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Health research data: unlocking the future of evidence-based medicine

· 7 min read

Understanding the value of structured health data in research

What are health research data?​

Health research data is any data used to produce knowledge that enhances health outcomes, policies, and medical understanding. It includes clinical, administrative, genomic, and lifestyle data, collected from sources like electronic health records (EHRs), wearable devices, patient registries, and insurance claims.

Structured data - organized and standardized for interoperability - is essential for meaningful research. Without common formats or terminologies, data becomes siloed and unusable across systems.

Key types of health data​

  • Electronic Health Records (EHRs): Clinical documentation from care providers
  • Claims Data: Administrative data from insurers
  • Registries: Disease-specific databases (e.g., cancer registries)
  • Patient-Generated Data: From wearables or health apps

A closer look: when data tells a life-saving story​

Imagine this: A 56 year old man in Berlin uses a smartwatch to monitor his heart rate during regular walks. One evening, the device detects an irregular rhythm and notifies his physician. Because the clinic participates in a regional health research program using standardized data platforms, the man's wearable data is immediately linked to his medical history, genetic predispositions, and past lab results. Within hours, doctors identify early signs of atrial fibrillation.

What’s remarkable isn’t just the real-time insight - but the complex ecosystem working quietly in the background. Today, health data are generated almost everywhere: during hospital stays, at pharmacy counters, through insurance claims, mobile health apps, genetic screenings, lab reports, and even fitness trackers. In theory, these data offer a holistic view of our health. In practice, however, they are often scattered across incompatible systems, locked behind silos, or used in ways that patients can’t trace.

Now imagine a different scenario: A future where your health data - whether collected in a university clinic, a local pharmacy, or a smartwatch - is securely stored, harmonized to a common format, and transparently shared (with your consent). In this world, researchers work with up-to-date, diverse, and representative data sets. Clinicians receive AI-supported alerts based on longitudinal health records. Public health policies respond to real-time trends rather than outdated statistics.

This is the vision structured health research data enables: a landscape where data improves healthcare. A world where data is not just technical infrastructure, but personal empowerment. And where every patient, regardless of age or origin, benefits from a smarter, more responsive system of care.

For the patient and their clinician, this means one thing: faster help, better outcomes.

Why health data matters in research​

Health data has become a crucial asset for advancing medical science. In the past, researchers often relied on small cohorts or clinical trials with narrow scopes. Today, the integration of broad, real-world health data sources opens new possibilities for faster discoveries, more equitable outcomes, and better decision-making in both public health and clinical care. The shift toward data-driven medicine means research no longer starts with hypotheses alone—it begins with data. Large-scale, high-quality health data enables:

  • Evidence-based medicine (EBM): Validates clinical decisions with real-world insights
  • Outcome tracking: Monitors long-term effects of treatments and policies
  • Population health research: Identifies trends across demographics and regions
  • Personalized medicine: Tailors treatments based on individual characteristics

Use case: longitudinal cohort studies​

Projects like the UK Biobank and the "All of Us" Research Program in the U.S. demonstrate how structured data across diverse populations can lead to breakthroughs in chronic disease, mental health, and cancer research.

Standards and interoperability​

For data to be useful at scale, analysts using it must be able to 'speak the same language' across institutions, countries, and technologies. This is why interoperability and international standards play such a crucial role in modern health research. Otherwise, valuable datasets remain isolated and insights through research are delayed. To make health data reusable, it must be standardized. Two leading data model frameworks are:

  • OMOP Common Data Model (CDM): Enables uniform representation and analysis of health data across institutions
  • FHIR (Fast Healthcare Interoperability Resources): Facilitates real-time data exchange and modular health data access

Together, these data models support data harmonization, allowing research projects to collaborate across borders and disciplines.

Tools & frameworks​

  • OHDSI ATLAS: Open-source platform for cohort building and phenotyping
  • LOINC, SNOMED CT, ICD: Medical terminologies for consistent classification

Technology enablers​

As the volume and complexity of health data grows, so does the need for advanced technology to handle it. From secure cloud platforms to AI-based analytics and semantic frameworks, technology enables researchers to transform raw data into actionable insight.

Data4Life’s infrastructure and research collaborations aim to reduce the translation time between scientific discovery and real-world medical application.

Collecting and using health data for research is not just a technical task - it’s a deeply human one. Trust, transparency, and respect for patient rights are essential. Navigating this space requires clear regulations, strong ethical frameworks, and practical tools that enable compliance without stifling innovation. Respect for patient autonomy, data security, and public trust are also critical in research. Some best practices for handling data collection and sharing are:

  • Obtain informed consent
  • Use pseudonymization or anonymization
  • Maintain transparent governance models
  • Enable participant control over data sharing

There are also various regulations across the world governing data collection and usage:

  • Europe: General Data Protection Regulation (GDPR)
  • United States: Health Insurance Portability and Accountability Act (HIPAA)
  • South Africa: Protection of Personal Information Act (POPIA)
  • Singapore: Personal Data Protection Act (PDPA)
  • China: Personal Information Protection Law (PIPL)
  • Australia: Privacy Act

Ethical handling of data is not only a legal requirement—it’s a moral imperative and essential to building sustainable, trustworthy research infrastructure.

Real-world examples​

Global initiatives and Data4Life’s own research partners show what’s possible when ethical governance, robust technology, and collaborative science come together. These examples highlight the real impact of data-driven research on individuals and society.

  • UK Biobank: Half a million participants contributing to research in dementia, cancer, and heart disease
  • All of Us (NIH): Integrates EHRs, genomics, and digital health for diverse, inclusive biomedical research
  • AIR·MS (Data4Life): Leveraging real-world data and AI to personalize multiple sclerosis research

Challenges and future outlook​

Despite promising advances, health data research still faces systemic obstacles. Addressing them requires a joint effort from policymakers, technologists, researchers, and patients alike. Only then can we move toward a fair, responsive, and intelligent health data ecosystem. Some of the challenges include:

  • Data fragmentation: Disparate systems and formats
  • Bias and representativeness: Skewed datasets can perpetuate inequality
  • Technical barriers: Integration and scaling of complex data
  • Personal issues: Unwillingness to share data

The future lies in collaborative ecosystems where interoperability, ethics, and technology converge. At Data4Life, we envision a world where health data is not just collected—but used meaningfully, respectfully, and globally.

FAQs​

What are health research data?
Any data used in medical, clinical, or public health research, such as EHRs, claims, and patient-reported data.

How are these data used?
They support disease modeling, treatment evaluation, policy planning, and more.

What standards are used?
OMOP, FHIR, SNOMED CT, ICD, and LOINC are commonly applied for structure and classification.

Are health research data secure?
Yes - if managed under regulations and with technical safeguards such as encryption and anonymization.

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