Starting with Small Health Data Opportunities for MHealth in Africa

Starting with Small Health Data Opportunities for MHealth in Africa

Ciara Heavin Yvonne O’ Connor 

Health Information Systems Research Centre, University College Cork, Ireland

| |
| | Citation



The need to obtain data to understand effective and available child mortality-reducing control measures in rural areas of developing countries is great. Evidence shows that this challenge can potentially be overcome with the increased availability of Information and Communication Technology (ICT) to support the data/information/ knowledge needs of healthcare delivery services in low resource settings. Recognising the benefits of ICT and the need for improvements in the Nigerian health sector, this paper outlines the plans for the technical feasibility assessment of the IMPACT (usIng Mobile Phones for Assessing, Classifying and Treating sick children) smartphone application to capture, store and analyse of child health assessment data. IMPACT is a secure, scalable, user friendly mobile health (mHealth) innovation that is being developed to support ‘small data’ capabilities within the context of healthcare in the community in Enugu State, Nigeria, Africa. Notwithstanding the heightened focus on ‘big data’ in health, this research is interested in investigating the opportunities associated with doing ‘small healthcare data’ well, with the long term view of building to the big data scenario for healthcare in the community in Enugu. This paper outlines the plan for the IMPACT project considering the implications for health data, knowledge management in healthcare and the big data opportunities to support disease surveillance, healthcare delivery and resourcing and healthcare practitioner education.


data, IMPACT application, Information and Communication Technology, knowledge management and big data, mobile health (mHealth)


[1] WHO, World malaria report - nigerian health profile, avaliable at publications/country-profiles/profile_nga_en.pdf?ua=1 (2015).

[2] WHO, Global Health Observatory (GHO) Part III: Global health indicators, 2013.

[3] Diaz, T., Aboubaker, S. & Young, M., Current scientific evidence for integrated community case management (iCCM) in Africa: findings from the iCCM evidence symposium. Journal of Global Health, 4(2), 2014.

[4] Rasanathan, K., Bakshi, S., Rodriguez, D.C., Oliphant, N.P., Jacobs, T., Brandes, N. & Young, M., Where to from here? policy and financing of integrated community case management (iCCM) of childhood illness in sub–Saharan Africa. Journal of Global Health, 4(2), 2014.

[5] Ozor, L., Rapid access expansion of integrated community case management of malaria, pneumonia and diarrhoea race 2015 – Nigeria. available at 2013.

[6] RAcE, Rapid access expansion 2015. In ed. M. Consortium, available at %28RAcE%202015, 2015.

[7] Bryce, J., Victora, C.G., Habicht, J.-P., Black, R.E. & Scherpbier, R.W., Programmatic pathways to child survival: results of a multi-country evaluation of integrated management of childhood illness. Health Policy and Planning, 20(1), pp. i5–i17, 2015.

[8] Franz-Vasdeki, J., Pratt, B.A., Newsome, M. & Germann, S., Taking mHealth solutions to scale: enabling environments and successful implementation. Journal of Mobile Technology in Medicine, 4(1), pp. 35–38, 2015.

[9] Lee, S.H., Nurmatov, U.B., Nwaru, B.I., Mukherjee, M., Grant, L. & Pagliari, C., Effectiveness of mHealth interventions for maternal, newborn and child health in low–and middle–i ncome countries: systematic review and meta–analysis. Journal of Global Health, 6(1), 2016.

[10] Ginige, J.A., Maeder, A.J. & Long, V., Evaluating success of mobile health projects in the developing world. Global Telehealth, p. 7, 2014.

[11] Peiris, D., Praveen, D., Johnson, C. & Mogulluru, K., Use of mHealth systems and tools for non-communicable diseases in low-and middle-income countries: a systematic review. Journal of Cardiovascular Translational Research, 7(8), pp. 677–691, 2014.

[12] Beratarrechea, A., Lee, A.G., Willner, J.M., Jahangir, E., Ciapponi, A. & Rubinstein, A., The impact of mobile health interventions on chronic disease outcomes in developing countries: a systematic review. Telemedicine and e-Health, 20(1), pp. 75–82, 2014.

[13] Donner, J. & Mechael, P., mHealth in Practice: Mobile Technology for Health Promotion in the Developing World, A&C Black, 2012.

[14] Sicotte, C., Paré, G., Moreault, M.P. & Paccioni, A., A risk assessment of two interorganizational clinical information systems. Journal of the American Medical Informatics Association, 13(5), pp. 557–566, 2006.

[15] Arshad, U., Mascolo, C. & Mellor, M., Exploiting mobile computing in health-care. Proceedings of Demo Session of the 3rd International Workshop on Smart Appliances, ICDCS03, 2003.

