Technological development has been a key driver of growth in global healthcare (Genovese et al., 2022). Every health sector must therefore integrate a complex adaptive system capable of utilizing valid and reliable data in decision-making, advocacy and intervention mapping. To achieve this objective, Nigeria needs transformation in its health ICT architecture, especially in the areas of software applications, data processes, metrics, technology, skills, incentives, sponsorship and culture. However, health information generation and analytics in Nigeria is still very low and the vacuum in its digital environment calls for innovative approaches to how information needs assessment is conducted—including how academic research gaps are identified and how research questions are framed.

Data analytics refers to an iterative process with feedback loops—and it utilizes software applications (Andreatta et al., 2011). Further, data analytics involves an extensive use of data which may be statistical in nature, including predictive and explanatory models, and factual interpretations of data that enhance decision-making as well as empower users to take strategic, result-oriented actions. In an era when business intelligence is measured by how machine learning and artificial intelligence (AI) are maximized for gathering, managing and reporting decision-oriented data, health organizations have better chances improve analytical capabilities, work processes and DH technologies. Thus, data analytics—as part of health innovation—has been useful in optimizing data management, training employees, and implementing DH projects to improve healthcare service delivery across the world. In Nigeria, the PHC system has deployed digital health as an innovative technology approach to enhance the quality of health data, provide remote training for a large number of health workers through video teleconferencing technologies, and disseminate health-related information through the Short-Message-Services (SMS) technology—thereby transforming the quality-of-care services (Ngabo et al., 2012).

Some examples of the innovative healthcare organizations influencing growth of DH in Rwanda are:

Zenysis technologies—a DH organization that owns platforms for integrating health data from routine information systems and surveys. The innovative platforms are also developed with the capacity to analyse, compare and share health data across multiple platforms thereby making it possible for clinicians and health research institutes to predict health events correctly.

Babyl—is a reputable provider of digital healthcare in Rwanda. The organization owns platforms used for virtual medical consultations in order to reduce waiting times at hospitals. Thus, the DH system was developed to remove redundancy and improve the efficiency and overall performance of Rwandan healthcare system.

The Global Alliance for Vaccines and Immunization (GAVI)—is a frontline influencer in the Rwandan DH ecosystem. The organization uses an acronym “INFUSE” which means (Innovation for Uptake, Scale-up and Equity in Immunization) to close the performance gaps in global immunization. GAVI also provides immense support for other related health projects such as using drones to facilitate distribution of lifesaving blood supplies among rural communities and health facilities situated in inaccessible locations across Rwanda.

Research participants agreed during the discussion sessions that data analytics is one of the major areas of disruptive health innovation being underutilized among health systems in Nigeria. They also concluded that the process of developing analytical capability is iterative because ICT personnel (including other health workers using DH technologies to perform their duties) tend to gain better insights into the dynamics of their jobs/activities over time by working with data and refining analytic models. This implies that integrating innovative health management strategies is never a quick and failproof effort. However, stakeholders in the DH ecosystem should collaborate and ensure that health sector data is up-to-date, safe, secured and interoperable—as well as easily accessed. In addition, DH facilitators and health administrators in Nigeria should develop frameworks for translating and using data analytics to improve quality of healthcare. To achieve this purpose, they need to implement recommendations from academic research institutions with unhindered access to the national data warehouse and other information exchange platforms. Importantly, state governments should support FMoH and WHO Country Office by consistently providing health statistics on time. Data managers also have an obligation to openly report de-identified data according to international data exchange standards outlined in the Health Information Exchange policy manual.

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