The Economic Case for Costing Digital Health Workforce Capacity in Africa

The Investment - Readiness Mismatch

Electronic health records, health information systems and other digital tools are being introduced across health facilities in Sub-Saharan Africa (InnovateHealth Africa [IHA], 2026). The value of these investments depends on a workforce able to use them, and the conditions for building that workforce are largely absent. IHA’s analysis of Global Digital Health Monitor data found that approximately 83% of countries in the region with available data are at the two lowest phases of integrating digital health into pre-service training, and that in-service training relies mainly on episodic, donor-funded programmes (IHA, 2026).

This is usually discussed as a training problem. This article argues that it is also an economic one. Workforce capacity is an investment input, in the same way that hardware, software and connectivity are. Its costs, and its contribution to the value obtained from digital systems, should therefore be assessed when investments are appraised, budgeted and evaluated. At present, the evidence does not allow those costs or returns to be quantified. That absence is not a weakness in the argument. It is part of the problem the argument identifies: decisions are being made without the information needed to judge them.

How Workforce Readiness Shapes Value Realisation

A digital health system produces value only when it is used, and used well. Its costs behave differently. Procurement, deployment, licences, devices and maintenance are paid in full whether the system is used by every health worker or by a few. As use falls, the cost of each unit of benefit rises.

A hypothetical example makes the point. Suppose a hospital installs an electronic medical record (EMR) system for all its clinical staff, but only some of them use it routinely. The hospital has paid for the whole system and is receiving only part of its value. The investment has not failed outright, but it is delivering less than it was bought to deliver.

The evidence shows that this situation is common. In Ethiopia, EMR use was 68.5% among providers at selected facilities in Addis Ababa (Keleb et al., 2022) but only 26.6% at public facilities in Dire Dawa (Tolera et al., 2022). These are two settings within one country, showing substantial differences in the use of the same type of technology.

Low use can also add costs, not just forgo benefits. Providers in Addis Ababa described recording patient information on paper and in the EMR at the same time, which increased their workload (Bekele et al., 2024). In that situation, the health system pays the running costs of two record systems, including the staff time spent maintaining both, while gaining the full benefit of neither. No study identified for this article has placed a monetary value on this loss, so its scale remains unknown.

What explains the difference in use? Workforce capacity is part of the answer. In Addis Ababa, providers who had received EMR training had substantially higher odds of using the system (adjusted odds ratio 5.8), although the wide confidence interval (1.6 - 20.7) shows considerable uncertainty about the size of the effect (Keleb et al., 2022). In Dire Dawa, however, training showed no significant association with use. There, use was more closely linked to whether the EMR was discussed in management meetings and to how staff rated the system and the support around it (Tolera et al., 2022). Both studies are cross-sectional, so neither can establish a cause.

The two studies point to two conclusions. First, workforce capacity means more than a single training course. It also includes supervision, ongoing support and keeping trained staff in post. Second, workforce capacity works alongside system design and infrastructure rather than replacing them. Training is unlikely to raise use where the system is poorly designed or the power supply unreliable, and a well-designed system will be under-used by a workforce that has not been prepared for it. The IHA report reaches the same conclusion: training without infrastructure or career pathways produces gains that do not last (IHA, 2026). The economic implication is that the workforce cannot be treated as an afterthought to technology spending. Each affects the return on the other.

Why Workforce Capacity Is Under-Financed

If workforce capacity shapes the value of digital investment, the next question is why it remains under-financed. Lack of guidance is not the reason. WHO’s Digital Implementation Investment Guide treats training, refresher training, supervision and the capacity-building needed to transfer systems to government as recurring costs, and identifies staff attrition as a cost driver (World Health Organization [WHO], 2020). It also warns that full costs are “frequently underestimated because budgets often focus on the costs related to the initial demonstration or deployment and do not take into account the resources needed for long-term operation and maintenance” (WHO, 2020).

