Africa faces a dual health challenge with high infectious disease prevalence and a rapidly increasing burden of non-communicable diseases1,2. This significant disease burden is likely to worsen due to climate change, particularly through the emergence of climate-sensitive infectious diseases3.

A beacon of hope emerges through Artificial Intelligence (AI) and data analytics technologies. Imagine AI-powered chatbots in remote villages offering basic health consultations in local languages or scanning vast medical databases to identify patterns for early and precise disease detection. These technologies could transform African healthcare through improved diagnostics, precision medicine, better public health systems, advanced drug discovery, patient monitoring, and remote consultation. The consequences of Africa not adopting these tools could perpetuate the already deplorable health systems, exacerbating negative socio-economic effects and widening existing health inequities4.

Despite numerous health data sources and the increasing accessibility of low-cost data collection technologies, the continent still grapples with limited health data availability and poor data quality, creating a situation often referred to as “health data poverty"4. Although African health institutions have been collecting data for decades, most clinical data remains undigitized and siloed within institutions generating it5. This is due to a lack of comprehensive, standardized data collection processes. Many existing systems are program-specific and frequently updated to suit evolving needs. This situation presents a critical opportunity to establish proper data standards supporting the transition to widely adopted digital tools. AI and data analysis tools produce robust and reliable inferences when provided with high-quality data. In contrast, low-quality datasets compromise analytical validity—the “garbage in, garbage out" principle6. Critically, poor datasets can lead to misleading conclusions, potentially causing adverse outcomes. Furthermore, poor digitization severely impacts routine healthcare operations, planning, and evaluation in an increasingly digital world7. Figure 1 categorizes health data issues into four pillars. This commentary outlines how prioritizing digitization, standardization, and harmonization can address issues in the first two pillars: collection and management (Fig. 1). These technological solutions require implementation support from policymakers and healthcare institutions to strengthen data infrastructure and unlock the transformative potential of data science in African healthcare systems. Addressing these two pillars will lay the foundation for effective data sharing and governance policies and large-scale AI implementation, both beyond this article’s scope.

Fig. 1: An illustration of key issues within the African health data ecosystem, varying across countries.
figure 1

Most challenges in the first two-pillars—collection and management—can be addressed through digitization, standardization, and harmonization. An added value of these technological solutions also lays the foundation for effectively tackling issues in the remaining pillars. Addressing these challenges collectively can greatly enhance the effectiveness and adoption of data science solutions in healthcare across the continent.

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Data issues in the Africa healthcare system

Africa’s health data issues range from limited availability and poor quality, to restricted accessibility, poor usability, lack of interoperability, and weak governance. Figure 1 summarizes these health data issues into four components reflecting the data science pipeline. We propose tackling health data ecosystem issues through digitization, standardization, and harmonization.

Data digitization

Significant analytics can only occur when data is digitized, making it easier to address data collection, management, and analytics issues (Fig. 1). Digitization facilitates fast access to comprehensive health data, fostering data-driven decision support within healthcare systems and enabling providers to track population health trends and respond rapidly to outbreaks using advanced analytics.

In Africa, healthcare digitization is primarily driven by electronic health records (EHRs). DHIS2 is the leading platform, used in 51 countries for health data management8. Electronic Community Health Information Systems (eCHIS) and portable data collection platforms supporting public health have also gained widespread adoption9. Other notable tools include REDCap for biomedical research and OpenMRS for patient records. Disease-specific programs like HIV/AIDS, malaria, and tuberculosis use custom EHRs, while private hospitals and research institutions often utilize their own systems. Despite these efforts, EHR adoption across Africa remains low5, and the effectiveness of existing systems, measured by usage and data quality, is uncertain10. Inconsistent deployment due to lack of guidelines may affect healthcare facilities both within and across regions. To fully harness data science potential, Africa must pursue comprehensive digitization, supported by strategic investments in infrastructure (electricity, internet, computing equipment), maintenance costs, and capacity building. This includes developing standardized systems for data collection at the point of care. AI can assist by digitizing historical records and generating synthetic data for scenario modeling. Without digitizing health records, other processes like standardization and harmonization cannot be effectively implemented.

