Challenges in Collecting Real-World Data in African Clinics: A Call for Strategic Investment and Collaboration

Abstract

The increasing global recognition of real-world data (RWD) as a critical asset for evidence-based healthcare decision-making presents a significant opportunity for African health systems. RWD, derived from routine clinical practice, offers invaluable insights into disease patterns, treatment effectiveness, and population health outcomes. However, African clinics face unique and substantial challenges in the systematic collection, management, and utilization of this data. These challenges span inadequate infrastructure, limited human resource capacity, poor data quality and standardization, complex ethical and regulatory landscapes, and insufficient funding. This white paper examines these multifaceted barriers, discusses their profound impact on healthcare delivery and research, and proposes actionable strategies for overcoming them. By fostering strategic investments, strengthening local capacities, and promoting collaborative frameworks, Africa can unlock the full potential of RWD to transform its healthcare landscape and contribute significantly to global health knowledge.

Introduction

Real-world data (RWD) refers to data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources outside of traditional clinical trials (FDA, 2025). These sources include electronic health records (EHRs), medical claims data, disease registries, patient-reported outcomes, and data from digital health technologies. The analysis of RWD generates real-world evidence (RWE), which is increasingly vital for informing regulatory decisions, optimizing clinical practice, and shaping public health policies globally (Lifebit, 2024).

In Africa, where diverse disease burdens, unique epidemiological profiles, and often resource-constrained healthcare settings prevail, RWD holds immense potential. The continent grapples with a high burden of communicable diseases like HIV/AIDS, tuberculosis, and malaria, alongside a rising prevalence of non-communicable diseases (NCDs) such as diabetes and hypertension, and persistent challenges in maternal and child health. RWD can provide context-specific insights crucial for developing tailored health interventions, improving resource allocation, monitoring disease outbreaks, and evaluating the effectiveness of health programs. Despite this promise, the continent lags behind other regions in developing robust health information systems capable of systematically collecting and leveraging RWD (Ducit Blue Solutions, 2023). This white paper delves into the primary challenges hindering effective RWD collection in African clinics, exploring their implications and outlining a pathway toward sustainable solutions.

The Promise of Real-World Data in African Healthcare

The strategic use of RWD can revolutionize healthcare in Africa by providing a more accurate and comprehensive understanding of health challenges. Unlike highly controlled randomized controlled trials (RCTs), which often involve homogenous patient populations and strict protocols, RWD reflects the complexities of real-world clinical practice, including patient adherence, co-morbidities, and variations in care delivery across diverse settings. This makes RWD particularly valuable for:

• Informed Decision-Making: RWD enables policymakers and healthcare providers to make data-driven decisions on resource allocation, intervention design, and public health strategies (Lifebit, 2024). For instance, granular RWD on disease prevalence in specific regions can inform targeted vaccine distribution campaigns, ensuring that scarce resources are directed to areas with the greatest need. Similarly, understanding the real-world effectiveness of different drug regimens can guide national drug procurement policies, leading to more cost-effective and impactful healthcare spending.

• Disease Surveillance and Outbreak Response: RWD facilitates early detection, monitoring, and response to epidemics by analyzing population demographics, travel history, and environmental conditions (PMC, 2025). During outbreaks, real-time RWD from clinics can provide crucial insights into the spread of a pathogen, identify hotspots, and track the effectiveness of containment measures. This was critically demonstrated during the COVID-19 pandemic, where data from various sources, including clinical records, helped track viral variants and inform public health interventions.

• Personalized Medicine: By analyzing extensive patient data, RWD supports the development of tailored treatment plans and understanding treatment effectiveness in diverse patient populations. This includes identifying which treatments work best for specific patient subgroups, considering genetic predispositions, lifestyle factors, and co-existing conditions prevalent in African communities.

• Health Systems Strengthening: RWD can be instrumental in identifying inefficiencies, bottlenecks, and areas for improvement within healthcare delivery systems. For example, analyzing RWD can reveal long patient waiting times, drug stockouts at specific clinics, or underutilized services, allowing health managers to implement targeted solutions to optimize workflows and enhance service delivery.

• Research and Innovation: RWD provides a rich source of data for observational studies, comparative effectiveness research, and the development of AI-driven health solutions (PMC, 2025). Researchers can leverage RWD to study the long-term effects of treatments, understand disease progression in real-world settings, and identify new avenues for therapeutic development. Furthermore, large, high-quality RWD datasets are essential for training and validating artificial intelligence and machine learning models that can assist in diagnosis, predictive analytics, and personalized care.