The way digital health is commonly financed helps explain why this happens. A system is often introduced through a project with a fixed budget and a fixed end date. Initial training is included because it is needed for launch. The costs that come later fall outside the project’s life: refresher training, supervision, replacing staff who leave, and support once the system is in routine use. When the project closes, these costs need a new budget holder. If none exists, they go unfunded. The IHA report describes this pattern across the region: in-service digital health training is largely episodic and tied to individual donor-funded deployments, and it ends when project funding ends (IHA, 2026).

National budget data show how wide the gap can be. Sierra Leone’s digital health roadmap found that the digital health interventions it assessed were 83% donor-driven and 17% privately funded, with no significant public funding. It also found that the Ministry of Health’s 2022 statutory budget made no provision for digital health (Sierra Leone Ministry of Health and Sanitation, 2024). In such a setting, there is no domestic budget line to absorb recurring workforce costs once external funding ends.

Staff turnover compounds the problem. Where public career pathways for digital health roles are absent, trained staff may leave, and the value of their training leaves with them (IHA, 2026). Providers in Addis Ababa identified the turnover of trained staff as a barrier to sustained EMR use (Bekele et al., 2024). Each departure means either repeating the training cost or accepting lower use.

Project-bound financing and limited budget provision are therefore the most evident explanations for the gap between what WHO’s guidance recommends and what budgets fund. They are unlikely to be the only ones, but they are the ones the available evidence documents most clearly.

The Costs of Workforce Development

Recognising workforce capacity as an investment requirement does not mean that every training programme is economically justified.

Workforce development has two kinds of costs. The first is direct spending on trainers, materials, venues and travel. The second is less visible: the time health workers spend away from patients while they are being trained. That time is scarce. The WHO African Region has an estimated 46% of the health workers it needs (WHO Regional Office for Africa, 2026). Every training day therefore has a real cost in services not delivered. In facilities facing staffing pressures, training may require adjusted schedules, temporary cover or additional work for colleagues. These effects matter when estimating the full cost of an intervention.

There are costs to both investing in workforce development and failing to do so. Money spent on training cannot be used for other health needs, such as medicines, salaries or facilities. But when digital systems are underused because staff lack the skills or support to use them, the money already invested in those systems may deliver fewer benefits than expected. A sound investment decision must consider both costs. The challenge is that available evidence does not yet show clearly how much each costs, making it difficult to judge where resources would be best spent.

The cost of training also depends on how it is delivered. In Namibia and Tanzania, a course combining online and in-person learning cost US$287–437 per participant,compared with US$980 for a fully in-person course. However, the in-person course produced somewhat greater learning gains (Rudd et al., 2019). This shows why the cheapest training option is not necessarily the best investment.

The economic question is therefore not whether to train, but which approach achieves the required level of competence at the lowest total cost. Total cost here includes health workers’ time as well as direct spending. Answering it requires comparing models on cost and on what each actually achieves, not on price alone.

Implications for Appraisal, Budgeting and Evaluation

These findings point to four changes, each involving a different set of actors.

First, investment appraisals should include the full cost of workforce capacity. When a ministry of health or development partner assesses a proposed digital system, the appraisal should cost recurring workforce needs alongside hardware and software over the system’s expected life. No new methodology is required. The cost categories in WHO’s guidance, including refresher training, staff attrition, supervision and transfer of ownership, already provide one (WHO, 2020).

Second, these recurring costs need a home in domestic budgets. The IHA report recommends a dedicated Ministry of Health budget line for digital health workforce development, separate from infrastructure procurement and subject to annual reporting on expenditure and outcomes (IHA, 2026). WHO’s guidance recommends that the estimated annual cost of sustained operations be used to inform future government allocations (WHO, 2020). Together, these would give recurring workforce costs a budget holder once project funding ends.

