AI does not fail in the model. It fails in the organisation around it.
Hospital and clinic leadership, health authorities, medical faculties and health-technology companies.
Healthcare carries the strictest version of every problem we work on: high-stakes decisions, sensitive data, and a public that will not accept an unexplainable model. The blocking factor is rarely the technology. It is whether anyone has decided who owns the risk.
Four services, with the time each takes
AI maturity assessment
Where the organisation actually sits across informal, functional, integrated and strategic use, and what each level exposes it to. Delivered as a diagnostic leadership can act on, not a score.
Clinical & administrative AI governance
Risk classification, human-oversight design and documentation for AI in triage, diagnostics and administrative decision-making, built for the deployer obligations rather than the vendor's.
Health data governance & EHDS readiness
Secondary use, consent architecture and cross-border flows, assessed against GDPR and the European Health Data Space.
Outcomes & wellbeing measurement
Building measures that hold up analytically, drawing on our work on Nordic wellbeing indices and subjective measurement.
What is driving the question here
EU AI Act
Most clinical decision support falls in the high-risk category, and Article 26 puts named obligations on the deployer, not only the vendor.
EHDS
Regulation (EU) 2025/327 changes what secondary use of health data requires, and rewards organisations whose data was governed before they needed it.
MDR overlap
Where a model becomes a medical device, and what that adds to the compliance path.
Informal use
Staff already using free accounts on clinical material is the most common exposure we find, and the one least likely to appear on a risk register.
Work in this sector
We designed and delivered the healthcare AI leadership session of an executive course run by AuroraMed with the Faculty of Medicine and Biomedical Sciences at the University of the Algarve, including the maturity model participants applied to their own organisations.
Leading the change for AI in healthcare
Universidade do Algarve, Faculdade de Medicina e Ciências Biomédicas · AuroraMed
The healthcare AI session of an executive course run by AuroraMed with the Faculty of Medicine and Biomedical Sciences at the University of the Algarve. Phibon designed and delivered the session on leadership, governance and organisational maturity.
Healthcare · Public sector
Nordic wellbeing & sustainability programme
Northeastern University
Six lectures on subjective wellbeing, wellbeing economics and sustainability, built around the World Happiness Report and the OECD Better Life Index for an honours cohort.
Public sector · Healthcare
Three ways to start
Scoping review
2 weeks · fixed fee
We establish what you are actually deciding, what evidence you already hold, and whether the question needs us at all.
- Two structured sessions with your team
- Review of existing documentation and data
- A written scope with options, effort and sequence
Assessment
6–10 weeks · fixed scope
A structured review against a defined standard — regulatory, methodological or strategic — returned as a prioritised action list rather than a report that sits on a shelf.
- Baseline against the applicable framework
- Benchmarking against comparable organisations
- Findings ranked by exposure, with owners and sequence
- A workshop handing the findings to the people who act on them
Retained advisory
6–12 months · monthly
For organisations under continuing regulatory or geopolitical pressure, where the question changes faster than a project can answer it.
- Standing access for named decision-makers
- Quarterly horizon briefings on your specific exposure
- Board and committee material on request