Challenges in RWD Collection in African Clinics

Despite the clear benefits, African clinics encounter significant barriers to effective RWD collection. These challenges are often interconnected and rooted in systemic issues, requiring a holistic approach to address them:

1. Infrastructure Limitations Many African clinics, particularly in rural and underserved areas, suffer from inadequate digital infrastructure, creating fundamental obstacles to modern data collection.

• Limited Internet Connectivity: Poor internet penetration, often characterized by slow speeds, high costs, and unreliable network access, severely hinders the adoption and effective use of digital health systems (Owhor, 2023). This means that even if a clinic has an electronic health record system, real-time data entry and transmission to a central database can be delayed or impossible, leading to data backlogs and a lack of up-to-date information for clinical decision-making or surveillance. The digital divide between urban centers, which may have better connectivity, and remote rural areas, which often lack it entirely, exacerbates health inequities.

• Unreliable Electricity Supply: Frequent and prolonged power outages are a major impediment to operating electronic devices, servers, and digital health technologies (Owhor, 2023). Clinics often rely on expensive and unreliable generators, or simply cannot operate digital systems during blackouts, leading to data loss, system downtime, and a forced return to paper-based methods, which undermines the benefits of digitization. This constant disruption makes it difficult to maintain consistent data collection workflows.

• Insufficient Hardware: A pervasive lack of essential hardware such as functional computers, tablets, printers, and reliable data storage devices further complicates the transition from paper-based to digital data collection (Ducit Blue Solutions, 2023). Even when hardware is available, it is often outdated, prone to breakdowns, and lacks the processing power or storage capacity required for modern health information systems. This forces healthcare workers to resort to manual records, which are inefficient and prone to errors.

2. Human Resource Capacity and Data Literacy A critical shortage of adequately trained healthcare professionals and data specialists, coupled with existing workload pressures, poses a significant and ongoing challenge.

• Inadequate Training: Many healthcare workers, from clinicians to administrative staff, lack the necessary foundational skills in digital literacy, data entry protocols, data management, and basic data analysis (Owhor, 2023). Training programs are often one-off events, lacking continuous support or refresher courses. This can lead to errors in data input, incomplete records, and a general lack of confidence or proficiency in using digital tools, ultimately impacting data quality and system adoption.

• Workload and Retention: High patient volumes, coupled with chronic staff shortages, often result in healthcare workers being overwhelmed. Data entry is frequently perceived as an additional, time-consuming burden rather than an integral part of patient care. This leads to rushed entries, omissions, and a prioritization of direct patient care over meticulous data recording. Furthermore, high staff turnover, driven by factors like poor working conditions or limited career progression, exacerbates the problem of maintaining a skilled workforce capable of consistent data collection. The "brain drain" of skilled health informatics professionals to other sectors or countries further depletes the local talent pool.

• Poor Data Culture: A lack of appreciation for the value of data among some healthcare professionals and managers can lead to poor commitment to data quality and utilization (Tolera et al., 2024). If staff do not understand how the data they collect contributes to better patient outcomes, resource allocation, or research, their motivation to ensure accuracy and completeness diminishes. This can manifest as a lack of daily tallying, delayed reporting, or a general disregard for data integrity.

3. Data Quality, Standardization, and Interoperability The quality and usability of collected data are frequently compromised, significantly limiting its utility for research, public health decision-making, and clinical care.

• Incomplete and Inaccurate Data: Manual data collection, inconsistent reporting practices, and the absence of standardized forms often lead to missing values, inaccuracies, and outdated information (Ducit Blue Solutions, 2023; Tolera et al., 2024). For example, patient identifiers might be missing, diagnosis codes may be inconsistent, or treatment outcomes might not be systematically recorded. This "garbage in, garbage out" scenario renders the data unreliable for analysis and decision-making.

• Fragmentation and Siloed Systems: Health data is often fragmented across different facilities, departments (e.g., laboratory, pharmacy, outpatient), and vertical health programs (e.g., HIV, TB, maternal health). This makes it challenging to integrate data for a holistic patient view or comprehensive population-level analysis (Ducit Blue Solutions, 2023). A patient's full medical history might reside in multiple disparate paper files or incompatible digital systems, preventing a complete understanding of their health journey. Only a small percentage of African countries have fully interoperable digital health ecosystems, indicating a significant hurdle (Ducit Blue Solutions, 2023).

