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Healthcare AI adoption is advancing, but the available evidence does not prove that data infrastructure is falling behind everywhere. Philips’ 2026 survey points to growing use and investment in AI; U.S. hospital data show that many hospitals exchange information across several important functions, while European Commission figures show uneven provider connectivity. These measures cover different populations and capabilities, so the more useful question is whether a particular health system can deliver the right data, securely and in a usable form, to a specific AI workflow.
What does “AI-ready data infrastructure” mean?
It is more than an internet connection, an electronic health record (EHR), or an API. For an AI tool to support clinical or operational work, the organization needs a chain of capabilities that gets relevant information to the tool and makes its output usable in context.
- Exchange: information can be sent, received, found, and incorporated from other systems.
- Usable data: records are sufficiently complete, current, and consistently represented for the intended task.
- Integration: data and AI outputs can fit into the relevant workflow rather than remain isolated in a separate application.
- Compute: the organization has suitable capacity for the workload, including training or real-time inference where needed.
- Governance and protection: access, privacy, security, accountability, and oversight are managed for the data and the use case.
A system can have one of these capabilities without having the others. In particular, technical access to information does not by itself show that the information is complete, interpretable, or integrated into care.
Is AI adoption moving faster than readiness?
There is evidence of momentum, alongside a more qualified picture of readiness. Philips’ commissioned Future Health Index 2026 research surveyed more than 2,000 healthcare professionals and 20,000 patients in 10 countries between February and April 2026. Philips reports that 62% of healthcare leaders said the benefits of AI investment met or exceeded its costs. That finding indicates perceived value among surveyed leaders; it is not a measure of how many organizations have AI-ready data or of whether AI improves clinical outcomes.
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Philips frames the issue this way: “Adoption among care teams is moving quickly, but effective use depends on how well health systems can adapt.” The survey is multinational, but it does not represent every country, care setting, or health system. Its results therefore describe reported views, not a universal adoption rate.
What do U.S. hospital exchange measures show?
The U.S. Office of the National Coordinator for Health Information Technology (ONC) assesses hospital exchange across four distinct activities: sending information, receiving it, finding it, and integrating it into the hospital’s EHR. In 2025, 76% of U.S. hospitals engaged in all four, according to ONC’s 2026 data brief.
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This is a meaningful measure of exchange activity, but it does not mean that 76% of hospitals have fully interoperable systems for every clinical use. The four-domain measure describes whether hospitals perform those activities; it does not establish that every incoming record is complete, semantically consistent, timely, or ready for an AI application.
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Patient-facing data access and submission of patient-generated information are different directions of exchange. ONC’s 2026 analysis of AHA Information Technology Supplement data, covering non-federal acute care hospitals with inpatient or outpatient sites, illustrates the distinction:
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| Capability | Reported hospital share | What it measures |
|---|---|---|
| Patient access through APIs, 2024 | Approximately nine in ten | Hospitals enabling patients to access information through an API. |
| Some patient-generated health data submission, 2024 | Two-thirds | Hospitals enabling patients to submit some health data back to the organization. |
| Patient-generated health data submission through APIs, 2024 | About half | Hospitals enabling that submission specifically through APIs. |
These percentages describe different functions, not interchangeable measures of interoperability. A patient being able to retrieve a record does not establish that the patient can return information to the EHR, or that a clinical AI system can consume either stream in a usable way.
What does the European evidence add?
The European Commission’s 2026 study reports data collected for 2025 using annual responses from national competent authorities. The EU-27’s average eHealth maturity score was 87%, while the study reported connection rates of 85% for public providers and 66% for private providers. Those provider figures indicate uneven connectivity across sectors.
The same study reports 78% for its supplier-coverage sub-indicator. That is one component of the composite assessment, not a substitute for the 87% EU-27 average. The study framework includes EU-27 countries, Iceland, and Norway, but the 87% figure cited here is specifically the EU-27 average. These measures should not be treated as directly comparable to ONC’s U.S. hospital exchange percentages: their populations and definitions differ.
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Do APIs and cloud compute make a system AI-ready?
APIs enable access, not automatic interoperability
ONC says that, since January 1, 2023, users of certified EHR technology have been required to have standardized FHIR APIs available for patient and population services. The 21st Century Cures Act sets the goal that information be “accessed, exchanged, and used without special effort through the use of application programming interfaces (APIs).”
Standardized interfaces can make data exchange easier to build and scale, but their availability does not guarantee that the underlying data are complete, consistently coded, or integrated into a clinical workflow. Nor does an API alone settle who may access information, for what purpose, or how an organization will monitor its use.
Compute is necessary for some workloads, but it is only one layer
The OECD describes cloud infrastructure as supporting high-performance AI training and real-time inference, while identifying interoperability as a backbone for useful, scalable health-data use. Compute capacity can make demanding workloads feasible; it cannot repair missing records, resolve inconsistent meaning across systems, or replace privacy, security, and governance controls. The appropriate architecture depends on the organization’s workload and requirements; these system-level considerations do not establish that one supplier or design is right for every provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a health system assess readiness for a specific AI workflow?
Assess the end-to-end data path for the intended use, rather than treating a general interoperability score or an API as a readiness certificate. A practical review asks:
- What decision or task will the AI support? Define the users, setting, and information needed for that workflow.
- Can the system find and obtain the required data? Check whether it can locate relevant information across sources and receive it reliably.
- Can the data be interpreted together? Examine completeness, timeliness, and consistency of representation, not only whether a connection exists.
- Does information enter and leave the workflow appropriately? Confirm how source data reach the tool and how its output is reviewed and incorporated into the EHR or operational process.
- Can the infrastructure support the workload? Match computing capacity and system performance to the actual task, including whether it needs training capacity or real-time responses.
- Are access and use governed? Establish appropriate permissions, protection, oversight, and accountability for both source data and AI outputs.
The evidence supports a measured conclusion: AI activity is growing, but healthcare readiness is not a single number. U.S. exchange measures, European connectivity measures, and multinational survey responses each illuminate part of the picture. For any proposed AI use, the decisive test is whether the organization can find, receive, interpret, integrate, secure, and govern the data that workflow actually needs.
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