Building Stronger Health Systems with Data
by Kwok Kin LeeMapbox Ookla Health
What determines whether health services reach those who need them most? It is not only the presence of hospitals, clinics, or healthcare workers. It also depends on data and how that data is analyzed and translated into insights that guide action. For instance, data informs where healthcare facilities should be located, how services are organized, and how resources are distributed. It also helps identify underserved communities, measure gaps in access, and ensure that investments are directed where they can have the greatest impact. Therefore, data plays a key role in shaping how health systems function and how effectively they respond to population needs.
In celebration of World Health Day on April 7, explore how teams from the World Bank collaborated with Mapbox and Ookla®, through the Development Data Partnership, to leverage different types of data to strengthen access to healthcare services in low-income countries.
Evaluating Digital Infrastructure for E-Health
Expanding equitable access to healthcare increasingly depends on the availability and quality of digital infrastructure. The COVID-19 pandemic and other disruptions highlighted how difficult it can be for people to access health facilities in-person, strengthening the case for e-health services as a viable alternative. Yet e-health can only work when connectivity is reliable and available.
With support from the Development Data Partnership, the World Bank’s Global Unit for Disaster and Climate Risk Management evaluated the quality of digital infrastructure needed for effective e-health services. For this study, the team utilized different data, including Ookla Speedtest Intelligence® data, which focused on download speed, upload speed, and latency.
The analysis found that not all populated areas have connectivity, although in some cases data is unavailable. It also showed that internet performance can fluctuate over time. Across nearly all countries studied, including Peru and South Africa, the infrastructure generally did not support the most advanced telemedicine services. Video calls tended to be limited to urban areas, although simpler services such as messaging or basic electronic communication had coverage approaching 100%.
By using the Ookla data as a realistic proxy for digital infrastructure quality, the team could evaluate not only where connectivity exists, but where it is stable enough over time to enable services such as real-time teleconsultations, remote diagnostics, and health information exchange. The team’s work also identified which services could be delivered to the population and quantified the number of people in areas with sufficient connectivity.
These insights can help inform policy decisions by showing where digital infrastructure improvements are most needed to make e-health more equitable. Learn more about this project here.
Data-Driven Health Planning in the Philippines
Health service delivery in the Philippines is highly fragmented across different levels of local administration. Since local government units manage health systems autonomously, these systems have been poorly coordinated, leading to overcrowded hospitals and clinics in some locations and varying health outcomes across regions. To support reforms aimed at more integrated service delivery, the government mandated province and city-wide healthcare provider networks that integrate public facilities and coordinate referrals across levels of care.
The World Bank Health team was asked to advise on the design of these networks. For the analysis, the team assessed how well people are connected to services by using the Mapbox Matrix API , facilitated through the Development Data Partnership. The API returns travel-time estimates between origin and destination pairs across different transport modes such as driving in traffic or walking. This allowed the team to calculate travel times from every 1 km grid cell to the nearest clinic or hospital, producing more realistic access estimates than straight-line distances.
The analysis also classified facilities according to different service levels: tier 3 (barangay health stations), tier 2 (rural health units, municipal health offices, birthing homes), tier 1 (hospitals, infirmaries). The team then used the same method of travel time analysis to determine networks between facilities from each tier. The findings were presented to health ministry staff and helped frame discussions on how to register citizens with providers. Find out more about this project here.
The above two data projects highlight a simple but powerful lesson: building stronger health systems requires data. Whether assessing digital connectivity for e-health services or measuring realistic travel times to clinics, data turns complex challenges into actionable insights. By leveraging data sources through our partners, policymakers can make more informed decisions, allocate resources more effectively, and move closer to equitable access to care. As health systems continue to evolve, data will remain essential to ensuring that no one is left behind.