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Independent assessments · Clinical data

I spent years building healthcare data platforms. Today I use that experience to assess them.

I help healthcare organisations establish whether their data, integrations, applications and infrastructure are ready before investing in AI, EHDS or a new data platform.

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About me

Trained as a biomedical engineer and specialised in data architecture and analytical systems, I combine clinical domain knowledge with technical expertise in data platform design.

I have taken part in projects for healthcare organisations including Osakidetza, Roche, AstraZeneca, Evinova, EMA and Boehringer Ingelheim, mostly as part of consulting teams, designing data systems that support regulated environments, clinical interoperability and advanced analytics.

Today I apply that experience in the opposite direction: I do not design the platform. I assess whether the one you have and the organisation around it are ready for what you want to do. I identify architecture, semantic and governance risks before they become technical or regulatory bottlenecks, and show you the evidence behind what must be resolved before investing.

I work at the intersection of data architecture, applications, integrations, cloud and the operational readiness required for clinical AI.

I primarily work with CIOs, CTOs, digital transformation directors and Data and AI leaders who need to decide whether an initiative can proceed, and what to prioritise if it cannot yet.

Want to know whether your organisation is ready for its next initiative? → Let's talk

The problem

What I detect

  • Platforms that don't scale. Organisations that built their data platform without thinking about scalability. As projects grow, every new integration or feature becomes costly and slow.
  • Ad-hoc clinical integrations. Without a clear interoperability strategy, every integration with a hospital or clinical system requires bespoke development. Cost and time spiral.
  • Data not ready for AI. Mixed operational and analytical data, inconsistent models and improvised pipelines block the implementation of advanced analytics and AI.
  • Unidentified technical risks. Architecture problems that are not visible until they impact projects: accumulated technical debt, scaling bottlenecks or regulatory risks.

The process

How I work

  1. 01
    Understand. The initiative you want to pursue, the decision you need to make and what is blocked today.
  2. 02
    Evidence. Analysis of architecture, clinical data model, semantics, pipelines, integrations and governance, using documentation, systems and interviews, not opinions.
  3. 03
    Diagnosis and roadmap. A clear readiness conclusion, prioritised risks and what must be resolved before investing. Sometimes the answer is 'not yet'.
  4. 04
    Decision support (optional). I can support the decision and review delivery against the roadmap. Implementation remains with your team or chosen provider, keeping my assessment independent from whoever delivers it.

Clients

Who I work with

I work with healthcare organisations that:

  • Have data, AI, EHDS or analytics projects that aren't progressing as they should
  • Need to scale hospital integrations without every one becoming a bespoke project
  • Are trying to build a reusable clinical data foundation, or need the external sources they consume to have sufficient semantic quality to be useful
  • Have seen a clinical AI, RWE or interoperability project fail due to data quality or coherence
Private hospital groupsRegional health servicesMid-large pharmaReal-scale HealthTechs (50+ people)Real world data platforms

Specialisation

Areas of expertise

Data architecture

Assessment of data platforms, operational/analytical separation and pipelines.

Clinical interoperability

Integration with hospitals and clinical systems, FHIR and OMOP standards.

Analytical platforms

Enabling advanced analytics and visualisation in clinical data environments.

Preparation for AI

Assessing whether the data, platform and governance can take advanced analytics and AI into production.

Regulation and EHDS

Readiness for the European Health Data Space: dataset cataloguing, data quality and secondary use in regulated environments.

Experience

Experience across healthcare and pharmaceutical organisations

I have worked on projects for healthcare and pharmaceutical organisations, mostly as part of consulting teams at Accenture, Minsait, Pragsis Bidoop or M2C.

RocheAstraZenecaBoehringer IngelheimEvinovaOsakidetzaEMASegurCaixa Adeslas

The names mentioned belong to their respective owners. They describe professional experience and do not imply sponsorship, a current relationship or endorsement of this service.

Applied experience

Data architecture on real projects

I have taken part in data architecture, analytics platform and data governance projects for healthcare and pharmaceutical organisations, contributing to the design of cloud platforms, data models, systems integration and analytics capabilities.

For confidentiality reasons, these examples are anonymised and describe only the responsibilities and deliverables I was directly involved in.

Analytics platform for a regional health service

Modernisation of an analytics platform with thousands of tables from clinical and administrative systems, evolving towards a Lakehouse architecture on AWS. As data architect, I defined the technical standards for pipelines and data models, the semantic layer for analytical datasets, and the permissions strategy with Lake Formation and IAM for consumption from BI tools such as QuickSight.

AWS LakehouseBronze / Silver / GoldLake FormationIAMQuickSight

Data platform for an international pharmaceutical company

Evolution of a corporate AWS platform for commercial and analytical processes around business and product data, including integration with sources such as Salesforce. I combined ETL development with architecture design: data models for analytical consumption, architecture standards and pipeline optimisation, working alongside international business teams.

AWS ETLSalesforceData modellingPipelines

Beyond any specific technology, my work is about turning complex data platforms into maintainable, governable architectures that are ready to evolve, balancing technical, operational and business considerations.

Is your organisation ready for what comes next?

Three assessments, one underlying question: EHDS, clinical data or AI. In a short call, I'll tell you which fits, or whether none makes sense yet.

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