This contributor article by Chinaza Esiaba was first published on TechCabal Insights.

For years, antimicrobial resistance (AMR) in Africa has been described through increasingly stark statistics. AMR occurs when bacteria and other microbes evolve to resist the medicines used to treat them, making common infections more difficult to cure and, in some cases, untreatable.

A Lancet analysis estimated that western sub-Saharan Africa recorded the world’s highest death rate attributable to bacterial AMR in 2019, at 27.3 deaths per 100,000 people. The figure demonstrates the scale of the threat, but it also highlights a paradox: in places where resistant infections are a major concern, the laboratory evidence needed to monitor them is often limited.

The World Health Organisation (WHO) estimates that one in five laboratory-confirmed bacterial infections in its African Region in 2023 was resistant to antibiotics. That estimate cannot account for infections that are never tested. The visibility problem extends beyond Africa: 48% of countries did not report resistance data to the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2023, and about half of the countries that did report lacked systems capable of producing reliable data.

This is both a public health challenge and a health technology problem. AI diagnostic and prescribing tools depend on laboratories that can test infections, records that connect results to patients, and surveillance systems that turn individual tests into evidence about local resistance. For governments and investors, establishing those connections may be the more immediate opportunity.

Clinicians need diagnostic evidence to prescribe well

The Nigeria Centre for Disease Control and Prevention says that one in two hospitalised patients receiving antibiotics is treated with more than one antibiotic. Antibiotics with a significant impact on AMR, including ceftriaxone, cefuroxime and ciprofloxacin, also account for a large share of prescriptions.

Nigerian studies have found high levels of empirical prescribing, meaning treatment is started before laboratory evidence identifies the organism or confirms which antibiotic will be effective. Dr Ifeyinwa George, a pharmacist and AMR programme manager at DRASA Health Trust, a Nigerian nonprofit focused on infection prevention and public health security, has identified limited laboratory capacity as a barrier to detecting and tracking resistant pathogens.

An Africa CDC-led study of 14 countries found that only 1.3% of approximately 50,000 laboratories in participating networks performed bacteriology testing — the laboratory work of culturing and identifying bacteria from patient samples. Of roughly 187,000 samples tested for resistance, 88% lacked clinical information such as the patient’s diagnosis or previous antibiotic use.

A survey of 219 Kenyan health facilities found that 61.6% did not offer bacterial culture testing, while only 16.9% performed antimicrobial susceptibility testing, which checks whether a given infection responds to particular antibiotics. As Ghanaian AMR researcher Prof Beverly Egyir put it, “When we do not test, we are essentially flying blind.”

The gap can persist even when testing is available. At Cape Coast Teaching Hospital in Ghana, laboratory reports took an average of 3.4 days from sample receipt to upload into the electronic health system.

These figures can make AMR appear primarily to be a problem of antibiotic overuse. Africa’s experience is more complicated. Without diagnostic evidence, clinicians may prescribe empirically because they lack information, rather than simply because they disregard antimicrobial stewardship. Providing better information functioning laboratories, timely results and systems that connect those results to patient records.

Medicine quality is another part of the data gap

Resistance surveillance also needs to account for the medicines patients receive. In 2025, Ghana’s Food and Drugs Authority found counterfeit pharmaceutical products worth GH₵42 million ($3.6 million). Nigeria’s National Primary Healthcare Development Agency has also stated that about 70% of medicines distributed in the country were substandard or counterfeit.

Substandard medicines containing too little active ingredient can expose bacteria to inadequate concentrations of treatment and contribute to resistance. This creates a need for better medicine traceability and reporting.

Nigeria’s National Agency for Food and Drug Administration and Control (NAFDAC) operates a Mobile Authentication Service that allows consumers to check medicine codes by text message. Batch tracking, rapid alerts and shared records can help regulators identify suspect products and remove them from circulation.

The WHO is pursuing a similar direction. Its 2026 digital-transformation guidance specifically encourages countries to move from fragmented, paper-based reporting of substandard and falsified medicines to interoperable digital reporting systems. These tools form part of the AMR response because medicine quality affects the treatment a patient actually receives.

When the data infrastructure exists

Some countries are building systems that make more advanced analysis useful. South Africa’s National Institute for Communicable Diseases publishes an AMR surveillance dashboard based on laboratory data that helps show which pathogens are becoming resistant.

In Ghana, researchers using whole-genome sequencing, which maps the full genetic makeup of a pathogen, have studied cholera isolates alongside samples from other African countries to examine resistance genes and the relationships between strains. Such methods can answer more precise public health questions when sample collection and routine data are sufficient to support them.

Kenya offers a practical example of how this foundation can develop. According to the WHO’s account of the programme, two model surveillance sites established in 2017 grew into a network of 32 sites across 27 countries. Kenya now integrates resistance, antibiotic-use and consumption data into a central warehouse and began submitting individual-level data to GLASS in 2025.

Susan Githii, Kenya’s GLASS focal person, says the richer data help the team understand resistance trends and guide decisions. The remaining work includes connecting hospital systems and filling missing patient fields.

The investment test

The lesson is not that Africa should abandon AI, rapid diagnostics or clinical decision-support software. These tools need a reliable foundation. For healthtech investors, the AMR opportunity may lie in systems that make laboratory evidence usable: software that delivers susceptibility results to clinicians, connects data across hospitals and helps regulators trace suspect medicine batches.

AI could use that foundation to flag unusual resistance patterns or support treatment decisions. It cannot compensate for infections that were never tested or medicines whose quality is unknown.

The more difficult question is who will keep this infrastructure operating. The EU-backed ARILAC initiative is set to strengthen laboratory and data systems in eight countries over four years. At its launch in July 2026, Africa CDC called for those systems to be included in national budgets. Its 2026–2030 AMR framework also identifies sustainable financing as a priority. ARILAC’s four-year window and the framework’s 2030 horizon set a concrete timeline against which the durability of that infrastructure can be judged.

Laboratories need reliable supplies, maintained equipment and skilled staff long after a grant ends. Investors need a credible path for health systems to pay for and continue using the tools they fund.

Technology alone will not slow resistance. Infection prevention, vaccination, clean water and responsible antibiotic use remain essential. But the strongest investment case may be in infrastructure that tells clinicians which treatments still work locally, shows public health teams where resistance is rising, and helps regulators prevent poor-quality medicines from reaching patients.