ICTs and Precision Development: Towards Personalised Development

Are ICTs about to deliver a new type of socio-economic development: personalised development?

ICTs can only have a significant development impact if they work at scale; touching the lives of thousands or better still millions of people.  Traditionally, this meant a uniform approach where everyone gets to use the same application in the same way.

Increasingly, though, ICTs have been enabling “precision development”: increasingly-precise in terms of who or what is targeted, what is known about the target, and the specificity of the associated development intervention.  The ultimate end-point would be “personalised development”: interventions customised to each individual.

Elements of digitally-enabled individualisation have already emerged: farmers navigating through web- or IVR-based systems to find the specific information they need; micro-entrepreneurs selecting the m-money savings and loan scheme and level that suited them.  But there is still rigidity and constraints within these systems.

Though we are far from its realisation, the potential for truly personalised development is now emerging.  For example:

• Personalised Learning: “a methodology, according to which teaching and learning are focused on the needs and abilities of individual learners”[1]. ICTs are integral to personalised learning and technology-enabled personalisation has had a demonstrable positive impact on educational performance[2].
• Precision Agriculture: though around as a concept for at least two decades, precision agriculture is only now starting to find implementations – often still at pilot stage – in the global South[3]. Combining data from on-ground sensors and remote sensing, precision agriculture provides targeted guidance in relation to “seeds, fertilizers, water, pesticides, and energy”.  The ultimate intention is that guidance will be customised to the very specific soil, micro-climate, etc. parameters of individual farms; even smallholder farms.
• Personalised Healthcare: diagnosis and treatment may appear personalised but typically involve identifying which illness group a person belongs to, and then prescribing the generic treatment for that group. This is becoming more accurate with improvements in electronic health records that provide a more person-specific history and context[4].  Precision medicine prescribes even more narrowly for the individual; typically based on genetic analysis that requires strong digital capabilities.  Though at early stages, this is already being implemented in developing countries[5].

ICTs are thus leading us on a precision development track that will lead to personalised development.  The promise of this can be seen in the examples above: individualised information on learning level, farm status, or health status that then enables a much more effective development intervention.

It will be interesting to log other examples of “ICT4PD” as they emerge . . .

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[1] Izmestiev, D. (2012). Personalized Learning: A New ICT-Enabled Education Approach, UNESCO Institute for Information Technologies in Education, Moscow.

[2] Kumar, A., & Mehra, A. (2018). Remedying Education with Personalized Learning: Evidence from a Randomized Field Experiment in India, ResearchGate.

[3] Say, S. M., Keskin, M., Sehri, M., & Sekerli, Y. E. (2018). Adoption of precision agriculture technologies in developed and developing countries. The Online Journal of Science and Technology, 8(1), 7-15.

[4] Haskew, J., Rø, G., Saito, K., Turner, K., Odhiambo, G., Wamae, A., … & Sugishita, T. (2015). Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya. International Journal of Medical Informatics, 84(5), 349-354.

[5] Mitropoulos, K., Cooper, D. N., Mitropoulou, C., Agathos, S., Reichardt, J. K., Al-Maskari, F., … & Lopez-Correa, C. (2017). Genomic medicine without borders: Which strategies should developing countries employ to invest in precision medicine? Omics: A Journal of Integrative Biology, 21(11), 647-657.

Bricolage and the Sustainability of ICT4D Solutions

In  ICT4D, bricolage refers to context-sensitive ways of implementing and sustaining ICT4D solutions [1].  Different from approaches where strategic goals, ways to achieve them, as well as success and failure metrics are defined in advance, bricolage is mostly characterised by improvisation and continuous learning from failures in environments with many uncertainties [2].  People who play key roles in shaping and driving the bricolage process are hereafter referred to as bricoleurs.

Drawing from a particularly successful long-term ICT4D project in Tanzania, for which the author of this post has been part of a team for about 10 years, this article discusses a three-stage process that local bricoleurs have gone through in sustaining the project in the face of scarce resources and diverse interests of stakeholders.  Extended empirical and theoretical insights about the role of bricolage in shaping and sustaining the project were reported in the work of Fruijtier and Senyoni [3], and this post will essentially provide some sound bites from the paper.