• Lack of Standardization: Diverse and too many data entry formats, coupled with a lack of common data standards, hinder the aggregation and comparison of data across different sources and regions (Lifebit, 2024; Tolera et al., 2024). Without standardized terminologies (e.g., SNOMED CT for clinical terms, LOINC for laboratory tests) or data exchange protocols (e.g., HL7, FHIR), integrating data from different clinics or even different systems within the same clinic becomes a complex, resource-intensive, and often impossible task.

4. Ethical, Legal, and Governance Hurdles The sensitive nature of health data necessitates robust ethical and legal frameworks, which are often underdeveloped, fragmented, or inconsistently applied in many African contexts.

• Privacy and Confidentiality Concerns: Protecting patient confidentiality and data security is paramount, but inadequate data governance frameworks and technological solutions can lead to vulnerabilities (Lifebit, 2024; Journal of Law and the Biosciences, 2024). This includes concerns about unauthorized access, data breaches, and the misuse of sensitive health information. Without clear guidelines and secure systems, public trust in digital health initiatives can erode, leading to reluctance in sharing data.

• Informed Consent: Obtaining truly informed consent for data collection and sharing, especially for secondary use in research, can be complex given varying levels of literacy, diverse cultural norms around health information, and the power dynamics between healthcare providers and patients (ResearchGate, 2024). Ensuring that consent is voluntary, clearly understood, and easily revocable is a significant ethical challenge.

• Regulatory Uncertainty: The absence of clear, harmonized, and consistently enforced regulatory guidelines for RWD collection, storage, and use across different countries can create ambiguity and impede cross-border data initiatives (Lifebit, 2024; Journal of Law and the Biosciences, 2024). While some countries are developing data protection laws (e.g., South Africa's POPIA), a continent-wide, interoperable legal framework for health data is still nascent, hindering regional health initiatives that rely on data sharing.

5. Funding and Sustainability Financial constraints significantly impact the ability to invest in, implement, and maintain robust RWD collection systems, often leading to unsustainable projects.

• Low Health Expenditure: African countries generally have low per capita health expenditure compared to developed nations, limiting domestic investment in essential healthcare infrastructure and digital health technologies (Ducit Blue Solutions, 2023). This means that foundational investments in reliable power, internet, and basic IT equipment often compete with other urgent healthcare priorities like drug procurement or staffing.

• Donor Dependency: Many digital health initiatives, particularly in their early stages, rely heavily on external donor funding. While crucial for initial setup, this funding can be seasonal, project-specific, and time-limited, hindering long-term sustainability and local ownership (Owhor, 2023). Once donor funds cease, projects often collapse due to a lack of recurrent budget for maintenance, upgrades, or personnel.

• High Implementation Costs: The initial and ongoing costs associated with hardware acquisition, software licenses, system customization, extensive staff training, and continuous maintenance of digital health systems are substantial (Owhor, 2023). For resource-constrained clinics, these costs are often prohibitive without significant external support or innovative financing models.

6. Cultural and Contextual Factors Local contexts and cultural nuances can significantly influence data collection practices, the acceptance of digital health solutions, and the willingness to share sensitive health information.

• Resistance to Change: Shifting from familiar traditional paper-based systems to new digital platforms can face resistance from staff accustomed to old methods, especially if the new systems are not user-friendly or perceived as adding to their workload. This resistance can manifest as slow adoption, bypass of digital systems, or even sabotage.

• Trust and Data Sharing: Community trust in healthcare systems, data custodians, and the government is crucial for encouraging participation and accurate data provision. Concerns about data privacy, security, and potential misuse (e.g., for commercial purposes or discrimination) can lead to reluctance in sharing sensitive health information, potentially resulting in incomplete or fabricated data. Building and maintaining this trust requires transparent communication and demonstrable benefits to the community.

• Local Relevance: Digital health solutions developed outside Africa, without sufficient input from local healthcare providers and communities, may not be culturally appropriate or relevant to the specific workflows and challenges of African clinics. This can lead to poor adoption and system failure.

