{"id":7715336,"date":"2026-04-14T21:42:49","date_gmt":"2026-04-14T21:42:49","guid":{"rendered":"https:\/\/orionhealth.com\/?p=7715336"},"modified":"2026-04-14T21:42:50","modified_gmt":"2026-04-14T21:42:50","slug":"who-controls-digital-health-infrastructure","status":"publish","type":"post","link":"http:\/\/orionhealth.com\/global\/blog\/who-controls-digital-health-infrastructure\/","title":{"rendered":"Who controls access to healthcare in the age of digital infrastructure?"},"content":{"rendered":"\n

Healthcare is changing in fundamental ways. Instead of being shaped mainly by hospitals or care pathways, it is now more shaped by digital systems that determine how patients access care and how decisions are made.<\/p>\n\n\n\n

These infrastructures include triage algorithms, AI assistants, digital health records, and interoperable data platforms. They are becoming the operating system of modern health systems.<\/p>\n\n\n\n

The shift in power: from providers to platforms<\/h2>\n\n\n\n

Power is shifting away from traditional providers to those who design and run digital infrastructure.<\/p>\n\n\n\n

The key question is not whether digital transformation will happen, but whether public health systems can maintain oversight of the systems that control access, allocation, and innovation.<\/p>\n\n\n\n

At the centre are large technology firms.  They are becoming systemic actors that shape how data is collected and monetised.<\/p>\n\n\n\n

Public health systems generate vast amounts of data, but it is increasingly processed and monetised in ecosystems outside public control.<\/p>\n\n\n\n

Governance gaps in the health data economy<\/h2>\n\n\n\n
\"\"
Global governance of commercial actors in data-intensive health innovation<\/strong>
Source: Shaw et al. (2026)<\/figcaption><\/figure>\n\n\n\n

Existing regulatory frameworks were not designed for a world in which data is continuously repurposed and used to train adaptive systems.<\/p>\n\n\n\n

The General Data Protection Regulation (GDPR) attempts to balance protection with innovation. However, its broad interpretation of \u201cscientific research\u201d creates ambiguity. Activities undertaken by private actors can fall within the same category as public interest research, enabling access to data under more permissive conditions.<\/p>\n\n\n\n

This creates a structural asymmetry. Public systems remain accountable to citizens, while private actors operate with greater flexibility in how they use and combine data. Over time, this erodes the public sector’s ability to control not only data but also the direction of innovation itself.<\/p>\n\n\n\n

The new front door to healthcare<\/h2>\n\n\n\n

The more immediate risk lies in access.<\/p>\n\n\n\n

Increasingly, patients are not entering the system through traditional channels such as general practice or national helplines. They are turning to digital platforms and AI assistants that provide instant, personalised guidance.<\/p>\n\n\n\n

These tools handle personal health data, give triage advice, and help decide when and where people seek care. Triage is not neutral; it affects how demand is distributed, which in turn changes wait times, priorities, and outcomes.<\/p>\n\n\n\n

When triage moves outside public infrastructure, control over these dynamics moves with it. Platforms optimise for user experience and engagement, not for system sustainability or equitable allocation.<\/p>\n\n\n\n

Data capture, consent, and platform dynamics<\/h2>\n\n\n\n
\"\"
Advantages and disadvantages of opt-in and opt-out models<\/strong>
Source: Williams et al. (2024)<\/figcaption><\/figure>\n\n\n\n

At the same time, platforms are building longitudinal health records outside public systems, as patients upload data into convenient private environments.<\/p>\n\n\n\n

Over time, this creates switching costs. As private records become more complete, patients are less likely to return to fragmented public systems, driving platform capture through gradual shifts in behaviour.<\/p>\n\n\n\n

Open vs closed AI: control vs convenience<\/h2>\n\n\n\n
\"\"
Closed vs. open approach of deploying LLMs for clinical applications<\/strong>
Source: Dennst\u00e4dt et al. (2025)<\/figcaption><\/figure>\n\n\n\n

