencies, and one billion more enjoying better health, depends entirely on the ability to measure progress. Without functioning health information systems at the country level, those targets are just aspirations.