SERVICES

Digital Health, Medtech & AI 

Zeumed works on digital health, medtech, and AI from a system and decision-making perspective, focusing on the frameworks and economic logic that determine whether technologies can be adopted, funded, and scaled in practice.

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This work spans both the innovator side (commercial and adoption design) and the payer side (assessment and payment frameworks), recognising that misalignment between the two is a major source of failure.

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In practice, this includes:

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• Commercial and adoption design for digital health and medtech
Supporting digital health and medtech teams to design products around realistic adoption and funding routes - including who ultimately pays, on what basis, and what economic and system evidence must be demonstrated.

This work is often delivered through structured partnerships with product and engineering teams, including Zeumed’s collaboration with ULAM Labs, to ensure commercial and system considerations are embedded early in product design.

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• Frameworks for assessing the value of AI-enabled technologies
Designing structured frameworks for evaluating AI-enabled technologies that go beyond narrow clinical endpoints. This includes assessing value at multiple levels - direct clinical or operational impact, system-level effects (such as capacity, utilisation, and workflow change), and wider economic or spillover effects, reflecting how AI technologies generate value in practice.

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• Payment and funding approaches for AI-enabled technologies
Advising on how AI-enabled technologies can be paid for within healthcare systems, where traditional reimbursement models are often not applicable. This includes assessing options such as procurement-based funding, bundled or service-level payments, and other payer-led approaches, and the implications these choices have for pricing, evidence expectations, and market behaviour.

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