Author(s): Priyusharmaa

Originally published on Towards AI.

Healthcare Interoperability: How Connected Data Is Improving Patient Care

A patient walks into an emergency room in a city they’re visiting. They’re unconscious. The attending physician has no medical history, no allergy information, no list of current medications. Every second counts, but the data that could save this person’s life sits locked in a system 500 miles away.

This isn’t a hypothetical. It happens thousands of times a day across the United States and globally. And it’s the exact problem that healthcare interoperability is designed to solve.

The concept is straightforward: make it possible for different healthcare systems, applications, and devices to exchange, interpret, and use patient data seamlessly. The execution, however, is one of the most complex challenges in modern technology.

But we’re finally reaching a tipping point. Regulatory mandates, maturing standards like FHIR, and growing investments in health data integration are converging to make connected healthcare a reality, not just an aspiration.

Let’s break down what’s actually happening, why it matters, and where the industry is headed.

What Is Healthcare Interoperability, and Why Does It Matter Now?

Healthcare interoperability refers to the ability of different health information systems, devices, and applications to access, exchange, and cooperatively use data in a coordinated manner. The goal is simple: the right information, at the right place, at the right time, for the right patient.

The Healthcare Information and Management Systems Society (HIMSS) defines four levels of interoperability: foundational (basic data transport), structural (standardized data format), semantic (shared understanding of meaning), and organizational (governance and policy alignment). Most healthcare organizations today operate somewhere between foundational and structural. The industry needs to reach semantic interoperability at scale.

Why the urgency now? Several forces are colliding:

• Regulatory pressure is intensifying. The 21st Century Cures Act in the US and its information blocking rules, enforced by the ONC, now penalize organizations that obstruct health data exchange. CMS interoperability mandates require payers to implement Patient Access APIs using the HL7 FHIR standard.
• Patients expect it. Consumer experiences in banking and retail have raised the bar. People don’t understand why they can transfer money instantly but can’t share their lab results with a new doctor without faxing paper records.
• The cost of fragmentation is staggering. A study published in the Annals of Internal Medicine estimated that the US healthcare system wastes approximately $760 billion annually, with a significant portion attributable to administrative complexity and care coordination failures — problems directly linked to poor data interoperability.

The Standards Making It Possible

FHIR: The Backbone of Modern Health Data Exchange

If you’ve followed healthcare data interoperability over the past decade, you’ve watched HL7 FHIR (Fast Healthcare Interoperability Resources) evolve from a promising standard to the de facto framework for health data exchange.

FHIR uses RESTful APIs, making it accessible to modern developers who don’t need deep healthcare domain expertise to build integrations. It represents clinical data as discrete “resources” — Patient, Observation, Medication Request — that can be queried and exchanged independently.

The FHIR R4 release, designated as the first normative version, gave organizations the stability they needed to build production systems. According to the Office of the National Coordinator for Health IT (ONC), over 96% of hospitals now use certified EHR technology with API capabilities, many of which support FHIR-based data exchange (ONC Data Brief, 2023).

TEFCA: A National On-Ramp

The Trusted Exchange Framework and Common Agreement (TEFCA), launched by the ONC and managed by the Sequoia Project as the Recognized Coordinating Entity, aims to create a universal governance framework for nationwide health information exchange. TEFCA establishes Qualified Health Information Networks (QHINs) that agree to a common set of rules for exchanging data.

As of early 2025, multiple QHINs are operational, and the framework is beginning to fulfill its promise of enabling query-based exchange across previously siloed networks. This is a significant step toward organizational interoperability — the most mature level in the HIMSS framework.

Real-World Impact: Where Connected Data Changes Outcomes

Care Coordination Across Settings

When a patient transitions from a hospital to a skilled nursing facility, information gaps are common. Medication reconciliation errors during these transitions contribute to nearly 20% of adverse drug events, according to the Agency for Healthcare Research and Quality (AHRQ).

Health data integration between acute care EHRs, post-acute systems, and pharmacy platforms can close these gaps. Organizations implementing interoperable discharge summaries and medication lists through standardized APIs have reported measurable reductions in 30-day readmission rates.

Population Health and Chronic Disease Management

Payers and provider networks managing large populations need aggregated, longitudinal patient data to identify at-risk groups and intervene proactively. Without healthcare data interoperability, this requires manual chart reviews and fragmented reporting.

Connected data platforms that pull information from EHRs, claims systems, labs, and social determinants of health databases enable a more complete picture. For example, identifying that a diabetic patient has missed two endocrinology appointments, filled no prescriptions in 60 days, and lives in a food desert requires integrating data from at least four different sources.