Bricolage in ICT4D Projects: Stages

1.    Opportunity Based: During this stage, a project opportunity is identified, its activities are mainly driven by external players, and the local bricoleur gets involved in project activities based on availability and need, as determined by main players. In the case of the Health Information Systems Program (HISP) team at the University of Dar es Salaam in Tanzania (hereafter referred to HISP UDSM), this stage was characterised by the advent of a pilot project for implementing the District Health Information Software (DHIS) in Kibaha and Bagamoyo districts in the Pwani region.  This was around 2002-2010 and the main focus of the project during this period was to demonstrate the capabilities of the then-new DHIS system in handling routine aggregate health data, and to make a case for the endorsement and national rollout of the system by the Ministry of Health (MoH) in Tanzania. The University of Oslo (UiO) (main developers of the DHIS system) mainly influenced the direction of project activities during this period, and the HISP UDSM team supported the pilot districts in activities such as training, user support and data analysis, as was determined by the main team at UiO. 

PhD and MSc scholarships were also established as a result of collaboration between the UiO and HISP UDSM in order to, among other things, strengthen local capacity for supporting project activities in Tanzania. It was the ability to serendipitously survive funding uncertainties and diverse interests of stakeholders, and the partnership with MoH in persuading a variety of stakeholders to pursue the common cause (strengthening HMIS (Health Management Information System) data reporting) that prepared the UDSM team for the would be next phases of the project where it (HISP UDSM) turned out to play a key role that fostered project success.

2.    Locally Owned: During this stage, bricoleurs cultivate the growth of what is already achieved while advancing their knowledge and understanding of practices in the project domain. In the case of the HISP UDSM team, this was the period from 2010-2015 which was characterised by close involvement with MoH in providing technical support during revision of HMIS data collection tools and definition of indicators prior to the national rollout of DHIS, and playing the central training role during the national rollout which was done in December 2013. After the national rollout, HISP UDSM got closely involved in supporting hundreds of users across the country, as well as bringing data for other programs and partners on board. Apart from this close involvement, care was taken to involve MoH and its various departments on every step of the way, to foster ownership and long term sustainability of the project.

3.    Locally Driven: At this stage, bricoleurs assume main control of events in the project. They can proactively anticipate challenges, and provide them with apt solutions. In the case of the HISP UDSM team, this is a period from 2015 onwards. It is characterised by, among other things, new projects and requirements from various stakeholders. Following the successful DHIS national rollout in 2013, the HISP UDSM team was also requested by other ministries in Tanzania to implement similar solutions for them. In response to this, so far, HISP UDSM has customised DHIS to serve the data reporting and analysis requirements of the Ministry of Agriculture and the Ministry of Water in Tanzania. Arrangements are underway to do the same for other ministries and government departments. As well, as they continue using DHIS, various MoH partners keep on requesting new and rather generic functionalities which are not yet implemented by the main DHIS developer base, which is globally led by UiO.  To respond to this, in 2015, the HISP UDSM team devised an innovation strategy which has seen the implementation of generic solutions, in terms of new DHIS functionalities and mobile apps, that have turned out to be useful to other DHIS users across the globe [3]. 

Conclusion

Two key take-aways for other ICT4D projects:

1. The sustainability likelihood of an ICT4D project increases with an increase in the ability of the bricoleur to create the environment that fosters the prosperity of bricolage. Importantly, to be innovative in unpredictable project envoronments, bricoleurs need to build both social and technological alliances.
2. Because of the special emphasis on learning, universities can be conducive environments for bricolage to thrive.

References

1.    Ali, Maryam, and Savita Bailur. “The challenge of “sustainability” in ICT4D—Is bricolage the answer.” Proceedings of the 9th international conference on social implications of computers in developing countries. 2007.

2.    Ciborra, Claudio U. “From thinking to tinkering: The grassroots of strategic information systems.” Bricolage, Care and Information. Palgrave Macmillan, London, 2009. 206-220.

3.    Fruijtier, Elisabeth, and Wilfred Senyoni. “The Role of Local Bricoleurs in Sustaining Changing ICT4D Solutions.” International Development Informatics Association Conference. Springer, Cham, 2018.

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Big Data and Healthcare in the Global South

The global healthcare landscape is changing. Healthcare services are becoming ever more digitised with the adoption of new technologies and electronic health records. This development typically generates enormous amounts of data which, if utilised effectively, have the potential to improve healthcare services and reduce costs.

The potential of big data in healthcare

Decision making in medicine relies heavily on data from different sources, such as research and clinical data, rather than only based on individuals’ training and professional knowledge. Historically, healthcare organisations have often based their understanding of information on an incomplete grasp of reality on the ground, which could lead to poor health outcomes. This issue has recently become more manageable with the advent of big data technologies.

Big data comprises unstructured and structured data from clinical, financial and operational systems, and data from public health records and social media that goes beyond the health organisations’ walls. Big data, therefore, can support more insightful analysis and enable evidence-based medicine by making data transparent and usable at much broader verities, much larger volumes and higher velocities than was ever available to healthcare organisations [1].

