WHO Calls for Stronger Health Data Governance in the Age of AI

WHO urges policymakers to implement governance frameworks that support innovation while protecting individual rights and fostering public trust.

In a significant move, the World Health Organization (WHO) urges policymakers worldwide to implement governance frameworks that support innovation while protecting individual rights and fostering public trust.

As digital technologies and artificial intelligence (AI) continue to reshape global healthcare, the governance of health data has become crucial to ensuring quality, equity, and sustainability in health systems.

Recognizing this, the WHO European Region has released a policy brief titled Health Data Governance in the Age of Artificial Intelligence: Policy Imperatives for the WHO European Region.

The brief incorporates findings from the 2022 WHO digital health survey and research from international institutions, including the Organisation for Economic Co-operation and Development (OECD), the World Bank Group, the European Union Agency for Fundamental Rights, and the European Consumer Organisation.

It highlights the pressing need for transformative policies to regulate health data collection, sharing, security, and ethical reuse.

Pandemic Lessons & the Need for Digital Health Investment

The COVID-19 pandemic exposed the disparities in global health data governance. Countries with well-established digital health infrastructures could respond effectively by mobilizing health data for real-time public health interventions.

In contrast, those lacking digital preparedness struggled to access and share critical information.

The WHO digital health survey found that 73% of European Member States allocated targeted funding to strengthen their digital health systems during the pandemic.

Additionally, 86% now have laws ensuring patients can access their personal health data, a move considered essential for transparency and trust.

Despite these advances, some regions continue to lag behind, unable to collect and use health data efficiently for timely decision-making. The pandemic underscored that digital transformation is no longer optional but a foundational component of modern healthcare resilience.

Countries that once viewed digital health as a secondary concern now recognize its significance in emergency preparedness, equitable healthcare access, and the broader goal of strengthening public health systems.

Interoperability Challenges in Digital Health

While many countries have prioritized data integration, interoperability remains a significant challenge.

The WHO report reveals that health information is often stored in fragmented systems that lack technical compatibility, making data exchange between hospitals, research institutions, and public health agencies difficult.

This lack of coordination hinders care continuity, public health surveillance, and the effectiveness of AI-driven health applications.

The WHO emphasizes that seamless interoperability is essential not just for operational efficiency but also for patient safety and health equity.

AI systems rely on high-quality, standardized datasets to deliver accurate results. When data is incomplete, biased, or stored in inaccessible formats, it can lead to inaccurate diagnoses and reinforce existing health disparities, the report highlighted.

To address this, WHO advocates for the adoption of FAIR data principles, which ensure that health data is findable, accessible, interoperable, and reusable.

According to the global organization, standardized data-sharing practices would improve the accuracy of AI-driven healthcare applications and enhance health systems' ability to respond effectively to public health challenges.

Trust & Ethical Considerations in AI-Driven Healthcare

The growing role of AI in healthcare presents both opportunities and ethical dilemmas. AI-powered tools can accelerate diagnosis, personalize treatment, and optimize health services, but they also raise concerns regarding data ownership, privacy, and accountability.

Questions arise about who owns the data used to train AI algorithms, how consent and transparency are maintained, and what mechanisms exist to prevent bias in AI-driven decisions.

WHO emphasizes that strong data governance frameworks are necessary to ensure AI is deployed responsibly. Clear legal and ethical guidelines should govern data collection, processing, and sharing.

Strategies such as consent-based data exchange, pseudonymization to protect patient identities, and algorithmic oversight are essential in minimizing risks and preventing misuse.

The WHO also stresses that governance should not be dictated solely by policymakers and technology developers.

Patients, healthcare professionals, researchers, and civil society organizations must actively shape data policies to ensure they align with societal values, address ethical risks, and foster trust.

Regulatory Frameworks & International Collaboration

Several global and regional initiatives are already shaping health data governance. The OECD's Council Recommendation on Health Data Governance outlines key ethical and efficient health data use principles.

In the European Union, a comprehensive legal framework is emerging, including the General Data Protection Regulation (GDPR), the Data Governance Act, the Artificial Intelligence Act, and the European Health Data Space (EHDS) Regulation.

These policies aim to ensure high-quality, privacy-compliant data usage while fostering innovation in AI-driven healthcare.

However, the WHO warns that legal frameworks alone are not sufficient. Governments must invest in digital infrastructure, secure data platforms, and institutional coordination.

Outdated IT systems, technological barriers, and a lack of skilled workforce continue to hinder the effective use of health data.

Public communication efforts are also necessary to address data privacy and surveillance concerns. Even the most advanced health data governance frameworks may face resistance without public trust.

Stay tuned for more such updates on Digital Health News.