After Go-Live: What We Know About Digital Capability in Nigerian Health Programs

Health workers are the point where digital technology meets the health system. Whatever a tool promises, it delivers only through the person using it. So when Nigeria introduces electronic medical records, surveillance platforms, immunization apps and data systems into hospitals and public health programs, the question that decides whether the investment pays off is not whether the tool was deployed. It is whether the people expected to use it can.

This review set out to find what the public record says about that question. Eight digital health programs in Nigeria were examined, four in clinical care and four in public health. Each introduced a technology and trained health workers to use it. For every program the same questions were asked. What did the training look like? Was workforce capability measured? Was use measured? What happened after the funding ended? Where an answer could not be found in publicly available sources, it is recorded as not publicly available. That absence turned out to be one of the findings.

What the record shows, in one line

Across the eight programs, deployment and reach were far more readily reported than any evidence that workers acquired, retained or applied digital skills. The Lagos primary care digitisation, led by the Lagos State Primary Health Care Board with the Clinton Health Access Initiative and supported by the Gates Foundation, reported 100 centres digitised at its Phase II review and documented a three-day training for facility Digital Champions; the EMR vendor's chief executive has stated in an interview that approximately 1,500 health workers were trained, a figure recorded here as a vendor claim. The EMID immunization data program reported 20,025 health workers trained across Nigeria through cascade training between September 2023 and mid-2024, according to eHealth Africa's 2024 mid-year report, and 39,932 children whose routine immunizations were recorded on the system during its five-month pilot. Federal Medical Centre Ebute-Metta reported 1,200 staff using its EMR. Rivers State reported 200 computers distributed to 50 facilities alongside the first phase of a cadre-based EMR training and, beyond that, almost nothing. These are measures of exposure and reach. None of them is a measure of capability.

One partial exception has emerged in Lagos: an independent evaluation partner has collected midline data on EMR use and adoption across the digitised centres and presented findings to the state. As of this review, those findings had not been published in full. Measurement happening without publication is a different finding from measurement not happening, and the distinction matters, because it shifts the question from whether capability is valued to whether the evidence ever reaches the public record.

The one program that measured it

One program stands apart. SORMAS-Nigeria, the disease surveillance and outbreak management platform developed with the Helmholtz Centre for Infection Research and piloted with Nigeria's field epidemiology network, ran an intensive two-day training with sessions from 8:30 in the morning to 5:00 in the evening, combining practical group work with discussion of organizational issues and evaluation of the pilot itself. Participants were assessed before and after: mean scores rose from 10.57 to 17.96 across the 106 participants assessed, a statistically significant improvement, publicly documented along with the challenges that surfaced during implementation.

It is also the only program in this review that went on to national adoption. SORMAS became Nigeria's surveillance and outbreak management system, reaching 15 states, 287 local government areas and roughly 700 users covering some 75 million people by late 2019, with health worker training built into the expansion. Whether measurement caused that success is more than the public record can prove. What the record does show is that the program with the strongest evidence trail is the one that grew.

What training looked like everywhere else

Training was present in nearly every program, and its design varied widely. Federal Medical Centre Abeokuta documents an institutional approach: a dedicated EMR unit established in 2022 drawing staff from departments across the hospital, regular re-training the hospital describes as ongoing, and a feedback channel for resolving staff complaints promptly. FMC Ebute-Metta used cross-institutional knowledge transfer, sending staff to observe a working EMR at a sister hospital and hosting a specialist from that hospital during its implementation. The EMID program used a cascaded training-of-trainers model running from national to state to local government level. Several programs did not publicly state their training method at all.

What almost none of them reported was what happened next. Most programs describe a single training event. Whether it was followed by refresher training, mentorship, onboarding for new staff or ongoing technical support is, in most cases, not publicly recorded. Where the record does speak, it confirms the concern. EMID's peer-reviewed evaluation documents temporary pandemic-era recorders with limited digital experience leaving for permanent jobs without proper handover, with the knowledge and capacity loss that follows, and concludes that ongoing training, retraining and continuous supportive supervision are essential. The cascade itself is similarly undocumented: how much knowledge survived each step from national trainers to state trainers to the recorders who actually use the tool is not publicly recorded.

Adoption is not capability

The clearest illustration comes from FMC Abeokuta. The hospital reported a successful transition from paper to EMR, with reduced paperwork, improved data accuracy and better teamwork. Studies of the same hospital complicate the picture. In one cross-sectional survey, 41.67 percent of respondents cited inadequate training, alongside reports of interface problems and system crashes, even as majorities in the same survey reported reduced paperwork and better patient safety. A second study reported training sessions clashing with clinical duties and a steep learning curve for staff with limited computer experience.

Both accounts can be true. A hospital can move off paper and still fall well short of what the tool could do. The improvements being reported are measured against pen and paper, not against what a well-used EMR would deliver. That is the distinction this review keeps returning to: system adoption is not equivalent to workforce capability, and evidence of training exposure exceeds evidence of capability acquisition.

What the evidence trail tells us

The thinness of the record is itself informative. When eight programs, several of them national in scale and backed by major institutions, leave almost no public trace of whether their training worked, it says something about how workforce capability is valued relative to deployment. Where outcomes were reported at all, they were mostly descriptive: improved data quality, improved patient care. A few concrete measures exist. The EMID program recorded data entry time falling from three to five minutes per client to about two. FMC Ebute-Metta went on to become a reference point for other public hospitals; in May 2025 its outgoing medical director put the number learning from its systems at over 50 nationwide. But quantified evidence of health worker capability, assessed before and after training, was found in one program out of eight.

What building capability would take

Health workers' digital capability should be treated as a longitudinal outcome, not a one-time skill acquisition. A worker can attend a training session and still be unable to perform required tasks independently, troubleshoot common problems or fit the tool into daily workflow. Counting attendance does not tell you whether any of that happened.

The mechanisms that would close the gap are not exotic. Refresher training. Onboarding processes that include the digital tool for newly recruited staff. Continuous technical support. Supervision. Clear institutional responsibility for workforce development. SORMAS shows that at least the first of these has already been done in Nigeria, with results. The open question is why it has not been repeated.

About this review

This is a desk review of eight digital health programs in Nigeria, conducted over five weeks in August and September 2026 using publicly available sources only: program websites, published reports and evaluations, peer-reviewed studies, news coverage and official announcements. No program staff were contacted. Findings describe what is publicly documented, not what programs may have done internally without publishing. Figures cited in this article were re-verified against primary and best-available public sources in September 2026. Where a figure originates from a program, vendor or official statement rather than an independent evaluation, it is identified as such. The eight programs were selected for diversity across setting, funder and scale, and are not a representative sample of Nigerian digital health initiatives. Full program-level sources are listed at the end of this article.