Impact on Healthcare and Research

These interconnected challenges collectively undermine the potential of RWD to significantly improve health outcomes in Africa. The consequences are far-reaching and impact various facets of healthcare delivery and research:

• Suboptimal Healthcare Planning: Decisions at national, regional, and local levels are often made based on incomplete, inaccurate, or outdated information, leading to inefficient resource allocation and interventions that may not address the most pressing health needs (Ducit Blue Solutions, 2023). For example, without accurate RWD on disease burden, a ministry of health might over-allocate resources to one area while neglecting another with a greater, but unrecorded, burden.

• Limited Evidence-Based Practice: The scarcity of reliable RWD severely hinders the ability to conduct robust local research, evaluate the effectiveness of interventions in context-specific settings, and develop relevant, evidence-based clinical guidelines. This perpetuates a reliance on research conducted in Western populations, whose findings may not be directly applicable or optimal for African patient populations, thereby limiting the quality and relevance of care.

• Weak Disease Surveillance: Inaccurate or delayed data impedes effective disease surveillance, making it extremely difficult to detect and respond to outbreaks promptly and effectively. This was evident in past epidemics where delays in data collection and reporting hampered rapid response efforts, leading to wider spread and increased mortality. Without robust RWD, identifying emerging health threats and tracking their trajectory becomes a formidable task.

• Reduced Accountability: Without reliable and verifiable data, it is challenging to monitor progress towards health goals, evaluate the impact of health programs, and hold stakeholders (governments, NGOs, healthcare providers) accountable for health outcomes and resource utilization. This lack of accountability can lead to stagnation and a failure to learn from past interventions.

• Exacerbated Health Disparities: The digital divide and uneven distribution of resources mean that clinics in remote or impoverished areas are less likely to benefit from RWD collection and utilization efforts. This can widen existing health disparities, as these underserved populations continue to receive care based on anecdotal evidence rather than data-driven insights, and their health needs remain invisible in national health statistics.

Strategies for Overcoming Challenges

Addressing these multifaceted challenges requires a multi-pronged, collaborative, and sustained effort involving governments, healthcare providers, technology innovators, academic institutions, and international partners. A holistic approach that integrates technological solutions with human capacity development and robust governance is essential.

1. Investing in Robust Digital Infrastructure

• Expand Internet and Electricity Access: Prioritize significant investments in reliable, affordable internet connectivity (e.g., fiber optics, satellite internet, community Wi-Fi initiatives) and sustainable energy solutions (e.g., solar power, micro-grids) for all healthcare facilities, especially those in rural and remote areas (Owhor, 2023). Public-private partnerships can play a crucial role in expanding this foundational infrastructure.

• Provide Appropriate Hardware: Ensure clinics are consistently equipped with necessary, durable, and fit-for-purpose digital devices such as rugged tablets, desktop computers, and reliable backup storage solutions. This must be accompanied by a robust maintenance and repair ecosystem to ensure longevity and functionality.

• Leverage Cloud-Based Solutions: Promote the adoption of secure, scalable, and cost-effective cloud computing for data storage and processing. This reduces the need for extensive on-site hardware and IT infrastructure, improves data accessibility from various locations, and facilitates centralized data management and analysis (Medbook, 2025).

2. Strengthening Human Resource Capacity and Training

• Comprehensive and Continuous Training Programs: Implement tailored, practical, and ongoing training programs for all healthcare workers on digital literacy, proficient data entry, effective data management, and basic data analysis. Training should be integrated into professional development pathways, responsive to evolving technological needs, and include mentorship and supportive supervision (Owhor, 2023; Tolera et al., 2024).

• Foster a Data Culture: Initiate sustained awareness campaigns, workshops, and provide tangible incentives (e.g., recognition, career advancement opportunities) to highlight the critical importance and benefits of quality data among all levels of healthcare staff and management. Emphasize how data directly contributes to improved patient outcomes and more efficient healthcare delivery.

• Develop Health Informatics Expertise: Invest significantly in advanced health informatics education and training programs within African universities and vocational institutions. This includes developing relevant curricula, supporting faculty, and fostering research to build a robust cadre of local experts in health information systems, data science, and digital health (HELINA, 2025; Moi University, n.d.).

3. Promoting Data Standardization and Interoperability

• Develop and Enforce National Data Standards: Establish and rigorously enforce national and, where appropriate, regional data standards and common data models. This ensures consistency, comparability, and quality across different health facilities, programs, and reporting levels. Adoption of international standards like HL7 FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) should be explored and adapted to local contexts.