Data standardization

Data standardization ensures consistency in data format and structure11, enabling uniform capture of variables across records and facilitating straightforward comparisons. For example, aggregating COVID-19 reports from multiple countries requires standardized case definitions and date formats to ensure accurate trend comparisons.

Achieving health data standardization requires both procedural efforts (shared guidelines and terminologies like SNOMED-CT and LOINC) and technological solutions (Common Data Elements and Models)12,13. Standardized data is particularly critical in African biomedical research due to the continent’s linguistic and cultural diversity, ensuring reliable cross-country interpretation.

Standardization typically follows three models: (i) open source with community input (e.g., DHIS2, OpenMRS), allowing anyone to contribute; (ii) closed source with community feedback (e.g., REDCap), where developers rely on user feedback; and (iii) proprietary software with individual user feedback, common among private healthcare providers. Getting Africans actively engaged with these communities ensures the continent’s perspectives shape emerging standards. Existing EHRs and research repositories often face standardization issues; specialized software for data cleaning, such as the cleanepi package in R14, can expedite the post-collection curation process.

Data harmonization

Data harmonization integrates heterogeneous data from diverse datasets into cohesive datasets suitable for analytical studies. While standardization ensures uniform data formatting, harmonization focuses on making standardized yet heterogeneous data interoperable across platforms and regions, encompassing data linkage, fusion, and integration of multi-modal data. Harmonization consolidates large datasets, boosting statistical power in personalized medicine, disease research, and public health policy development. It ensures interoperability across diverse data sources—from genetic information to clinical records—and integrates data from various EHRs, supporting holistic health approaches like OneHealth. Without harmonization, health systems across Africa remain fragmented. For example, during the 2014–2016 Ebola outbreak, limited data sharing among affected countries hindered cross-border responses15. In South Africa, the HIV/AIDS data system isn’t yet fully integrated with other disease monitoring programs, creating barriers for integrated patient care16. Efforts to harmonize African health data utilize open-source standards like FHIR17, supported by initiatives such as RISLNET, OpenHIE, HELINA, and WHO Africa Observatory. While promising, these initiatives remain largely at pilot stages, with adoption lagging due to structural and infrastructural limitations.

What is the way forward?

Recommendations

We recommend that African ministries of health initiate or reinvigorate digital transformation of their health data systems by putting in place programs that:

  • Invest in both physical and digital infrastructure and technologies, which are increasingly the backbone of functional health data systems.

  • Establish and oversee the implementation of an agile digitization strategy, as digitization is demonstrably a core pillar in AI-powered health transformation.

  • Adopt standards that ensure compatibility across systems and alignment with international standards. This will facilitate harmonization.

  • Build human expertise and develop capacity in fields of study that will equip stakeholders with data science skills to build standardized, digitized, and interoperable health data systems. Realizing AI’s potential in healthcare depends heavily on human expertise for implementation and sustainability.

To optimize the impact of the digital transformation across the African continent, ministries of health and regional health bodies (like the Africa CDC, WHO-Afro, WAHO, etc) should lead collaborative work on programs that:

  • Foster interoperable systems enabling easy data exchange within and across regions, leveraging open standards and transparent APIs.

  • Promote collaborative and inclusive solutions among public and private sector health stakeholders.

  • Establish policy, legal, and regulatory frameworks aligned with national and regional laws, ensuring ethical data use, privacy, and security. Without effective data governance, even well-organized data systems risk becoming isolated silos, unsuitable for collaborative sharing.

In particular, regional health bodies should lead policy development that supports programs promoting interoperable systems with open standards and transparent APIs to enable seamless data exchange within and across regions.

Conclusion

The current state of health data digitization in Africa is limited, with standardization being inconsistent and harmonization nearly nonexistent. This represents an opportunity to leapfrog directly to reliable AI adoption before fragmented approaches become entrenched. By implementing recommended actions, African nations can significantly increase standardized and interoperable digital health data. This shift could unlock data science’s transformative potential, revolutionizing healthcare delivery, disease management, patient care, and public health policies continent-wide.

If Africa fails to embrace data science, the consequences will be severe. The continent risks exacerbating existing health challenges, stifling innovative research, and impairing decision-making processes, with profound economic implications—remaining trapped in persistent “health data poverty.”