Clinical trials remain essential to medical evidence generation. They are critical for establishing whether new interventions are safe and effective under controlled conditions. But for African health systems and life sciences strategy, trial data alone is not enough. If countries want evidence that supports clinical decision-making, service design, policy, and innovation in routine care, they also need stronger systems for generating, connecting, and using health data outside trial settings.
If Africa is to build resilient health systems and a competitive life sciences ecosystem, it must move beyond a trial-centric approach and invest in how everyday health data is generated, connected, and used. This includes real-world data (RWD) (1): data generated during routine care and other non-interventional settings, outside the tightly controlled conditions of a clinical trial.
Real-World Data and the Limits of Trial-Centric Evidence
Clinical trials are designed to answer specific questions under highly controlled conditions. Participants are carefully selected, protocols are standardized, and timelines are limited. While this rigor is necessary, it also means that trial findings do not always reflect what happens in real-world settings. Questions around continuity of care, multimorbidity, referral pathways, adherence under routine conditions, and variation in service delivery often sit outside what trials are designed to capture. Patients in routine care often present with multiple conditions, face barriers to access, and experience variations in care delivery that are not captured in trial environments.
This gap can be especially important in African health systems, where trial activity may be concentrated in selected sites (2), while routine care is delivered across far more varied clinical and operational settings. As a result, trial data can tell us what is possible under ideal conditions, but not necessarily what is happening across the broader health system.
For life sciences strategy, this distinction matters. Trial data can support product development and regulatory evidence generation, but routine health data is often what reveals how interventions are actually used, where patients are lost along the care pathway, how outcomes vary across facilities, and where system bottlenecks limit impact (3). Without that layer of evidence, policy and innovation decisions may be based on an incomplete view of performance.
Real-world data helps close this gap by showing how disease is diagnosed, treated, and followed up in everyday practice. It can help identify differences between protocol-based care and routine delivery, reveal longer-term outcomes, and support analysis of safety, effectiveness, and service performance in settings that trials do not fully represent. Regulatory bodies such as the U.S. Food and Drug Administration now recognize that real-world data and real-world evidence can play a useful role when applied appropriately and with sufficient data quality (1).
Registries as Strategic Evidence Infrastructure
One practical way to build usable real-world data systems is through well-designed patient registries.
Patient registries can generate longitudinal, condition-specific data that is difficult to capture through one-off studies alone. When they are well designed, they can support clinical decision-making, service planning, surveillance, and research by showing how patients move through care over time and how outcomes vary across sites and populations (4). Their value is not just technical. Registries can help turn fragmented clinical encounters into a more continuous evidence base.
WHO’s Regional Office for Africa describes health information systems as an anchor pillar of a functional health system because they support data generation, analysis, and evidence-based decision-making across levels of care (5).
Despite their potential, registries remain underdeveloped in many African countries. Where they exist, they may be limited by fragmentation, weak interoperability, inconsistent funding, variable data quality, and limited linkage to routine clinical workflows (6).
Bridging Care and Research
A related challenge is the persistent separation between healthcare delivery and research. In many settings, data collected during routine care is not structured in ways that make it easy to reuse for research, while research programmes often operate through parallel data systems that do not strengthen day-to-day clinical workflows. This weakens both sides: routine care loses analytical value, and research loses connection to the realities of service delivery.
Better integration does not mean turning every clinical encounter into a research exercise. It means designing systems so that high-quality routine data can inform service improvement, observational analysis, registries, and selected research use cases without duplicating effort. When done well, this reduces fragmentation and makes evidence generation more sustainable.
Integrating care and research can make health systems more responsive when data generated during routine care is analyzed, interpreted, and fed back into practice. This is the logic behind learning health systems: systems that use data and experience continuously to improve care. A systematic review found that learning health system models built on electronic medical records, linked data, registries, and feedback mechanisms can generate measurable improvements in care, service performance, and research translation (7).