Using big data, healthcare providers can, for example, manage population health by identifying patients at high-risk during disease outbreaks and then take preventive actions. In one case, Google used data from user search histories to track the spread of influenza around the world in near real time (see figure below).

Google Flu Trends correlated with influenza outbreak^[2]

Big data can also be used for identifying procedures and treatments that are costly or delivering insignificant benefits. For example, one healthcare centre in the USA has been using clinical data to bring to light costly procedures and other treatments. This helped it to reduce and identify unnecessary procedures and duplicate tests. In essence, big data not only helped to improve high standards of patient care but also helped to reduce the costs of healthcare [3].

Medical big data in the global south

The potential healthcare benefits of big data are exciting. However, it can offer the most significant potential rewards for developing countries. While global healthcare is facing challenges to improve health outcomes and to reduce costs, these issues can be severe in developing countries.

Lack of sufficient resources, poor use of existing funds, poverty, and lack of managerial and related capabilities are the main differences between developing and developed countries. This means health inequality is more pronounced in the global south. Equally, mortality and birth rates are relatively high in developing countries as compared to developed countries, which have better-resourced facilities [4].

Given improvements in the quality and quantity of clinical data, the quality of care can be improved. In the global south in particular, where health is more a question of access to primary healthcare than a question of individual lifestyle, big data can play a prominent role in improving the use of scarce resources.

How is medical big data utilised in the global south?

To investigate this key question, I analysed the introduction of Electronic Health Records (EHR), known as SEPAS, in Iranian hospitals. SEPAS is a large-scale project which aims to build a nationally integrated system of EHR for Iranian citizens. Over the last decade, Iran has progressed from having no EHR to 82% EHR coverage for its citizens [5].

EHR is one of the most widespread applications of medical big data in healthcare. In effect, SEPAS is built with the aim to harness data and extract value from it and to make real-time and patient-centred information available to authorised users.

However, the analysis of SEPAS revealed that medical big data is not utilised to its full potential in the Iranian healthcare industry. If the big data system is to be successful, the harnessed data should inform decision-making processes and drive actionable results.

Currently, data is gathered effectively in Iranian public hospitals, meaning that the raw and unstructured data is mined and classified to create a clean set of data ready for analysis. This data is also transferred into summarised and digestible information and reports, confirming that real potential value can be extracted from the data.

In spite of this, the benefit of big data is not yet realised in guiding clinical decisions and actions in Iranian healthcare. SEPAS is only being used in hospitals by IT staff and health information managers who work with data and see the reports from the system. However, the reports and insights are not often sent to clinicians and little effort is made by management to extract lessons from some potentially important streams of big data.

Limited utilisation of medical big data in developing countries has also been reported in other studies. For example, a recent study in Saudi Arabia [6] reported the low number of e-health initiatives. This suggests the utilisation of big data faces more challenges in these countries.

Although this study cannot claim to have given a complete picture of the utilisation of medical big data in the global south, some light has been shed on the topic. While there is no doubt that medical big data could have a significant impact on the improvement of healthcare in the global south, there is still much work to be done. Healthcare policymakers in developing countries, and in Iran in particular, need to reinforce the importance of medical big data in hospitals and ensure that it is embedded in practice. To do this, the barriers to effective datafication should be first investigated in this context.

References

[1] Kuo, M.H., Sahama, T., Kushniruk, A.W., Borycki, E.M. and Grunwell, D.K. (2014). Health big data analytics: current perspectives, challenges and potential solutions. International Journal of Big Data Intelligence, 1(1-2), 114-126.

[2] Dugas, A.F., Hsieh, Y.H., Levin, S.R., Pines, J.M., Mareiniss, D.P., Mohareb, A., Gaydos, C.A., Perl, T.M. and Rothman, R.E. (2012). Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clinical infectious diseases, 54(4), 463-469.

[3] Allouche G. (2013). Can Big Data Save Health Care? Available at: https://www.techopedia.com/2/29792/trends/big-data/can-big-data-save-health-care (Accessed: August 2018).

[4] Shah A. (2011). Healthcare around the World. Global Issues. Available at: http://www.globalissues.org/article/774/health-care-around-the-world (Accessed: August 2018).

[5] Financial Tribune (2017). E-Health File for 66m Iranians. Available at: https://financialtribune.com/articles/people/64502/e-health-files-for-66m-iranians (Accessed: August 2018).

[6] Alsulame K, Khalifa M, Househ M. (2016). E-Health Status in Saudi Arabia: A Review of Current Literature. Health Policy and Technology, 5(2), 204-210.

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