Interoperability is one of the most frequently invoked — and least well understood — concepts in modern health IT. In principle, it means that your cardiologist in Manhattan should be able to seamlessly access lab results ordered by your primary care physician in New Jersey. In practice, the healthcare system falls far short of this vision, and patients pay the price every day.
What interoperability actually means
Interoperability is one of the most frequently invoked — and least well understood — concepts in health IT. At its core, interoperability refers to the ability of different information systems, devices, and applications to access, exchange, integrate, and cooperatively use data in a coordinated manner. In healthcare, this means that a patient's cardiologist in Manhattan should be able to seamlessly access lab results ordered by their primary care physician in New Jersey, medication records from a pharmacy in Connecticut, and device data from a wearable worn while traveling in California.
The reality falls far short of this vision. Despite more than $36 billion invested through the HITECH Act, fewer than half of U.S. hospitals can perform the four core functions of interoperability: electronically sending, receiving, finding, and integrating patient health information from external sources (ONC, 2022). The result is a healthcare system where critical patient context is routinely lost during care transitions, referrals, and emergency encounters.
The Office of the National Coordinator for Health IT has defined four levels of interoperability, each building on the last:
- Foundational interoperability — the ability to send data from one system to another
- Structural interoperability — the ability to interpret data at the field level (e.g., knowing that "DOB" means date of birth)
- Semantic interoperability — the ability to interpret data meaningfully (e.g., understanding that "Type 2 DM" and "T2DM" and "non-insulin-dependent diabetes" refer to the same condition)
- Organizational interoperability — governance, policy, and social components that enable secure and timely data sharing across organizations
Most health systems today have achieved foundational and structural interoperability for limited data sets. Very few have achieved semantic interoperability, and organizational interoperability remains largely aspirational.
Why patient context matters
The concept of patient context extends far beyond the clinical data stored in an EHR. True patient context encompasses the full picture of an individual's health, including their clinical history, social determinants, behavioral patterns, treatment preferences, care relationships, and personal health goals.
Research consistently demonstrates that provider access to comprehensive patient context improves outcomes. A landmark study published in the Annals of Family Medicine found that physicians who had access to comprehensive patient context were 2.4 times more likely to make clinically appropriate decisions and 63% less likely to order duplicative tests (Weiner et al., 2010). The study defined "contextual errors" — treatment decisions that were clinically correct for the disease but wrong for the patient — and found that they occurred in 62% of encounters where patient context was unavailable.
Consider a common scenario: a patient with well-managed diabetes presents with declining glucose control. Without context, the standard response is to intensify pharmacological treatment. But if the provider knew that the patient had recently lost their job, was experiencing food insecurity, and had been unable to afford their medication for the past two months, the appropriate intervention would be entirely different — connecting the patient with financial assistance programs and community resources rather than prescribing additional medications.
The social determinants layer
Social determinants of health — the conditions in which people are born, grow, live, work, and age — account for approximately 40% of health outcomes, yet this information is almost entirely absent from clinical data systems (Braveman & Gottlieb, 2014). ZIP code is a stronger predictor of life expectancy than genetic code, and the ten-year survival gap between the richest and poorest Americans has widened to 14.6 years for men and 10.1 years for women (Chetty et al., 2016).
Integrating social determinants data into the patient record is not just a technical challenge — it requires new data collection methods, new documentation workflows, and new clinical decision frameworks. Several health systems have begun screening for social needs using standardized tools like the PRAPARE assessment, but translating screening results into actionable clinical context remains an unsolved problem (National Academy of Medicine, 2019).
FHIR and the standardization movement
The Fast Healthcare Interoperability Resources (FHIR) standard, developed by HL7 International, represents the most significant advance in health data standardization since the adoption of ICD coding. Unlike previous standards that relied on document-based exchange, FHIR uses a modern API-based approach that enables granular, real-time data access.
FHIR organizes health data into discrete "resources" — Patient, Observation, Condition, Medication, Procedure, and more than 140 others — each accessible through standard RESTful APIs. This modular architecture means that applications can request precisely the data they need rather than receiving entire clinical documents and extracting relevant fields.
The regulatory environment has strongly favored FHIR adoption. The 21st Century Cures Act, finalized in 2020, prohibits information blocking — the practice of interfering with the access, exchange, or use of electronic health information — and mandates FHIR-based APIs for patient data access. The CMS Interoperability and Patient Access Rule further requires that payers make claims and encounter data available through FHIR APIs (CMS, 2023).
The remaining challenges
Despite regulatory momentum, several challenges remain. FHIR defines the structure of data exchange but does not fully address semantic consistency — the same clinical concept can still be coded differently across systems. Terminology services like SNOMED CT and LOINC help, but mapping between coding systems is imperfect and resource-intensive.
Additionally, FHIR adoption varies significantly by setting. Large health systems with dedicated IT teams have generally implemented FHIR APIs on schedule, but smaller practices, community health centers, and behavioral health providers often lack the technical resources to participate. A 2023 survey by the American Medical Association found that only 23% of independent physician practices had fully implemented FHIR-based data sharing (AMA, 2023).
Building patient context at Welli
At Welli, we are building a patient-centric data layer that aggregates information from multiple sources into a comprehensive health profile. Rather than waiting for universal interoperability to arrive, we are meeting patients where they are — connecting with EHR patient portals, pharmacy systems, wearable devices, and patient-reported data to build the most complete picture of health possible.
Our approach is guided by three principles:
Patient ownership. Health data belongs to patients. Our platform gives individuals control over what data is collected, how it is used, and who can access it. This is not just an ethical position — it is a practical one. Patients who trust the system will share more data, leading to better context and better care.
Longitudinal continuity. Unlike institution-centric EHRs that capture snapshots during episodes of care, our platform maintains a continuous health timeline that persists across providers, insurance changes, and geographic moves. Your health story should not restart every time you change doctors.
Actionable intelligence. Data without interpretation is just noise. Our AI companion, Vita, synthesizes patient context into actionable insights — identifying trends, flagging potential issues, and helping patients and their care teams make more informed decisions.
The vision for connected care
The ultimate goal of interoperability is not data exchange for its own sake — it is the creation of a healthcare system where every clinical decision is informed by the full context of the patient's life. Where a patient presenting to an emergency room in a city they are visiting can have their allergies, medications, and care preferences immediately available. Where a specialist recommendation is automatically reconciled with the primary care plan. Where preventive care gaps are identified and addressed before they become acute problems.
We are not there yet. But the technical, regulatory, and cultural foundations are being laid. The organizations that will shape the future of healthcare are those building bridges between data silos, putting patients at the center of their own health information, and using technology to create the comprehensive patient context that great care requires.
References
- AMA. (2023). Digital Health Survey: Physician Adoption of Digital Tools. American Medical Association.
- Braveman, P., & Gottlieb, L. (2014). The social determinants of health. Public Health Reports, 129(Suppl 2), 19–31.
- Chetty, R., et al. (2016). The association between income and life expectancy in the United States. JAMA, 315(16), 1750–1766.
- CMS. (2023). Interoperability and Patient Access Final Rule. Centers for Medicare & Medicaid Services.
- National Academy of Medicine. (2019). Integrating Social Care into the Delivery of Health Care. National Academies Press.
- ONC. (2022). Interoperability Progress Report. Office of the National Coordinator for Health IT.
- Weiner, S. J., et al. (2010). Contextual errors and failures in individualizing patient care. Annals of Internal Medicine, 153(2), 69–75.