Digital Infrastructure, Artificial Intelligence, and Data Quality
As interest in artificial intelligence (AI) and digital health grows across Africa, it is important not to mistake downstream tools for upstream readiness. Advanced analytics depend on the availability of high-quality, representative, well-governed data. If the underlying data is fragmented, incomplete, or poorly linked across systems, the outputs of digital tools may be less useful than they appear. This matters not only for technical performance, but also for whether digital tools reflect the populations, care pathways, and service realities they are meant to support.
This is why investment in electronic health records, interoperability, data standards, and governance matters. These are not just technical upgrades. They are the infrastructure that allows health systems to connect data across services, reduce duplication, improve continuity, and generate evidence that is useful for care, planning, and research. In Africa, interoperability remains a major challenge across digital health systems, which limits the value of otherwise promising data assets (5).
The African Union’s Digital Transformation Strategy places digital infrastructure and data systems at the centre of broader development and innovation efforts, creating an important policy backdrop for health-sector digital transformation as well (8).
What This Means for Africa’s Life Sciences Strategy
For Africa’s life sciences ecosystem, this moment presents an opportunity to rethink strategy. Clinical trials will continue to play an important role, but they should not be the sole focus. A more balanced approach would include strengthening real-world data systems, expanding the use of registries, and embedding research within routine care.
For life sciences specifically, stronger health systems data can support far more than service delivery. It can improve epidemiological understanding, support post-market follow-up, strengthen disease surveillance, inform registry-based research, and provide a more credible foundation for digital and analytical innovation. In that sense, routine data systems are not separate from life sciences strategy. They are part of it.
Such a shift would strengthen African countries’ ability to generate evidence that is grounded in their own populations, care pathways, and health-system realities.
The goal is not to replace clinical trials, but to complement them with a broader evidence base. By strengthening health systems data, expanding registries, and integrating care with research more effectively, African countries can build evidence systems that are more useful for policy, service improvement, innovation, and long-term resilience. Trial data remains essential. But on its own, it cannot answer every question that health systems need to solve.
For organizations working across research coordination and health-system strengthening, this is an important strategic direction: building data systems in which routine care, research, and evidence use are more closely connected.
References
- Commissioner O of the. FDA [Internet]. FDA; 2026 [cited 2026 Mar 25]. Real-World Evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
- Clinical Research & Trials Community (CRTC) Programme | Science for Africa Foundation [Internet]. [cited 2026 Mar 25]. Available from: https://scienceforafrica.foundation/programmes/clinical-research-and-trials-community
- Blonde L, Khunti K, Harris SB, Meizinger C, Skolnik NS. Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician. Adv Ther. 2018;35(11):1763–74. doi:10.1007/s12325-018-0805-y PubMed PMID: 30357570; PubMed Central PMCID: PMC6223979.
- Gliklich RE, Dreyer NA, Leavy MB. Patient Registries. In: Registries for Evaluating Patient Outcomes: A User’s Guide [Internet]. 3rd edition [Internet]. Agency for Healthcare Research and Quality (US); 2014 [cited 2026 Mar 25]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK208643/
- Health Information System | WHO | Regional Office for Africa [Internet]. 2026 [cited 2026 Mar 25]. Available from: https://www.afro.who.int/health-topics/health-information-system
- Musa SM, Haruna UA, Manirambona E, Eshun G, Ahmad DM, Dada DA, et al. Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice. Public Health Rev. 2023 Aug 29;44:1605821. doi:10.3389/phrs.2023.1605821 PubMed PMID: 37705873; PubMed Central PMCID: PMC10495562.
- Casey JD, Courtright KR, Rice TW, Semler MW. What Can a Learning Healthcare System teach us about Improving Outcomes? Curr Opin Crit Care. 2021 Oct 1;27(5):527–36. doi:10.1097/MCC.0000000000000857 PubMed PMID: 34232148; PubMed Central PMCID: PMC8744083.
- au.int/sites/default/files/documents/38507-doc-dts-english.pdf [Internet]. [cited 2026 Mar 23]. Available from: https://au.int/sites/default/files/documents/38507-doc-dts-english.pdf