Third, implementing partners should plan for the workforce before and after deployment. The IHA report recommends a structured workforce readiness assessment before any system is deployed, covering device access, connectivity, digital literacy by cadre and prior training. The assessment should also identify what is actually preventing effective use. If the main problem is poor connectivity or system design, more training alone will not solve it. This helps ensure that funding goes towards the problem that needs fixing. The IHA report also recommends a dedicated budget for workforce support for at least eighteen months after go-live, with that support transferred to a domestic institution before the project closes (IHA, 2026). Planned and funded in this way, the handover would no longer be a point at which support simply stops.

Fourth, evaluations should measure how far and how well systems are used, not only whether they are installed and functioning. They should also measure what that use achieves, including changes in data quality, staff workload, duplicated record-keeping and service delivery. High use does not necessarily mean that a system is delivering its intended benefits. Without this information, governments and their partners cannot answer the allocation question that matters most: whether the next unit of spending would achieve more if directed to training, supervision or workflow redesign, or to expanding the system further. Measuring both use and outcomes would help governments answer that question with evidence.

Conclusion

The economic case for costing digital health workforce capacity rests on a simple point. Digital health investments may deliver less than expected when the workforce lacks the skills and support needed to use them effectively. Yet the costs of developing and maintaining that capacity are not always adequately reflected in investment plans and budgets. WHO’s guidance already identifies these costs. The priority now is to ensure they are properly assessed, financed and evaluated. This would help governments and development partners make better decisions about where to invest limited resources and how to get greater value from digital health systems.

References

Bekele, T. A., Gezie, L. D., Willems, H., Metzger, J., Abere, B., Seyoum, B., Abraham, L., Wendrad, N., Meressa, S., Desta, B., & Bogale, T. N. (2024). Barriers and facilitators of the electronic medical record adoption among healthcare providers in Addis Ababa, Ethiopia. Digital Health, 10, Article 20552076241301946. https://doi.org/10.1177/20552076241301946

InnovateHealth Africa. (2026). The state of digital health readiness in the African health workforce: 2026 baseline. https://www.innovatehealth.africa/research

Keleb, G., Taye, G., Ayele, W., Tassew, B., Biruk, E., Habtamu, T., Getachew, D., & Addise, M. (2022). Electronic medical record utilization, determinant factors and barriers among healthcare providers at selected health facilities in Addis Ababa, Ethiopia. Ethiopian Journal of Health Development, 36(1, Special Issue). https://www.ejhd.org/index.php/ejhd/article/view/5577

Ministry of Health and Sanitation. (2024). Sierra Leone digital health roadmap 2024–2026. https://mohs.gov.sl/download/68/digital-health/18150/sierral-leone-digital-health-roadmap-2024-2026-2.pdf

Rudd, K. E., Puttkammer, N., Antilla, J., Richards, J., Heffron, M., Tolentino, H., Jacobs, D. J., KatjiuanJo, P., Prybylski, D., Shepard, M., Kumalija, J. C., Katuma, H. L., Leon, B. K., Mgonja, N. G., & Santas, X. M. (2019). Building workforce capacity for effective use of health information systems: Evaluation of a blended eLearning course in Namibia and Tanzania. International Journal of Medical Informatics, 131, Article 103945. https://doi.org/10.1016/j.ijmedinf.2019.08.005

Tolera, A., Oljira, L., Dingeta, T., Abera, A., & Roba, H. S. (2022). Electronic medical record use and associated factors among healthcare professionals at public health facilities in Dire Dawa, eastern Ethiopia: A mixed-method study. Frontiers in Digital Health, 4, Article 935945. https://doi.org/10.3389/fdgth.2022.935945

World Health Organization. (2020). Digital implementation investment guide (DIIG): Integrating digital interventions into health programmes. https://www.who.int/publications/i/item/9789240010567

World Health Organization Regional Office for Africa. (2026, May 6). Africa’s health workforce expands but shortages, unemployment and migration intensify: WHO report [Press release]. https://www.afro.who.int/pt/node/23190