Achieving interoperability in healthcare requires a combination of technical standards, shared terminology, modern infrastructure, and organizational alignment. No single tool or policy gets you there. The challenge is connecting systems that were never designed to talk to each other, while keeping data accurate, secure, and useful at every handoff. Here’s how organizations are making it work.
The Four Levels of Interoperability
Interoperability isn’t binary. The National Library of Medicine describes four distinct levels, and understanding them helps you figure out where your organization currently sits and what comes next.
Level 1, Foundational: One system can receive data from another. This is the baseline, mostly solved by basic IT infrastructure. Think of it as two computers being able to send a file back and forth, even if neither understands what’s inside.
Level 2, Structural: The format of the data is standardized so the receiving system can parse it correctly. The structure preserves the data’s purpose and framework, meaning fields land where they’re supposed to.
Level 3, Semantic: The meaning of the data is preserved. A lab result called one thing in System A is understood as the same thing in System B. This is where clinical terminologies become critical.
Level 4, Organizational: Governance, policy, legal agreements, trust frameworks, and integrated workflows all align so that data flows not just technically but practically, across departments and institutions. This is the hardest level to reach and the one most organizations are still working toward.
Adopt FHIR as Your Data Exchange Standard
The single most important technical decision for interoperability today is building on FHIR (Fast Healthcare Interoperability Resources), the standard developed by HL7. FHIR was designed for the modern web. It uses familiar web technologies, including RESTful APIs and standard data formats, which means developers don’t need specialized healthcare IT training to build with it.
FHIR organizes health data into “resources,” which are modular definitions of common clinical concepts: patient, observation, practitioner, device, condition, and so on. Each resource gets its own URL, and applications access them through standard web requests. This granular approach lets you pull exactly the data you need rather than exchanging massive, monolithic documents.
The practical payoff is significant. FHIR allows developers to build browser-based applications that access clinical data from any health system regardless of operating system or device. It reduces message variability, lowers implementation complexity, and can display a patient’s history in a single consolidated view. If your organization is still relying on older messaging standards, migrating to FHIR-based APIs is the highest-impact move available.
Standardize Clinical Terminology Upstream
Structural interoperability means nothing if two systems use different names for the same lab test. For decades, laboratories and clinical systems used local, idiosyncratic codes to identify results inside electronic messages. A hemoglobin A1c might be coded differently across three hospitals in the same city.
Two international standards solve this. SNOMED CT provides a comprehensive vocabulary for clinical concepts like diagnoses, procedures, and findings. LOINC standardizes the names and codes for laboratory tests and clinical observations. Together, they ensure that a blood glucose reading from a clinic in Denver means the same thing when it arrives at a specialist’s office in Chicago.
The key insight from implementation experience is that standardizing data at the point of creation is far more efficient than trying to normalize it after the fact. When producing systems encode data with consistent terminology from the start, significantly less effort is needed downstream to achieve interoperability. Retrofitting terminology mappings across legacy systems is expensive and error-prone. If you’re building new workflows or onboarding new EHR modules, baking in SNOMED CT and LOINC from day one saves enormous effort later.
Use SMART on FHIR for App Integration
Getting third-party applications to work inside an EHR has historically required deep, custom integration projects. SMART on FHIR changes that. It’s a security standards-based platform that lets developers build apps capable of drawing data from and communicating with any EHR that supports the framework.
The architecture handles authentication and authorization at the system level. When a patient or provider launches a SMART app, the platform validates their credentials against the FHIR authorization server before allowing data access. This eliminates the need for separate login steps and reduces the burden of building custom security for each integration. One implementation demonstrated that SMART on FHIR could bridge efficient data collection through patient-facing apps, seamless EHR integration, and real-time provider access, all without requiring deep modification of complex EHR data models. For organizations looking to integrate patient-reported outcomes, remote monitoring tools, or clinical decision support, SMART on FHIR offers a standardized path that avoids vendor lock-in.
Consider Cloud-Based Data Lakes
Traditional data warehouses require you to define a rigid schema before loading any data. That works poorly in healthcare, where data arrives in wildly different formats: structured EHR records, unstructured clinical notes, imaging files, wearable device streams, and genomic sequences.
Cloud-based data lakes flip this model. They accept raw data in any format first and let you impose structure later during analysis. Platforms like AWS S3, Azure Data Lake, and Google BigQuery provide elastic storage, high-security environments, and the computing power needed for real-time analytics. Healthcare organizations use them to consolidate EHR data alongside wearable device output, medical imaging, and genomic information, eliminating the traditional silos that kept these data types isolated from one another.
The practical advantage is flexibility. A data lake can serve as a centralized repository that feeds clinical care, research, and population health analytics simultaneously. When combined with FHIR-formatted data ingestion, it becomes a powerful foundation for AI-driven insights and cross-institutional research.
Navigate the Regulatory Requirements
U.S. federal policy now actively penalizes organizations that block data sharing. Under the 21st Century Cures Act, “information blocking” means any practice by a covered actor that is likely to interfere with the access, exchange, or use of electronic health information. The rule applies to healthcare providers, health IT developers of certified systems, and health information exchanges.
The consequences are real. Health IT developers and health information networks face civil monetary penalties of up to $1 million per violation if the HHS Office of Inspector General determines they committed information blocking. Healthcare providers face a separate set of disincentives established through federal rulemaking. The standard for providers is whether they knew a practice was unreasonable and likely to interfere with data flow. For IT developers and networks, the bar is even lower: whether they knew or should have known.
On the payer side, the CMS Interoperability and Prior Authorization Final Rule requires impacted payers to implement certain provisions by January 1, 2026, with API-specific requirements extending to January 1, 2027. These rules mandate payer-to-payer data exchange and patient access APIs, pushing interoperability obligations beyond hospitals and into the insurance ecosystem.
Connect to National Exchange Networks
The Trusted Exchange Framework and Common Agreement (TEFCA), also envisioned by the Cures Act, is building the infrastructure for nationwide health data exchange. It works through designated Qualified Health Information Networks (QHINs) that agree to a common set of rules for sharing data. As of the most recent designations, the list of QHINs has grown to seven organizations, including CommonWell Health Alliance and Kno2.
Connecting to a QHIN gives your organization a standardized on-ramp to exchange data with any other participant in the network without negotiating individual point-to-point agreements. For health systems that currently maintain dozens of separate data-sharing contracts, TEFCA participation can dramatically simplify the legal and technical overhead of cross-organizational exchange.
Address the Non-Technical Barriers
Technology alone won’t solve interoperability if organizational and financial barriers remain. Data silos persist not just because systems can’t communicate but because institutions sometimes lack incentives to share, or actively hoard data for competitive advantage. Even when technical connections exist, data blocking limits the practical flow of information.
Cost is a major factor. Integrating medical devices like ventilators and physiologic monitors to an EHR can run $6,500 to $10,000 per bed in one-time costs, plus up to 15 percent annually in maintenance fees. For hospital systems already operating on margins below 3 percent, these investments are daunting. Yet the cost of inaction is steeper: one estimate found that widespread medical device interoperability alone could eliminate at least $36 billion of waste in inpatient settings.
Care transitions remain a weak point. When patients are discharged or referred to another provider, many organizations still rely on paper or fax to send care summaries, fragmenting coordination at the exact moments it matters most. Solving this requires not just better pipes but better processes: standardized referral workflows, shared care plans, and organizational commitments to making data portable. Governance, trust agreements, and workforce training are just as essential as the APIs and terminologies that carry the data.