Healthcare has spent decades solving one part of the interoperability challenge: moving data from one system to another. Standards such as HL7v2 and FHIR have made data exchange faster, more scalable and more technically achievable.
But as our Interoperability Solutions Architect Yanick Gaudent explains in our latest Interoperability Masterclass, healthcare organizations now face a more complex question: Is the data being exchanged actually usable?
That is the idea behind interoperability with intent. It is not just about connecting systems or transferring information. It is about ensuring healthcare data carries enough meaning, context and governance to support better decisions across the full Circle of Care.
Today, that circle is expanding. Patient data is no longer used only by clinicians inside a hospital. It may also support community care teams, specialists, caregivers, insurers, regulators, pharma companies, researchers and increasingly, AI systems. At the same time, data is being generated from more sources than ever, including EHRs, clinical systems, wearables, remote monitoring devices and patient-facing applications.
This creates a major opportunity for healthcare. With the right approach, interoperable data can support more preventive, personalized and outcome-focused care. For example, AI could help identify long-term patterns in patient data that would be difficult for a clinician to spot manually, such as recurring changes in blood markers over several years.
But this only works if the data is clean, contextual and properly governed.
In the episode, Yanick highlights one of the most overlooked risks in healthcare interoperability: organizations often know how to move data, but they may not know who owns it, who is responsible for its quality or whether the receiving system is interpreting it correctly. Without clear governance, even technically successful data exchange can lead to confusion, inconsistency and poor decision-making.
This becomes especially important as healthcare organizations prepare for AI. AI agents and analytics tools need more than raw data points. They need metadata, clinical context, standardized coding and clear rules around access and usage. Otherwise, the industry risks scaling bad data faster.
Watch the full Interoperability Masterclass episode to hear Yanick explain how healthcare organizations can move beyond basic data exchange and build interoperability strategies that are truly fit for AI-enabled, patient-centered care.