[16] Goel, M.K., Kumar, Y., Rasania, S.K., Roy, P. & Bachani, D., Potential of mobile technology (mhealth) in medical and health care delivery. Research & Reviews: Journal of Medical Science and Technology, 2(1), 2013.

[17] Kumar, S., Nilsen, W., Pavel, M. & Srivastava, M., Mobile health: revolutionizing healthcare through transdisciplinary research. Computer, 1, pp. 28–35, 2013.

[18] Ammenwerth, E., Buchauer, A., Bludau, B. & Haux, R., Mobile information and communication tools in the hospital. International Journal of Medical Informatics, 57(1), pp. 21–40, 2000.

[19] Davis, T., Going mobile: how mobile technology is evolving in clinical trials. 2014.

[20] Neff, G. Why big data won’t cure us. Big Data, 1(3), pp. 117–123, 2013.

[21] Staab, S. & Studer, R., Handbook on Ontologies, Springer Science & Business Media, 2013.

[22] Puri, C.A., Gomadam, K., Jain, P., Yeh, P.Z. & Verma, K., Multiple ontologies in healthcare information technology: motivations and recommendation for ontology mapping and alignment, ICBO, 2011.

[23] Nardon, F.B. & Moura, L.A., Knowledge sharing and information integration in healthcare using ontologies and deductive databases. Medinfo, 11(1), pp. 62–66, 2004.

[24] Davenport, T.H. & Prusak, L., Working Knowledge. How Organizations Manage What They Know, Boston, Mass.: Harvard Business School Press, 1998.

[25] Wurman, R.S., Information Anxiety 2, Que, Indiana, USA., 2001.

[26] Abidi, S.S.R., Knowledge management in healthcare: towards ‘knowledge-driven’decisionsupport services. International Journal of Medical Informatics, 63(1), pp. 5–18, 2001.

[27] Shoniregun, C.A., Dube, K. & Mtenzi, F., Electronic Healthcare Information Security, Springer Science & Business Media, 2010.

[28] Kim, J. & Chung, K.-Y., Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications, 71(2), pp. 873–888, 2014.

[29] Lucas, J.D. & Bulbul, T., Ontology to support healthcare facility management. Ontology in the AEC Industry, p. 47, 2015.

[30] Lieberman, H. & Mason, C., Intelligent agent software for medicine. In Future of Health Technology, ed. R.G. Bushko, Amsterdam: IOS Press, pp. 99–109, 2002.

[31] Anderson, J.G., Clearing the way for physicians’ use of clinical information systems. Communications of the ACM, 40(8), pp. 83–90, 1997.

[32] Druss, B., Glasziou, P., Kernick, D.P., Gerhardus, A., Wilson, T., Ben-Shlomo, Y., Moayyeri, A., Soltani, A. & Twisselmann, B., Evidence based medicine: does it make a difference? British Medical Journal, 7482, p. 92, 2005.

[33] Glasziou, P., Burls, A. & Gilbert, R., Evidence based medicine and the medical curriculum. British Medical Journal, 337,2008.

[34] El Morr, C. & Subercaze, J., Knowledge management in healthcare. Handbook of Research on Developments in e-Health and Telemedicine: Technological and Social Perspectives, eds. M.M Cunha, A. Tavares & R. Simões, IGI Global, 2010.

[35] Cowling, A., Newman, K. & Leigh, S., Developing a competency framework to support training in evidence-based healthcare. International Journal of Health Care Quality Assurance, 12(4), pp. 149–160, 1999.

[36] Jones, R., Panda, M. & Desbiens, N., Internal medicine residents do not accurately assess their medical knowledge. Advances in Health Sciences Education, 13(4), pp. 463–468, 2008.

[37] Ventola, C.L., Mobile devices and apps for health care professionals: uses and benefits. Pharmacy and Therapeutics, 39(5), p. 356, 2014.

[38] Verhoeven, A.A.H., Information-seeking by general practitioners. 1999.

[39] Timpka, T. & Arborelius, E., The GP’s dilemmas: a study of knowledge need and use during health care consultations. Methods of Information in Medicine, 29(1), p. 23, 1990.

[40] Lenz, R. & Reichert, M., IT support for healthcare processes–premises, challenges, perspectives. Data and Knowledge Engineering, 61(1), pp. 39–58, 2007.

[41] Ebell, M.H., How to find answers to clinical questions. American Family Physician, 79(4), pp. 293–296, 2009.

[42] Weingart, S.N., Massagli, M., Cyrulik, A., Isaac, T., Morway, L., Sands, D.Z. & Weissman, J.S., Assessing the value of electronic prescribing in ambulatory care: a focus group study. 

