A Proposed Biomedical Data Policy Framework to Reduce Fragmentation, Improve Quality, and Incentivize Sharing in Indian Healthcare in the era of Artificial Intelligence and Digital Health

A research paper proposes a comprehensive policy framework for India to address fragmentation in biomedical data sharing by aligning institutional incentives around AI and digital health. The framework recommends recognizing data curation in academic promotions, incorporating open data metrics into institutional rankings, and implementing Shapley Value-based revenue sharing in federated learning—while navigating India's 2023 data protection regulations.

India sits on vast untapped biomedical datasets fragmented across hospitals, research institutions, and vendor-locked systems, yet lacks mechanisms to incentivize their productive sharing. This paper identifies the core constraint as economic misalignment rather than technical limitation—researchers and institutions face reputational risk and minimal reward for opening their data. The proposed framework tackles this through institutional redesign, positioning data stewardship as a recognized career path comparable to traditional academic roles.

The policy context matters significantly. India's Digital Personal Data Protection Act 2023 introduces privacy constraints that could further entrench data silos unless paired with incentive structures. The framework constructively engages existing initiatives like the National Data Sharing and Accessibility Policy, suggesting integration rather than conflict. Shapley Value-based revenue sharing in federated learning consortia represents a sophisticated approach to distributing value from AI models trained on collaborative data—addressing equity concerns that often derail pooling efforts.

For India's AI ambition, this proposal directly impacts dataset quality and availability for model training. Better-curated, interoperable biomedical data accelerates domestic AI development in healthcare, potentially creating competitive advantage in emerging markets. However, implementation requires simultaneous reform across academic promotion systems, institutional rankings, and funding mechanisms—a coordination challenge spanning government, universities, and private institutions.

The framework's success hinges on sustained policy commitment and cultural shift within Indian academia. Short-term adoption barriers remain high, but long-term gains from unlocking biomedical data could materially strengthen India's AI research ecosystem and healthcare innovation capacity.

• →India's biomedical data fragmentation stems from misaligned institutional incentives, not technological limitations.
• →Integrating data curation into academic promotion criteria and institutional rankings would directly reward data sharing activity.
• →Shapley Value-based revenue sharing addresses equity concerns in federated learning, making data pooling economically attractive.
• →The framework balances regulatory compliance with the 2023 Digital Personal Data Protection Act while enabling productive data access.
• →Systematic implementation across government, universities, and hospitals could unlock significant competitive advantage for India's AI healthcare sector.