The Data Silo Problem in Healthcare
The average healthcare organization in the Netherlands operates across 15 to 25 different software systems. From electronic client and patient records to scheduling software, billing systems, client portals, and reporting tools — each system holds a piece of the puzzle, but none provides the complete picture.
Patient data becomes fragmented across dozens of databases that do not communicate with one another. A general practitioner sees different information than the hospital specialist, and the community nurse lacks both perspectives. Clinicians spend valuable time searching for information rather than delivering care.
The absence of a single, coherent client view also means that patterns remain invisible. The data exists — but the insights do not.
“Connected data is the key to proactive, personalized care.”
The Value of Connected Health Data
When health data is connected, something powerful emerges: patterns become visible, insights come to light. Connected data is the key to a fundamentally different approach to care — proactive rather than reactive, personalized rather than generic.
When a clinician can view the complete medical history, active treatments, and current measurements at a glance, clinical decisions are better substantiated. By combining data from multiple sources, anomalous patterns can be identified at an early stage.
At an aggregate level, connected data reveals trends in disease burden, risk factors, and care utilization. This enables executives and policymakers to deploy capacity, prevention initiatives, and quality improvement programs with greater precision.
Privacy-by-Design as the Foundation
Connecting health data carries significant responsibility. Health information is among the most sensitive personal data that exists. Privacy-by-design is therefore not an add-on, but the foundation upon which the entire ecosystem is built.
All data exchange complies with GDPR. Data processing agreements, DPIAs, and lawful bases are embedded in every process. Affiliated organizations operate in accordance with NEN 7510, the Dutch standard for information security in healthcare, with auditable technical and organizational measures.
Privacy and data-driven care are not conflicting objectives. With the right architecture, they reinforce each other: trustworthy data processed securely leads to better care and greater client confidence. Organizations that minimize data, pseudonymize where possible, and are transparent with clients about how their data is used build a data practice that is not only compliant but also sustainable.
Architecture: What Does Connected Data Look Like in Practice?
The simplified notion of 'connecting data' often implies placing all data in one location — a central data lake or master database. In healthcare, that is rarely the right choice. Centralization creates a single point of failure (both technically and legally), complicates version management, and frequently conflicts with GDPR purpose limitation.
The viable architecture is federated: data remains in the source system but is accessible via a standardized layer (FHIR, HL7) for specific purposes, with explicit consent and purpose limitation per query. No copies, but access. No central silo, but standardized exchange.
On top of that sits an access and consent layer: who may view which data, on what legal basis, and for what purpose? In a modern healthcare architecture, this is not buried in code but governed explicitly and in an auditable manner. Clients can see — and control — who accesses their data and for what purpose.
Use Cases by Care Segment
Connected health data operates differently across each sector. In mental healthcare, the greatest gains come from maintaining treatment history across mental health providers, general practitioners, and specialist care. A client presenting in crisis to a new clinician does not need to recount their history — the relevant context is immediately available.
In elderly care and home care, the focus is typically on caregiver coordination and multidisciplinary collaboration. When the general practitioner, community nurse, physiotherapist, and informal caregiver all work from the same factual basis, conflicting advice, duplicate appointments, and missed signals disappear.
In youth care, connected data requires additional attention: parental authority relationships, municipal governance, and Youth Act legal bases all intersect. Nevertheless, the benefits are substantial — it significantly reduces the number of 'first-conversation-with-the-second-clinician' moments where client information must be gathered from scratch each time.
What a Strong Start Looks Like
Many healthcare organizations embark on a 'data strategy' that turns out in practice to be a data lake project. A strong start looks different. Begin with a concrete use case where value delivery is clear — for example, medication transfer between a general practitioner and a specialist, or sharing care plans between an electronic client record and a specialist application. One use case, one pair of systems.
Conduct the DPIA upfront, not retrospectively. The DPIA compels explicit articulation of purpose limitation, data minimization, and legal basis — questions that otherwise only surface when a project is nearly live and the legal costs are exponentially higher.
Build federated, not centralized. An intermediary layer that exposes FHIR resources from existing systems delivers the same functionality as central storage, but without the legal and operational complexity. Copy only what is strictly necessary — for example, for analytical purposes — and always with a separate legal basis.
And measure the value. What time savings did the first connection deliver? How frequently is the connected data actually used? Which decisions concretely improved? Without that measurement, 'connected data' remains an aspiration without accountability; with it, it becomes a capability that sells itself to the next department.
