Building Distributed Health Data Systems: What Buyers Need to Know

In healthcare, data rarely sits neatly in one place. It lives in GP surgeries, hospital trusts, community clinics, and increasingly on patients' own devices. The ambition of a distributed data system—like our FHIR Cube Control Plane—is to connect these fragments into something coherent, resilient, and usable.

That ambition sounds simple. The reality is more complex. Buyers and leaders exploring this path need to look beyond the technology and into the practicalities of building a system that lasts.

Balancing Local and Central Needs

Distributed systems promise flexibility: every clinic or hospital can manage their own node, while data remains aligned with wider standards. But balance is everything. Too much local variation, and you end up with silos. Too much central control, and you lose the agility that distribution is meant to deliver.

The NHS, with its mixture of national mandates and local autonomy, is the perfect test case. Buyers need to ask: does the architecture give local teams enough freedom to act, while ensuring data remains interoperable and safe at scale?

Governance, Trust, and Resilience

Any system handling health data has to be trustworthy. Governance isn't a bolt-on—it's the operating system. From clinical safety checks to patient consent, distributed networks raise the question: how do you make sure every part of the system meets the same standard? Buyers should expect transparency: clear safety assurance, traceable decision-making, and auditable processes across every node.

And governance is about more than standards. Distributed systems also face practical risks:

• Power outages at local sites
• Network interruptions that break data flows
• Software failures that could cascade if not contained

Resilient governance means planning for these realities. Buyers should look for designs that degrade gracefully, isolate faults, and recover automatically. Without this kind of resilience, a distributed system risks becoming not just a new set of silos, but a fragile one.

Consent and the New Reality of Data Trust

Consent has always been part of healthcare. But in recent years, public awareness has shifted. The rise of big tech, high-profile data breaches, and controversial data-sharing programmes have made people far more conscious of where their information goes and who profits from it.

For buyers of distributed health systems, this is not an abstract issue — it's central to adoption. A technically brilliant system without a clear, patient-centred consent model will fail to gain public trust.

Key considerations:

• Granular consent, not blanket forms — Patients expect to decide which parts of their data can be shared, with whom, and for how long.
• Transparency over access — Patients want to see who has accessed their record, when, and why.
• Revocability — Consent must be reversible, with decisions respected across the system.
• Patient-controlled devices — Smartphones and wearables should allow consent management directly, not through complex forms.
• Trust through governance — Buyers should demand systems that prove consent is enforced: clear logs, auditable trails, and independent oversight.

This is more than compliance. It is about power and trust. Distributed systems offer the opportunity to move from a model where patients feel data is "taken" to one where it is entrusted — controlled by them, shared with clinicians, and protected by design.

For the NHS, where public legitimacy is as important as technical robustness, consent will be the deciding factor. Get it wrong, and distributed systems will be resisted. Get it right, and they will be embraced as a tangible sign of respect for patients.

The Patient and Clinician Perspective

Consider Sarah, a patient with a heart condition.

She visits her GP, who refers her to a local cardiologist. A month later, she ends up in A&E at a different hospital while visiting family. Each time, she repeats the same history: medications, allergies, previous tests. Paperwork is carried in her bag. Discharge notes arrive late or not at all.

Now imagine the same journey in a distributed data system. Her GP's notes, the cardiologist's ECGs, and the hospital's discharge summary are connected and available wherever she presents. Sarah doesn't have to be the messenger. Every clinician sees the same record, and she can see who has accessed it.

Meanwhile, consultants today complain of having to open three, four, sometimes five systems to establish a single view of their patient. Imaging in one portal, medications in another, GP referrals in a third. It slows down care and risks things being missed. In a distributed model, the patient record is no longer scattered—it's unified, accessible, and trustworthy.

And crucially, a distributed system also gives patients more ownership of their health journey. They can annotate notes, add symptoms, and record episodes between appointments—without always triggering costly and time-consuming eConsult processes. Clinicians see a richer, more continuous picture of health, while patients feel heard without extra burden falling on practice staff.

For patients, that means less repetition and more agency. For clinicians, it means less time chasing data and more time making decisions.

Cost Considerations: Centralised vs Distributed

When exploring new infrastructure, cost is always one of the first questions. It's tempting to assume that a single, centralised system will be cheaper than a distributed one. The reality is more nuanced. Both approaches carry cost, but the mechanisms differ.

Centralised

• Concentrates spend in one place: large data centres, enterprise licences, big-bang rollouts.
• Integration projects multiply as every local system has to feed into the centre.
• Hidden costs include staff time wasted switching systems or re-keying data.

Distributed

• Spreads spend across the network: local nodes with modest requirements, automated updates, and decentralised resilience.
• Costs scale predictably (add a node, not a new monolith).
• Delivers patient and clinician efficiency gains that translate into capacity and productivity.

Neither is inherently cheaper. But distributed offers a model that aligns better with the NHS landscape: complex, multi-organisational, and in constant change.

What Can Be Processed Where?

A common misconception is that distribution means duplication. In reality, it's about placing the right workload in the right place.

Workloads that can safely move to local devices

• Signal pre-processing (e.g. noise reduction on wearables)
• Daily rollups and summarisation
• Patient-added notes and symptom logs
• Simple threshold alerts (e.g. HR > 160 bpm)
• Consent management and data-sharing controls

Workloads that must stay centralised

• Clinical validation and decision support
• Cross-patient analytics and cohort studies
• Research and AI training at scale
• Long-term record storage with governance
• Complex event correlation across sites and modalities

For buyers, the distinction is important. Local processing reduces cost and empowers patients. Centralised processing ensures safety, governance, and system-wide learning. The value lies in drawing that boundary well.

Cautions for Buyers

Beyond cost, consent, and governance, buyers should keep in mind several practical cautions:

• Digital inequality — Systems mustn't assume every patient has the latest device or connectivity. Fallback pathways are essential.
• Clinical burden shift — Patient-generated data must be triaged and summarised, not dumped into clinician workflows as noise.
• Vendor lock-in — Even "open" solutions can hide proprietary hooks. Insist on FHIR UK Core and SNOMED CT alignment.
• Fragmented responsibility — Clarify accountability: who owns the node, who manages incidents, who is liable in a breach?
• Hidden operational costs — Budget for monitoring, updates, and long-term support, not just hardware.
• Cultural resistance — Trust is as much about people as systems. Training, communication, and change management must be built in.

These cautions don't argue against distribution — they highlight the conditions for success.

A Foundation, Not a Finish Line

A distributed health data system isn't an end in itself. It's a foundation—something that allows future innovation in clinical AI, population health, and patient-facing tools. Buyers should see it as an investment in flexibility, resilience, and trust.

If it works well, the payoff is clear: patients like Sarah no longer have to be their own messengers, consultants no longer juggle half a dozen log-ins to see the full story, and the health system moves forward with confidence.