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Open Data in Health Research: Making Science Transparent and Collaborative

· 7 min read

Unlocking the potential of shared knowledge for global health advancement

What is open data in healthcare research?​

Open data in health research refers to datasets that are freely available for use, reuse, and redistribution by anyone - typically with minimal restrictions. Examples include epidemiological statistics, genomic sequences, clinical trial results, and anonymized patient-generated health data. The goal: foster transparency, accelerate scientific discovery more economically, and ensure that health research is a global, collaborative effort.

Common data sources​

  • WHO Global Health Observatory
  • NIH Open Data Platform
  • EU Open Data Portal
  • Open-access cancer registries
  • Patient-led data initiatives (e.g. OpenHumans)

These repositories have enabled hundreds of researchers to access and analyze critical health information without needing to replicate expensive studies or go through extensive licensing processes.

Why open data matters for medical progress​

Health challenges such as pandemics, chronic diseases, and health inequalities cannot be solved by researchers working in silos. Open data allows researchers from diverse disciplines and geographies to collaborate, validate findings, and translate science into actionable insights faster.

When researchers share their data openly:

  • Studies become reproducible and more trustworthy
  • Innovation is accelerated, particularly in fields like AI and personalized medicine
  • Policymakers can act faster with timely access to real-world health trends

Open data as an educational resource​

Freely accessible datasets are essential for training healthcare professionals, data scientists, and epidemiologists. Universities use OMOP-CDM-based real-world datasets to teach clinical epidemiology and data stewardship.

  • Massive open online courses and data competitions (e.g., Kaggle) enable learners to work with authentic datasets and apply theoretical knowledge to real-world problems.
  • Students learn to detect bias, address ethical issues, and apply statistical methods to real health data.

Embedding open-data literacy early in education helps create a workforce prepared to manage sensitive health information responsibly.

Citizen science and public engagement​

Open health data also empowers citizens to co-create knowledge through participatory research:

  • Open Humans allows individuals to contribute wearable and genetic data for research.
  • PatientsLikeMe connects patients to share lived experiences for therapeutic insights.
  • Germany’s Corona-Datenspende enabled millions to contribute health app data for pandemic surveillance.

While these initiatives foster trust and innovation, they must also address inclusivity to ensure that participants reflect diverse demographics to avoid biased conclusions.

Health data is inherently sensitive, and protecting individual privacy is non-negotiable. Ethical openness requires robust safeguards:

  • Obtain informed patient consent for data reuse
  • Mitigate re-identification risks via advanced anonymization techniques
  • Clarify data ownership and governance

Global variation in regulation - GDPR (Europe), HIPAA (U.S.), PIPL (China), and African Union data policies - means cross-border research must navigate complex compliance landscapes. Without careful attention, open data initiatives risk reproducing data colonialism, where lower-income countries contribute data but lack equitable access to resulting benefits

Standards and frameworks for open health data​

To ensure data is not only open but also useful, it must be FAIR: Findable, Accessible, Interoperable, and Reusable. That’s where standards come in.

Enabling tools and frameworks​

  • FAIR Principles: Promote data stewardship best practices
  • OMOP CDM & OHDSI: Harmonize diverse datasets for research collaboration
  • FHIR & HL7 APIs: Enable seamless exchange of structured health data
  • Zenodo & OpenAIRE: Platforms for publishing and citing open datasets

When applied effectively, these frameworks ensure open data is not just available but usable across contexts.

OMOP vs. FHIR: Two standards – One goal​

Standardized health data is the backbone of trustworthy open science. Among the most widely adopted data models are OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) and FHIR (Fast Healthcare Interoperability Resources). While they serve different purposes, both are foundational pillars in achieving a more interoperable and research-ready health data ecosystem.

Key Differences: Focus, Structure, and Use​

AspectOMOP CDMFHIR
FocusPopulation-level analytics and real-world evidence researchInteroperable health data exchange
StructureRelational common data model with standardized tables and vocabulariesModular resources (e.g., Patient, Observation) for flexible data representation
ComplexityRequires comprehensive ETL processes and expertise in data harmonizationEasier to integrate with modern APIs, but high semantic and structural variability
Use CasesEpidemiology, pharmacovigilance, cohort studiesEHR interoperability, patient apps, telemedicine
GranularityDesigned for cross-database harmonization - less clinical detailCaptures detailed clinical context, often more complex to normalize

OMOP is optimized for research and analytics, focusing on structured, standardized data. FHIR is designed for flexibility in clinical workflows, capturing data at a high level of granularity.

Shared Values and Synergies​

Despite these differences, OMOP and FHIR share critical objectives:

  • Interoperability: OMOP enables research across institutions; FHIR facilitates data exchange across care providers.
  • Standardization: Both adopt international terminologies like SNOMED CT, LOINC, and ICD-10.
  • Broad adoption: Used in large-scale initiatives like the All of Us Research Program (USA) and HiGHmed (Germany).
  • Complementary potential: FHIR-to-OMOP transformation tools (e.g., MENDS-on-FHIR, InterSystems OMOP platform) allow FHIR to serve as an input layer, and OMOP as the analytics engine.

Empowering AI-Driven research​

FHIR and OMOP aren’t competing - they’re complementary. FHIR excels in representing health data from diverse systems. OMOP makes that data analytically powerful and is very useful in sharing de-identified data.

This synergy is especially relevant for AI and ML in health research:

  • FHIR captures live, contextualized health data.
  • OMOP enables scalable training, validation, and comparison of models across populations.

Responsible AI in open health data requires bias audits, explainability protocols, and public engagement to ensure models do not perpetuate inequities.

Open science and global collaboration​

Open data underpins open science by lowering barriers to participation and fostering equity.

Collaborative models have emerged that include:

  • Citizen science platforms for patient-led research
  • Trusted research environments (TREs) for secure collaboration
  • Global data commons like GA4GH and EOSC

These efforts aim to lower barriers to participation, enhance scientific equity, and make the health research ecosystem more inclusive.

Barriers to implementation and the road ahead​

Despite strong momentum, several barriers remain:

  • Institutional resistance to share data
  • Technical challenges in standardization and integration
  • Funding gaps for open infrastructure and long-term stewardship

Future strategies​

  • Implement open-by-default policies for publicly funded research
  • Incentivize researchers to publish datasets alongside publications
  • Involve patients and communities as active stakeholders in open data governance

At Data4Life, we believe that responsible openness - not just technical access - is the key to building a global health data ecosystem that works for everyone. Our mission is to foster ethical, secure, and collaborative data environments that bring science closer to the people it serves.

FAQs​

What is open health data?
Open health data refers to health-related datasets that are freely available for public use, usually under specific licensing terms that support reuse.

Why is open data important in health research?
It enables faster innovation, improves transparency, and supports global collaboration to address urgent health challenges.

Is open health data secure?
Yes - if shared responsibly using de-identification, consent, and governance protocols aligned with legal frameworks like GDPR.

What are FAIR principles?
FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles guide the ethical and effective management of open research data.

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