Information and Communication Technology for Development (ICT4D) (Routledge Perspectives on Development) by Richard Heeks

Author:Richard Heeks [Heeks, Richard]
Language: eng
Format: epub
Publisher: Taylor and Francis
Published: 2017-11-08T08:00:00+00:00

Figure 6.6 Trajectories of e-health for development

6.2.2. e-Health and development challenges

The listing above presents a fairly positive picture of e-health applications in development. However, it is reasonable to summarise that the leitmotif of e-health and development literature has been challenge more than it has been achievement. Developing countries are not alone in this – the UK National Health Service has been described as “a late and slow adopter” of new healthcare technologies (Peirce et al. 2015:69) – neither are e-health applications alone in this. We will therefore try to focus here on the particular challenges faced by e-health applications in developing countries.

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Ensuring the information value chain runs all the way from data to results is especially challenging in the context of health in developing countries. The poor state of health data makes it hard even to secure the first stage of the chain: providing data that meets even a minimum of the OCARA (see Chapter 2) criteria for data quality. Health systems (remembering Figure 6.4) remain paper-intensive; they are fragmented both horizontally and vertically (for example with different hospitals using different data systems; with health centres and government agencies using different data systems; and with splits across public, private and non-profit health providers) making interoperability and use of data standards difficult; with further challenges to interoperability due to the particular need to maintain confidentiality of health data (Friederici et al. 2012, Fornazin & Joia 2014).

Technology can help to deliver data more efficiently but – unless moving to digital decision-making – it then runs up against problems of human decision-making. Telemedicine applications can hit this barrier: if there is a set number of specialists in the country, then connecting them into multiple other sites will achieve nothing unless they can be made to work more efficiently or work longer hours. As seen in the IKON project, capacity-building of other decision-makers is thus increasingly built into telemedicine projects.

Similar issues arise with health management and policy IS: decision-making processes and capacities are often weak, so data can be delivered to decision-makers but then remain unused. A national health information systems project in Ivory Coast recognised this and the need to move beyond just technological intervention, to include decision-making and implementation capacity-building (Nutley et al. 2014). The result in health districts was a rise from 40 per cent to 82 per cent of decision-making discussions that included health information system data, and a rise from 43 per cent to 64 per cent of referring decisions upwards in order that they could be actioned. In part, what these additional interventions also seek to achieve is moving e-health intervention from data-push to decision-pull: not shoving data at decision-makers but improving the data demand from decision-makers.

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This moves us towards the issue of incentives and motivations – as argued in Chapters 2 and 3, a core to understanding success or failure of ICT4D applications. These may impact e-health from a more objective direction; for example, given the severe shortage of funding that health systems face in developing countries, e-health may be a low priority.

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