International Journal of Medical Informatics, 78(9), pp. 571–578, 2009.

[43] Yang, C.W., Fang, S.C. & Huang, W.M., Isomorphic pressures, institutional strategies, and knowledge creation in the health care sector. Health Care Management Review, 32(3), p. 263, 2007.

[44] Nonaka, I., A dynamic theory of organizational knowledge creation. Organization Science, 5(1), pp. 14–37, 1994.

[45] Alavi, M. & Leidner, D.E., Review: knowledge management and knowledge management systems: conceptual foundations and research issues. Mis Quarterly, 25(1), pp. 107–136, 2001.

[46] McQueen, R., Four views of knowledge and knowledge management. AMCIS 1998 Proceedings, 204, 1998.

[47] Zack, M., An architecture for managing explicated knowledge. Sloan Management Review, 1998.

[48] Eriksson, I. & Raven, A., Gaining competitive advantage through shared knowledge creation: 

in search of a new design theory for strategic information systems, 1996.

[49] Grant, R.M., Toward a knowledge-based theory of the firm. Strategic Management Journal, 17, Winter Special, pp. 109–122, 1996.

[50] Berman, S.L., Down, J. & Hill, C.W.L., Tacit knowledge as a source of competitive advantage in the National Basketball Association. Academy of Management Journal, 45(1), pp. 13–31, 2002.

[51] Lam, A., Tacit knowledge, organizational learning and societal institutions: an integrated framework. Organization Studies, 21(3), pp. 487–513, 2000.

[52] Allard, S., Knowledge creation. Handbook on Knowledge Management, 1, pp. 367–379, 2003.

[53] Abeson, F. & Taku, M., Knowledge source and small business competiveness. Competition Forum, 4(2), pp. 464–469, 2006.

[54] Hsia, T.L., Lin, L.M., Wu, J.H. & Tsai, H.T., A framework for designing nursing knowledge management systems. Interdisciplinary Journal of Information, Knowledge, and Management, 1(1), 2006.

[55] Holsapple, C.W. & Whinston, A.B., Knowledge-based organizations. The Information Society, 5(2), pp. 77–90, 1987.

[56] Mitchell, R. & Boyle, B., Knowledge creation measurement methods. Journal of Knowledge Management, 14(1), pp. 67–82, 2010.

[57] Persson, A. & Stirna, J., How to transfer a knowledge management approach to an organization – a set of patterns and anti-patterns. Proceedings of the 6th International Conference on Practical Aspects of Knowledge Management (PAKM), Springer, pp. 243–252, 2006.

[58] Dung, T.Q. & Kameyama, W., A proposal of ontology-based health care information extraction system: Vnhies. Research, Innovation and Vision for the Future, 2007 IEEE International Conference on: IEEE, pp. 1–7, 2007.

[59] Bodenreider, O., Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearbook of Medical Informatics, 67, 2008.

[60] Chen, H., Chiang, R.H. & Storey, V.C., Business intelligence and analytics: from big data to big impact. Mis Quarterly, 36(4), pp. 1165–1188, 2012.

[61] Murdoch, T.B. & Detsky, A.S., The inevitable application of big data to health care. Jama, 309(13), pp. 1351–1352, 2013.

[62] Tata Consulting Services, Managing knowledge from big data analytics in product development, available at [last accessed 23/3/16] 2013.

[63] McAfee, A. & Brynjolfsson, E., Big data: the management revolution. Harvard Business  Review, 90(10), pp. 60–68, 2012.

[64] Lycett, M., ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), pp. 381–386, 2013.

[65] Davenport, T.H., Keep up with your quants. Harvard Business Review, 91, pp. 7–8, p. 120, 2013.

[66] LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S. & Kruschwitz, N., Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), pp. 21–32, 2011.

[67] Rahman, N., Aldhaban, F. & Akhter, S., Emerging technologies in business intelligence. Technology Management in the IT-Driven Services (PICMET), 2013 Proceedings of PICMET’13: IEEE, pp. 542–547, 2013.

[68] Kołodziej, J., Correia, L. & Molina, J.M., Intelligent Agents in Data-Intensive Computing, Springer, 2015.

[69] Raghupathi, W. & Raghupathi, V., Big data analytics in healthcare: promise and potential. 

Health Information Science and Systems, 2(1), p. 3, 2014.

[70] Arima, H., Utilizing big data for public health. Journal of Epidemiology, 26(3), pp. 105–105, 2016.

[71] Knoke, D., Networks of elite structure and decision making. In Advances in Network Analysis: Research in the Social and Behavioural Sciences, ed. S.W. Galaskiewicz, Thousand Oaks, CA: SAGE, pp. 274–274, 1994.