This dynamic extends to AI infrastructure. Closed models are easier and cheaper to deploy, but reduce control and increase dependence. Open or local models offer greater control and data sovereignty but require more capability and investment.<\/p>\n\n\n\n

Governing digital infrastructure as a strategic asset<\/h2>\n\n\n\n

The strategic challenge is not whether to adopt digital infrastructure, but how to govern it.<\/p>\n\n\n\n

Public systems must treat data infrastructure as a strategic asset, maintaining custodianship, setting interoperability standards, and controlling how data flows across the ecosystem. Governance also needs to shift from focusing on who uses data to how it is used, distinguishing between beneficial and extractive uses.<\/p>\n\n\n\n

Strong institutional capability is essential. Governance bodies must have the authority to oversee data access, approve partnerships, and enforce accountability.<\/p>\n\n\n\n

Control of access pathways is equally critical. Public systems need to shape how digital triage operates, ensuring decisions reflect clinical priorities rather than commercial incentives.<\/p>\n\n\n\n

Finally, there is an economic dimension. When private actors derive value from publicly generated data, mechanisms must ensure that value is shared.<\/p>\n\n\n\n

A defining choice for health leaders<\/h2>\n\n\n\n

The core issue is control.<\/p>\n\n\n\n

Health systems have long been defined by their ability to allocate resources based on need and maintain public trust. These capabilities are now shaped by digital infrastructure.<\/p>\n\n\n\n

If control of that infrastructure shifts, the foundations of public healthcare are at risk.<\/p>\n\n\n\n

The choice is clear: passive adoption leads to loss of control, while active governance enables systems to harness digital capabilities while preserving equity, accountability, and long-term sustainability.<\/p>\n\n\n\n

Authored by\u00a0Tom Varghese<\/a>, Global Product Marketing & Growth Manager at Orion Health.<\/p>\n\n\n\n

\n\n\n\n

References<\/h2>\n\n\n\n
    \n
  • Marelli, L., Testa, G., & Van Hoyweghen, I. (2021). Big Tech platforms in health research: Re-purposing big data governance in light of the General Data Protection Regulation\u2019s research exemption. Big Data & Society, 8(1), 1\u201314.\u00a0<\/li>\n\n\n\n
  • Rikap, C., & Lundvall, B.-\u00c5. (2022). Big tech, knowledge predation and the implications for development. Innovation and Development, 12(3), 389\u2013416.\u00a0<\/li>\n\n\n\n
  • Siira, E., Johansson, H., & Nygren, J. (2025). Mapping and summarizing the research on AI systems for automating medical history taking and triage: Scoping review. Journal of Medical Internet Research, 27, e53741.\u00a0<\/li>\n\n\n\n
  • Tony Blair Institute for Global Change. (2024). Preparing the NHS for the AI era: A digital health record for every citizen.\u00a0<\/li>\n\n\n\n
  • Tony Blair Institute for Global Change. (2026). Who controls access to NHS care in the age of Big Tech?\u00a0<\/li>\n\n\n\n
  • Williams, M., Karim, W., Gelman, J., & Raza, M. (2024). Ethical data acquisition for LLMs and AI algorithms in healthcare. npj Digital Medicine, 7, 377.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    Healthcare is changing in fundamental ways. Instead of being shaped mainly by hospitals or care pathways, it is now more shaped by digital systems that determine how patients access care and how decisions are made. These infrastructures include triage algorithms, AI assistants, digital health records, and interoperable data platforms. They are becoming the operating system […]<\/p>\n","protected":false},"author":37,"featured_media":7715340,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[1],"tags":[],"region":[25,26,23,24,27,28],"class_list":["post-7715336","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","region-au","region-ca","region-global","region-nz","region-uk","region-us"],"yoast_head":"\nWho controls digital health infrastructure? - Orion Health<\/title>\n<meta name=\"description\" content=\"Explore who controls digital health infrastructure and how governance shapes access, data use, and healthcare outcomes.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/orionhealth.com\/global\/blog\/who-controls-digital-health-infrastructure\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Who controls digital health infrastructure? 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