• Interoperability Frameworks: Implement robust technical and policy frameworks that facilitate seamless and secure data exchange between disparate health information systems (Lifebit, 2024). This includes developing national health enterprise architectures and promoting the use of open-source platforms that support interoperability.

• Leverage Context-Appropriate Digital Health Solutions: Adopt and scale proven digital health platforms like electronic health records (EHRs) and hospital management systems (e.g., Medbook, mPharma) that are specifically designed for low-bandwidth environments, adaptable to local languages, and relevant to the specific workflows and challenges of African clinics (Medbook, 2025; mPharma, n.d.). Co-creation with local users is key.

4. Establishing Robust Ethical and Governance Frameworks

• Clear and Enforceable Data Governance Policies: Develop and enforce comprehensive data governance policies that clearly articulate data ownership, access rights, privacy safeguards, security protocols, and responsible data sharing mechanisms (Journal of Law and the Biosciences, 2024). These policies should align with international best practices while being sensitive to local cultural contexts.

• Strengthen Ethical Review Boards: Enhance the capacity of local ethical review boards (ERBs) to effectively review and oversee RWD research. This includes providing training on RWD methodologies, data privacy principles, and ensuring that ERBs are adequately resourced to manage the increasing volume and complexity of data-driven research (ResearchGate, 2024).

• Community Engagement and Trust Building: Implement transparent communication strategies to engage communities in discussions about data collection, its purpose, and its benefits. Building and maintaining trust is paramount to ensuring accurate data provision and public acceptance of digital health initiatives. This involves clear consent processes that are culturally sensitive and accessible.

5. Securing Sustainable Funding and Partnerships

• Increase Domestic Investment: African governments must recognize digital health and RWD as strategic national assets and prioritize and significantly increase domestic financial allocation to digital health infrastructure and RWD initiatives (Ducit Blue Solutions, 2023). This includes allocating recurrent budgets for maintenance and operational costs, not just initial setup.

• Diversify Funding Sources: Explore innovative financing mechanisms beyond traditional donor aid, including public-private partnerships, impact investments, social bonds, and leveraging local philanthropic contributions. This can create more sustainable funding streams that are less susceptible to external fluctuations.

• Strategic and Equitable Collaborations: Foster genuine partnerships with international organizations, academic institutions, and technology companies that offer not just funding but also expertise, technology transfer, and sustainable models for digital health development (Owhor, 2023). These partnerships should prioritize capacity building and local ownership to avoid "digital colonization."

6. Fostering Local Innovation and Community Engagement

• Support Local Tech Solutions: Actively encourage, fund, and invest in African-led digital health innovations that are tailored to the continent's unique challenges and opportunities (Medbook, 2025; mPharma, n.d.). This includes creating incubators, accelerators, and innovation hubs that support health tech startups.

• User-Centric Design and Co-creation: Ensure that digital health tools are designed with the end-users (healthcare workers and patients) in mind, making them intuitive, accessible, and relevant to local workflows and cultural contexts. Involve users in the design and testing phases to ensure solutions meet their needs and are adopted effectively.

• Demonstrate Data for Action: Continuously emphasize and visibly demonstrate how collected data can directly lead to improved patient care, more efficient health services, and better public health interventions. This tangible demonstration of value is crucial for motivating clinicians to collect high-quality data and for communities to trust and participate in data initiatives.

Conclusion

The systematic collection and utilization of real-world data are indispensable for advancing healthcare in Africa. While the challenges are significant and multifaceted, ranging from foundational infrastructure deficits to complex human resource, data quality, ethical, and financial hurdles, they are not insurmountable. By strategically investing in robust digital infrastructure, building comprehensive human resource capacity, promoting rigorous data standardization and interoperability, establishing clear and enforceable ethical and governance frameworks, securing sustainable and diversified funding, and fostering vibrant local innovation, African nations can progressively overcome these barriers. The journey towards a truly data-driven healthcare ecosystem in Africa requires sustained commitment, collaborative efforts from all stakeholders – including governments, local communities, international partners, and the private sector – and a profound recognition that robust RWD is not merely a technical aspiration but a fundamental prerequisite for achieving universal health coverage, improving the well-being of its diverse populations, and contributing invaluable, context-specific insights to the global health community. Unlocking the power of RWD will not only transform African healthcare but also position the continent as a leader in innovative, context-appropriate health solutions.

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