Health is the sector where technology promises the most and carries the highest cost of getting it wrong. A telemedicine platform, a claims engine, a diagnostics company and a national health programme look like very different organisations, but they hold data that is more sensitive than almost any other category; they operate under overlapping regulators, and they are increasingly deploying technology into decisions that affect patient outcomes.
We work across four parts of this sector, and our role is different in each.
Care delivery and health technology. Hospitals, clinics, telemedicine, electronic health records, pharmacy and diagnostics platforms. Here we work across the full picture: structure, strategy, regulation, data, technology and governance.
Health insurance and financing. HMOs, claims platforms and the technology layer beneath them, where product design, regulation and automated decision-making meet.
Biotechnology and diagnostics. Our work sits in the commercial and contractual layer, including technology licensing, research collaboration and material transfer agreements, intellectual property ownership, funding arrangements, and the governance of clinical and genomic data. We do not advise on product approval pathways or clinical trial regulatory strategy; where clients need that, we work alongside specialists who do.
Health systems and development organisations. We work with implementing partners, advisory organisations, donors and programme-delivery bodies where technology, data or regulatory obligations sit inside larger health interventions. Our role is typically the enabling layer: data protection and information governance, AI and technology governance, technology procurement and contracting, regulatory analysis, and the compliance frameworks expected by funders, partners and regulators.
Health data sits within the sensitive-personal-data regime under the Nigeria Data Protection Act, bringing additional questions around lawful processing, security, risk assessment and accountability. Clinical, genetic and population-scale programme data can make those questions more complex still, particularly where information is shared across institutions or transferred across borders.
As artificial intelligence moves into triage, diagnosis, claims adjudication and programme targeting, the governance question stops being theoretical: who is accountable for the output, what the model was trained on, what happens when it is wrong, and whether any of it can be explained to a regulator or a donor. Deploying an AI tool inside a health programme is simultaneously a data protection question, a procurement question, a donor compliance question and a governance question. It is rarely well served by treating those as four separate engagements.