By Paul Nesara
Many African countries are facing multi-faceted policy challenges, in part, due to the quality of data collected or the verticalized “big data” existing ecosystems that are not fully interrogated to generate evidence. Blumer et al., describe big data as “the extensive volume of information that individuals and agencies generate daily”.[1] There are concerns that these siloed data systems are project based, thus pose significant challenges related to data standardisation, timely collation and analyses of data to generate evidence.
In many instances, community-led data systems are absent from the main health information architecture; however, thanks in part to the Global Fund’s 2022-2028 strategy that is being developed and will have community led data systems at the centre of its work. With emerging diseases such as Ebola in some parts of Africa and COVID-19 globally, there are opportunities for strategic information to take a leading role to timely inform the decision-making process.
One of the major purposes of strategic information is to provide critical evidence necessary for program managers and other key players along the cascade to make informed decisions that improve outcomes at all levels. The Consolidated HIV Strategic Information Guidelines by the World Health Organisation[2] clearly identify strategic information as a lynchpin for accountability and transparency in decision-making processes. The view underpins accountability as a way to promote good governance and empower people and communities to claim their health related rights, while transparency is meant to build trust between government and civil society.[3]
Through strategic information, it has been established in some European countries that the increasing global burden of, and the epidemiologic shift towards non-communicable diseases due to ageing and lifestyle changes has had serious ramifications on the social and economies of countries.[4] As a result, European policymakers are now able to use the available data to determine optimal ways to reduce disability and improve the quality of life.
In many African countries, digitization as a component of strategic information still lags yet has the potential to radically transform the health data system for a timely response. With seemingly unlimited opportunities to collect data and reduce the burden of data entry though interface or data interchange with other sector electronic systems such as the private and community vital registrations, lab services, displaced population, service delivery, and logistics, digitization can play a pivotal role by promptly making information available for policymaking.
Data on COVID-19 collected through private and government institutions, for example, has helped countries put in place spread control strategies, in a timely manner coming up with policy measures such as lockdowns, curfews, school closures, or a ban on large gatherings (e.g., religious, sports, funerals, and other social events) to mitigate the spread of the virus. Being able to leverage all the data, nonetheless, requires overcoming the computational, algorithmic, and technological challenges that characterize today’s highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints.
The full potential of big data could be realized if data is intentionally made accessible and shared.[5] In selected European countries, for example, the sharing of patient level information among countries through systems such as Electronic Health Records,[6] has helped health professional to keep track of key elements of patients’ clinical pathways thereby allow for better coordination. In Zambia, the use of SmartCare which has been under development since 2004 aims to provide continuity of care to patients no matter their location.[7]
Prospects for strategic information in the decision-making discourse continue to be high and include setting up sound measures to undertake special surveys, evaluations, and surveillance to detect and rapidly respond to epidemics. In addition, strategic information has helped program managers to put in place integrated community systems for detection and response. Based on evidence generated, public health institutions and policymakers are better equipped to monitor policy changes and evaluate their impact and risks at a population level. When data are readily available, countries are empowered to make better decisions, for example, about public health and resource allocation, including providing for better-informed activities to establish health guidelines, norms, and standards. Lessons can be drawn too from the E-Heza[8] and the eFICHE[9] in Rwanda.
In France, a new policy was built based on a collective analysis of data generated from difficulties encountered following physicians’ resistance to a project meant to introduce the Electronic Health Records.[10] Since then, the Electronic Health Record is widely available to provide all players with the much-needed information to make rational clinical or economic decisions. Several pilot projects in many developing countries such as Kenya, Malawi, Peru, and Haiti are demonstrating the viability of setting up Electronic Health Records in resource constrained countries to generate data and make decisions across the care cascade.
In conclusion, it is worth noting that HIV, TB, or Malaria data collected by the government, private and community-led systems are rarely available in one repository, let alone analysed in totality to give a comprehensive picture of HIV, TB, and malaria in the context of other diseases or conditions prevalent in the country. Inter-operability is one of the solutions and a vision linked at the policy level with anticipated improvements in the efficiency, safety, equity, and cost effectiveness of care.[11] Sadly, it has not yet been realised on a large scale anywhere in the world and many examples exist of it turning into an expensive failure.[12]
Countries with integrated data systems are better prepared to detect public health threats above endemic levels and effectively respond to pandemics. Developing algorithms to systematically harness these data from multiple sources and analyze them is one important step strategic information could continuously play to generate evidence and enhance the decision-making process.
[1] Blumer, L., Giblin, C., Lemermeyer, G., & Kwan, J. A. (2017). Wisdom within: unlocking the potential of big data for nursing regulators. International Nursing Revion, 64(1),77-82.
[2] World Health Organization. (2020). Consolidated HIV strategic information guidelines: driving impact through programme monitoring and management. Executive summary. Geneva, Licence: CC BY-NC-SA 3.0 IGO
[3] Tello, J. & Baez-Camargo, C. (2015). Strengthening health system accountability: a WHO European Region multi-country study WHO Regional Office for Europe, Copenhagen
[4] European Union and Free Trade Association Regional Edition Institute for Health Metrics and Evaluation. (2013). The Global Burden of Disease: Generating Evidence, Guiding Policy. IHME, Seattle, WA, 69
[5] Mählmann, L., Reumann, M., Evangelatos, N., & Brand, A. (2017). Big data for public health policy-making: Policy empowerment. Public Health Genomics, 20(6), 312-320.
[6] World Health Organization. (2016). From innovation to implementation. E-Health in the WHO European Region
[7] Ministry of Health. (2021). About SmartCare. Accessed from About SmartCare – KnowledgeBase (moh.gov.zm)
[8] William Davidson Institute. (2021). E-Heza Digital Health Record Expansion, TIP Global Health (Rwanda). Accessed from E-Heza Digital Health Record Expansion, TIP Global Health (Rwanda) – William Davidson Institute (umich.edu)
[9] Digital Health Care System. (2020). eFiche – A health Care Ecosystem
[10] Philippe Burnel. (2018). The introduction of electronic medical records in France: More progress during the second attempt. Health Policy, 122(9), 937-940
[11] Hoffmarcher, M. M., Oxley, H., & Rusticelli, E. (2007). Improved health system performance through better care coordination. Paris: Organisation for Economic Co-operation and Development. (Health working paper No 30)
[12] Scott, T., Rundall, T. G., Vogt, T. M., & Hsu, J. (2007). Implementing an electronic medical record system: successes, failures, lessons. Oxford: Radcliffe
