The Health Data Problem: How Countries Can Turn Fragmented Patient Records Into Better Care and Responsible AI

Health systems generate enormous volumes of clinical, laboratory, pharmacy, imaging,and insurance data. However, much of this information remains fragmented across disconnectedsystems, limiting continuity of care, public-health surveillance, research, and the safedeployment of artificial intelligence (AI).This article proposes a practical, policy-oriented framework for transforming fragmentedhealth data into better clinical decisions, stronger health-system governance, and responsibleAI deployment, with particular attention to developing countries. It uses a comparativepolicy analysis of eight jurisdictions—South Korea, the United States, the United ArabEmirates, Denmark, Estonia, Singapore, Taiwan, and India—complemented by an analysisof prominent implementation failures (England’s care.data programme and the NationalProgramme for IT) and the European Health Data Space as an emerging supranational legaltemplate. The analysis identifies complementary strengths, transferable lessons, limitations,and failure modes.The central finding is that no single country provides a complete model. South Koreademonstrates the value of federated and distributed analysis; the United States highlightsstandardized FHIR-based interoperability; the UAE shows how regulatory coordination canaccelerate adoption; Denmark illustrates the long-term value of linked population data; Estoniaemphasizes patient transparency and trust; Singapore focuses on secure AI deployment;Taiwan demonstrates the potential of linking insurance, care, and public-health data; andIndia offers the most relevant large-scale experience from a developing country, including thepractical challenges of adoption in a mixed health system.Countries should not aim simply to build the largest data warehouse or deploy AI at scale.The strategic priority is to create a trusted health-data ecosystem that improves clinical decisions,protects patient rights, strengthens equity, and enables measurable improvementsin health outcomes. A phased, use-case-driven approach is recommended, beginning withhigh-value applications such as reducing duplicate tests, preventing medication errors, andimproving discharge documentation, with explicit attention to patient matching, cybersecurity,and the institutional preconditions that have caused comparable programmes to failelsewhere.