Challenges in Health Data Management

Why Most Health Data Lies Dormant

We’re all aware that healthcare systems — in Finland and around the world — are under increasing pressure. Between resource constraints and an aging population, the challenges are mounting. Fortunately, technology continues to offer new ways to help.

One key area that stands out in our research projects is data management. People now generate huge amounts of health data through smartwatches, rings, and other wearables. Add to that the data produced during every healthcare interaction, and it’s clear we’re not short on information.

And yet, up to 97% of healthcare data goes unused (World Economic Forum 2024).

Key Barriers: Silos, Privacy & Standards

The real challenge isn’t a lack of data — it’s integrating it in a secure, compliant, and meaningful way. If we can connect the dots, we unlock the potential for AI-powered insights: earlier detection of illness, more accurate diagnoses, and truly personalized health plans.

Breaking down data silos isn’t just a technical goal — it’s a major step toward more preventive, efficient care that eases the load on healthcare professionals.

Regulatory Reality Check

Any discussion of integration must fit inside Europe’s tightening privacy and interoperability frameworks:

  • GDPR sets the baseline for lawful, transparent processing and patient consent.
  • The upcoming European Health Data Space (EHDS) aims to standardise formats, boost secondary-use research, and empower individuals to control their records.
  • Finland’s own Kanta Services already enable nationwide e-prescriptions and patient summaries, providing a springboard for broader innovation.

By aligning solutions with these rules from day one, we avoid costly re-engineering later and build trust with patients and professionals alike.

Opportunity: Turning Raw Data into Real-World Impact

  1. Earlier Detection – Continuous streams from wearables can flag subtle changes long before symptoms prompt a clinic visit.
  2. Precision Diagnostics – Integrated lab, imaging, and lifestyle data feed AI models that narrow differential diagnoses and reduce unnecessary tests.
  3. Personalised Care Plans – Algorithms combine genetics, activity levels, and treatment history to recommend therapies tailored to each patient.
  4. Lighter Clinician Workloads – Unified dashboards replace manual record-hunting, giving nurses and physicians more time for high-value tasks.
  5. System-Level Insights – Aggregated, de-identified datasets guide policymakers toward preventive strategies that ease long-term cost pressures.

About the author

Kira is a Research Analyst & Consultant at Catapult, analysing data across diverse industries and sectors.

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