From interoperability to intelligence: Making healthcare data AI-ready

Editor’s Note: This is the first of three articles, powered by CHIME Digital Health Insights and sponsored by InterSystems, examining how healthcare organizations are advancing interoperability and population health strategies in an era of AI-enabled care. 

The healthcare industry is buzzing with excitement about the potential of artificial intelligence (AI) to transform everything from population health and clinical decision support to predictive analytics. Yet, a major challenge remains: AI is only as powerful as the data it’s built on. For AI to be effective, its data foundation must be labeled, normalized, interoperable, and trustworthy. Many healthcare organizations are still in the early stages of making their data “AI-ready”. Ultimately, interoperability is the method, while a unified, trusted data foundation is the goal. AI can only provide real-world value when fragmented information streams are unified into a single, reliable source. 

Why interoperability matters more than ever

Data fragmentation remains the defining challenge. Patient data is scattered across electronic health records (EHRs), health information exchanges (HIEs), affiliated and unaffiliated providers, community partners, and public health agencies. In this fractured environment, AI tools risk amplifying the very gaps and inconsistencies they are designed to solve. When this poorly standardized data is used to train and run AI models scaled through machine learning, it risks amplifying existing errors, biases, and blind spots. Conversely, when data is unified and standardized, AI can surface insights that were previously invisible. 

At the same time, regulatory and ecosystem shifts are raising the bar, making interoperability a baseline requirement. Participation in Trusted Exchange Framework and Common Agreement (TEFCA) and Qualified Health Information Network (QHIN) is accelerating the push toward common frameworks. Patients are demanding application connections that let them access and share their records more freely. And integration of social determinants of health (SDOH) and behavioral health data is no longer optional — it is essential to whole-person care. 

For CIOs and other healthcare leaders, the message is clear: Interoperability has moved from aspiration to baseline requirement. Without it, AI strategies risk stalling before they begin. 

What “AI-ready” data looks like in healthcare

Progress on connecting data sources throughout the healthcare ecosystem will only be a partial win if the data itself isn’t ready for advanced analytics and AI. For AI to deliver on its promise of improving care and operational efficiency, it needs a strong foundation of high-quality data. In healthcare, “AI-ready” data has four key attributes: 

Standardized – Data must use modern frameworks and shared vocabularies, such as FHIR APIs, SMART on FHIR, and HL7. Healthcare has long relied on frameworks such as HL7, but today FHIR APIs have become the lingua franca, or common language, of data exchange, and its role is now expanding to support AI. Their importance is growing even further as HL7 moves closer to publishing new AI standards that specify how FHIR can support machine learning (ML) and predictive modeling. Major cloud vendors like AWS are also building entire health data platforms around FHIR as the core framework. 

Complete – For a holistic view of a patient’s health, data must be longitudinal and include information from multiple settings, including acute, ambulatory, post-acute, and community care. Predictive risk stratification and precision analytics only work when demographic, SDOH, utilization, and clinical data can flow together without gaps. 

Timely – Data must move at the speed of care. Near real-time feeds are essential for care transitions, predictive analytics like risk scoring, and point-of-care decision support. Delayed data risks undermining trust and diminishing clinical impact.  

Governed – AI adoption depends on trust, which in turn depends on patient consent, privacy, data stewardship, and auditability. Strong governance ensures that data use complies with regulations like HIPAA while maintaining the transparency and accountability required for responsible AI. Without clear and robust data governance frameworks, health systems risk both compliance failures and reputational harm.  

When these key attributes come together, the result is not simply better data but a trustworthy foundation capable of fueling AI-driven care and decision-making. 

Interoperability as an AI enabler for population health

Population health management has long been about connecting disparate data points into a cohesive picture of patients and communities. AI offers the potential to take these capabilities to new heights, but only when interoperability underpins the effort. 

In practice, this means powering care management workflows such as alerts and prioritized worklists or enabling remote monitoring programs where AI-driven triage highlights patients at greatest risk. It also means integrating SDOH data to surface inequities and target interventions that address them, and using advanced analytics including surveillance, environmental data, and real-time dashboards to glean deeper insights into population health trends. 

Checklist: Is Your Data AI-Ready?

• Standardized with modern frameworks like FHIR, HL7, SMART on FHIR  

• Aligned with national frameworks like TEFCA or QHINs  

• Completed across all settings, including SDOH and community data  

• Timely enough to support real-time decision-making  

• Governed with consent, privacy, and auditability  

• Interoperable across your ecosystem and partners 

About InterSystems

InterSystems is the leading provider of data technology for extremely critical data in healthcare. InterSystems brings disparate data into a single reality, creating a unified vision that enables informed decisions and powerful outcomes. Its cloud-first data platforms solve scalability, interoperability, and speed problems for large organizations around the globe.

InterSystems also develops and supports unique managed services for hospital EMRs, unified care records for communities and nations, and laboratory information management systems. InterSystems is committed to excellence through its award-winning, 24/7 support in more than 80 countries. Over 1 billion healthcare records are managed using InterSystems technology around the world.