Clinical Research and Real-World Evidence

The pharmaceutical industry is increasingly relying on real-world data (RWD) to supplement clinical trials. Interoperable health data makes it possible to identify eligible trial participants faster and generate real-world evidence from routine clinical care. The FDA’s Framework for Real-World Evidence explicitly recognizes the importance of data quality and interoperability in generating reliable evidence for regulatory decisions.

Challenges That Still Slow Progress

Let’s be honest: healthcare interoperability is not solved. Several barriers persist.

Data quality and standardization gaps. Even with FHIR, the way organizations populate data fields varies enormously. A “diagnosis” in one system might be a structured SNOMED CT code; in another, it’s free text. Semantic interoperability requires not just shared formats but shared meaning, and that remains inconsistent.

Legacy system complexity. Many hospitals still run core systems built in the 1990s and early 2000s. These systems weren’t designed for API-based exchange. Modernizing them — or wrapping them with integration layers — requires significant investment and carries operational risk.

Privacy and consent management. Health data exchange across state lines and organizational boundaries introduces complex consent management challenges. HIPAA provides a federal floor, but state laws like those in California (CCPA/CMIA) and New York add additional requirements. Managing patient consent dynamically across a network of interconnected systems is a technical and legal challenge that’s still maturing.

Trust and governance. Organizations are understandably cautious about sharing data with competitors. Even when the technical plumbing works, the business and governance agreements needed to enable exchange take time to negotiate. TEFCA is addressing this at a national level, but adoption is still ramping.

Emerging Trends Shaping the Future

AI and Interoperability Are Becoming Inseparable

Artificial intelligence needs data. Lots of it. And that data needs to be clean, structured, and accessible. Healthcare data solutions that combine interoperability infrastructure with AI capabilities are gaining traction. Natural language processing (NLP) is being used to extract structured data from unstructured clinical notes, effectively bridging the semantic interoperability gap that standards alone can’t fully close.

According to a report from McKinsey & Company, AI-enabled healthcare could generate up to $100 billion annually in value for the US healthcare system, but only if the underlying data infrastructure supports it (“The Era of Exponential Improvement in Healthcare?” McKinsey, 2023).

Cloud-Native Integration Platforms

The shift to cloud-based health data integration platforms is accelerating. These platforms offer pre-built connectors for major EHRs, claims systems, and public health registries, reducing the time and cost of building point-to-point integrations. They also provide the scalability needed to handle the growing volume of health data from wearables, remote monitoring devices, and genomic sequencing.

Patient-Centered Data Access

The consumer health data movement is gaining momentum. Apple Health Records, CommonHealth on Android, and SMART on FHIR applications are putting patients at the center of their own health data exchange. This shift isn’t just philosophical — it’s practical. When patients can carry their data, they become the integration layer.

Organizations like Persistent Systems are working at this intersection of healthcare domain expertise and technology modernization, helping health systems and payers build interoperable, cloud-native platforms that support these evolving standards. Their work in healthcare data integration and connected care solutions reflects the kind of engineering-led approach that enterprises need to move beyond pilot projects to production-scale interoperability.

What Technology Leaders Should Do Now

If you’re a CTO, CIO, or VP of Engineering at a healthcare organization, here’s a practical starting point:

1. Audit your current interoperability maturity. Map your systems, data flows, and integration points. Identify where you’re still relying on batch files, faxes, or manual processes.
2. Invest in FHIR-native architecture. If you’re building new capabilities, build them on FHIR from the start. Retrofitting is always more expensive.
3. Engage with TEFCA early. Even if you’re not ready to become a QHIN participant, understand the framework and plan your network strategy accordingly.
4. Treat data quality as a first-class problem. Interoperability without data quality moves bad data faster. Invest in data governance, terminology mapping, and validation.
5. Plan for AI readiness. Your interoperability investments today will determine whether you can leverage AI effectively tomorrow.

The Bottom Line

Healthcare interoperability isn’t a technology trend. It’s the foundational infrastructure that everything else in modern healthcare depends on — value-based care, population health, precision medicine, AI-driven diagnostics, and patient experience.

The good news is that the standards are maturing, the regulatory environment is supportive, and the technology is ready. The challenge is execution: modernizing legacy systems, aligning governance, and building the organizational will to share data across boundaries.

The organizations that get this right won’t just improve their operations. They’ll genuinely improve patient care. And in healthcare, that’s the only metric that ultimately matters.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI