The Data Cartels
Who Owns Health Data, Who Profits, and Who Loses Sovereignty.
From Clinical Records to Strategic Assets
Healthcare data has crossed a threshold. What was once understood as confidential clinical documentation—ethically stewarded under professional norms of care—has been reclassified as a strategic economic resource. This transformation did not occur through legislative decree or democratic debate. It unfolded incrementally through technical partnerships, cloud migrations, and AI “solutions” introduced under the banners of efficiency, modernization, and innovation.
Morley (2022) identifies this shift as a governance failure rather than a technological inevitability. Health systems, particularly public ones, increasingly outsource data infrastructure to private technology vendors without retaining enforceable control over downstream data use. The result is a structural inversion: institutions responsible for patient welfare no longer fully control the informational substrate upon which care decisions are built.
What emerges is not a free market, but a cartelized data economy—where a small number of firms gain privileged access to vast health datasets while patients, clinicians, and even states remain informationally subordinate.
The Mechanics of Data Capture
The acquisition of health data by private AI firms rarely resembles expropriation. It is procedural, contractual, and often welcomed. Understaffed hospitals facing mounting patient loads accept vendor-managed electronic health record systems. Ministries of health confronting epidemiological uncertainty adopt AI-driven analytics platforms. Cloud providers offer “secure” storage solutions that quietly embed proprietary standards and lock-in mechanisms.
Li (2024), examining AI deployment in low-resource public health systems, demonstrates how governance gaps enable data extraction without reciprocal sovereignty. Data generated by public populations becomes embedded in private infrastructures, often processed offshore, and repurposed for model training beyond the original clinical or public health mandate. The data leaves; control does not follow.
Brookings Institution (2025) warns that these public–private arrangements frequently lack clear provisions on secondary use, algorithmic audit rights, or data exit strategies. Once embedded, health systems become dependent on vendors whose commercial incentives are structurally misaligned with public accountability.
Consent Without Power
Consent remains the most frequently invoked ethical safeguard in health data governance—and the most hollow. Moulaei (2025) shows that patient consent for secondary data use in AI contexts is routinely abstract, bundled, and non-specific. Individuals may consent to “research” or “system improvement” without any realistic understanding of downstream AI training, cross-border data transfers, or commercialization pathways.
Conduah and Yadav (2023) further demonstrate that consent mechanisms collapse entirely in contexts of structural vulnerability. Marginalized populations—already disproportionately surveilled and under-resourced—are more likely to have their data extracted without meaningful agency. Data poverty and data exploitation coexist in the same communities, revealing consent as a procedural formality rather than a vehicle of autonomy.
This is not an ethical oversight; it is a design feature of contemporary data economies.
Read also: AI & Health: Who Controls The Cure?—PART 1
Data Ownership: The Legal Vacuum
Despite its centrality to AI governance, data ownership in healthcare remains legally ambiguous. Liu (2024) notes that most jurisdictions lack clear statutory definitions assigning ownership rights over health data once it is digitized, aggregated, or de-identified. This ambiguity benefits intermediaries—platforms, analytics firms, and cloud providers—who operate within a permissive gray zone.
Pham (2025) highlights the legal consequences of this vacuum. When harm arises from AI-assisted decisions—whether through biased models or erroneous predictions—responsibility fragments across developers, data custodians, healthcare institutions, and regulators. Patients are left navigating a labyrinth of diffuse liability, often without recourse.
Nouis et al. (2025) describe this condition as an accountability failure rooted in governance, not technology. Systems that shape life-and-death decisions operate without clearly assigned responsibility because no actor fully owns the data, the model, or the outcome.
Data Justice and Structural Inequality
The data cartel phenomenon does not distribute harm evenly. Shaw (2023), advancing a data justice framework, argues that health data governance must be evaluated not only on privacy grounds but on distributive justice. Who benefits from data-driven health innovations? Who bears the risks of misclassification, surveillance, or exclusion?
Oktaviana (2025), studying hospital governance perspectives, finds that institutions often lack internal capacity to challenge vendor data practices. Decisions about data sharing are made under financial and operational pressure, not ethical deliberation. This imbalance systematically disadvantages patients whose data generates value without yielding commensurate benefit.
Weiner (2025) reinforces this point by showing that ethical strategies lag far behind technical deployment. Governance mechanisms are reactive, fragmented, and often symbolic—insufficient to counteract the structural power of entrenched data intermediaries.
Health Sovereignty and the Global Dimension
For low- and middle-income countries, the stakes escalate from ethics to sovereignty. Ghaffari Heshajin (2024) demonstrates that weak health information governance frameworks enable foreign entities to process national health data with minimal oversight. The analytical value extracted from these datasets often flows outward, reinforcing global asymmetries in knowledge production and economic gain.
Geist (2025), analyzing national health data sovereignty, warns that states risk losing strategic control over population health intelligence. Once predictive models are trained externally, countries become dependent on proprietary systems to interpret their own epidemiological realities. Sovereignty erodes not through coercion, but through technical dependency.
Why Data Control Determines the Cure
AI does not control healthcare because it is intelligent. It controls healthcare because it controls data lineage—what is collected, what is excluded, how variables are weighted, and which populations become statistically visible. Li (2025) frames this as ecosystem governance: those who govern data flows govern outcomes.
Without enforceable public-interest data governance, every promise of ethical AI remains downstream of a deeper power imbalance. Transparency initiatives, fairness metrics, and ethics boards cannot compensate for structural data capture.
The forensic conclusion is unavoidable: who owns the data increasingly determines who controls the cure.
This chapter establishes the infrastructural foundation of AI power in healthcare. The next investigation moves one layer deeper—into the clinical interface itself—where opaque systems convert data into decisions.
Professor MarkAnthony Ujunwa Nze is an internationally acclaimed investigative journalist, public intellectual, and global governance analyst whose work shapes contemporary thinking at the intersection of health and social care management, media, law, and policy. Renowned for his incisive commentary and structural insight, he brings rigorous scholarship to questions of justice, power, and institutional integrity.
Based in New York, he serves as a full tenured professor and Academic Director at the New York Center for Advanced Research (NYCAR), where he leads high-impact research in governance innovation, strategic leadership, and geopolitical risk. He also oversees NYCAR’s free Health & Social Care professional certification programs, accessible worldwide at:
https://www.newyorkresearch.org/professional-certification/
Professor Nze remains a defining voice in advancing ethical leadership and democratic accountability across global systems.
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