Analysis of a country’s relative ranking on each component metric, and the summary metric, can be used to identify aspects where further development would contribute to eHealth investment strengthening. The summary metric provides an overall indication of a country’s eHealth investment readiness, relative to other countries.
Comparison of the component metric profiles of regional country groupings can help those countries identify good practices to be shared with neighbouring countries. Individual metrics can hide nuances, therefore exploring all metrics for each country under evaluation is encouraged. Similarly, comparing countries’ profiles provides additional insights illustrated by the varying patterns seen on the spider charts. Scoring less than 1.00 for a metric shows underperformance against peers, and represents an opportunity for improvement. Comparison of the scoring patterns can reveal individual and/or regional performance in each of these quadrants: bottom and right lower quadrant for financial and economic indicators, left lower quadrant for two ICT development indicators, left upper quadrant for human capital and governance, and right upper quadrant for development of the eHealth environment (Fig. 7).
Using the study findings, each African country can review its metric scores, plot its spider chart to show its performance, and use the results to establish an eHealth investment strengthening plan. For example, despite having the highest summary score, Mauritius’ results identified four areas that, if strengthened, will improve the likelihood of successful eHealth investment. These included updating its eHealth strategy, addressing aspects of the GOE survey that scored poorly, growing the Mauritian economy and lobbying for more allocation of the fiscus to health.
Countries with an eHealth Strategy, relatively high GDP and health spend per capita, and high governance scores, such as Mauritius, South Africa and Botswana, can apply the Five Case Model to improve eHealth investment decisions. Countries with an eHealth Strategy and high governance score, but low CHE scores, such as Kenya, Morocco and Senegal, should start by focusing on the economics and finance aspects of their eHealth programmes. Countries with an eHealth Strategy and low governance score, such as Nigeria, should focus on governance strengthening, as a foundational requirement for eHealth investment.
Regional spider charts help to illustrate this analysis. Thus, data for the AMU (Fig. 3) suggest that while Morocco and Tunisia show similar patterns, Tunisia remains hampered by lack of an eHealth Strategy retarding its eHealth development. Changing this will remain challenging while growth of real GDP and CHE metrics remain low, represented on the spider chart as low scores on the right, lower quadrant. Despite Libya’s generally lower than average performance, Libya scores well on growth of real GDP (2nd ), far higher than Morocco (22nd ) and Tunisia (39th ). This, combined with a moderate IDI score (0.70), sets the stage for Libya to craft an eHealth Strategy to guide the beginning of eHealth investments.
In the EAC (Fig. 4), Kenya and Tanzania have similar summary metric scores and high scores for strategy, growth rates of real GDP and governance, yet important differences, such as Kenya’s higher score on Internet penetration. If the region considered identifying a country lead for key elements, it would include Kenya leading on connectivity. South Sudan scores are poor on all metrics, though with a slight shift to the left caused by the HCI score (0.45), which could indicate potential warranting further development. A dominant feature of the EAC spider chart is poor scores on the two CHE metrics, represented by the “missing” bottom right quadrant, highlighting the need for growth to include more fiscal allocations to health.
In the ECOWAS (Fig. 5), all three countries show good growth of real GDP, though CHE per capita remains poor and IDI is poor. Each of the spider chart quadrants has some activity, which may indicate that a collaborative regional approach will prove fruitful. Sierra Leone has achieved the highest score on CHE as a percentage of GDP (1st ), though has inadequate eHealth Strategy (36th ) and poor GOE survey scores (37th ). An opportunity could be to develop a new eHealth Strategy, fuelled by CHE priorities. Promising governance rankings in Senegal (10th ) underpin the growth of real GDP and a regional eHealth leadership role for Senegal.
The SADC spider chart (Fig. 6), shows a marked “lean” towards the left caused by low scores in the two eHealth implementation metrics in the top right quadrant. Namibia’s poor eHealth strategy score may help to explain why, despite promising rankings on governance (5th ) and IDI (13th ), the GOE survey score remains low (33rd ). Angola is constrained by poor scores on strategy, CHE metrics, IDI and governance. A regional strategy that includes collaboration to share good practices, particularly to improve SADC country’s eHealth strategies, might prove useful.
Correlation analysis provides information about relationships between component metrics. Correlations above 0.75 between the summary metric and two component metrics, the Ibrahim Governance Index (0.85) and Internet penetration (0.78), suggest that either of these would provide a reasonable surrogate indicator of overall eHealth investment readiness. Correlation between component metrics shows modest correlation for Ibrahim Governance Index and IDI (0.66), and Ibrahim Governance Index and Internet penetration (0.64). These are consistent with suggestions that ICT development plays a role in promoting good governance [44, 45] and may suggest that governance is a requirement for countries to make productive eHealth investments. Correlation between health expenditure per capita and ICT development (0.65) underpins the importance of addressing affordability issues and may support suggestions that ICT initiatives themselves contribute positively to economic growth. [46]
The metrics used to develop the eHealth Investment Readiness Assessment Tool reflect aspects of eHealth investment that are aligned to the five cases. The tool highlights countries’ strengths and weaknesses, thereby providing information for targeted eHealth investment plans. It also helps to identify strengths in neighbouring countries to support collaborative partnerships for regional eHealth investment. This demonstrates the applicability of the Five Case Model to African eHealth investment decisions. The Five Case Model should now be validated through in-country field testing.