Achieving TEFCA-Ready Interoperability: How LLMs Bridge Legacy HL7 v2 and FHIR R4 in Adult Care

By WinFully on Technologies | Healthcare AI & Interoperability Insights

Reading Time: 7 minutes | Last Updated: March 2026

Introduction: The Interoperability Imperative in Adult Care

Adult care and skilled nursing facilities (SNFs) manage residents who frequently transition between hospitals, rehabilitation centers, specialty providers, and home health services. Every transition generates critical data: admission records, discharge summaries, medication updates, lab results, and care plans. When this information does not flow seamlessly between systems, the consequences are tangible delayed treatments, medication errors, duplicated tests, and increased readmission risk.

The stakes have never been higher. TEFCA, the Trusted Exchange Framework and Common Agreement, has matured rapidly since its formal launch in December 2023. As of early 2026, there are over 12,000 organizations live on TEFCA, representing more than 72,000 unique connections to clinicians, hospitals, post-acute care facilities, and public health authorities, with over 474 million documents shared since go-live. Eleven Qualified Health Information Networks (QHINs) have been designated  more than double the initial five  including major platforms like Oracle Health, eHealth Exchange, Epic Nexus, and Kno2. Federal agencies including the Social Security Administration, VA, CMS, CDC, and NIH are actively onboarding to the framework.

Yet despite this national momentum, many adult care and long-term care facilities remain tethered to legacy HL7 v2 interfaces built decades ago. These systems were designed for point-to-point message exchange, not for today’s API-driven, network-based interoperability environment. Research consistently shows that information gaps during care transitions between hospitals and SNFs contribute to adverse events, with medication discrepancies affecting up to 60 percent of patients during handoffs. The gap between legacy infrastructure and TEFCA-ready compliance creates both a patient safety risk and a strategic business risk that demands an intelligent bridging solution.

The HL7 v2 Challenge: Why Legacy Systems Fall Short

HL7 v2 remains the most widely deployed messaging standard in healthcare, forming the operational backbone of ADT (Admission, Discharge, Transfer) notifications, laboratory result reporting, medication orders, and billing workflows across thousands of facilities. For decades, it has reliably moved data between systems.

However, HL7 v2 was designed as a flexible standard and that flexibility became its limitation. Messages are pipe-delimited and loosely structured, with implementations varying significantly across vendors and organizations. Fields are often inconsistently populated, locally customized, or semantically ambiguous. The standard lacks native support for RESTful APIs, making real-time, bidirectional data exchange difficult to achieve at scale.

In adult and elder care settings, these limitations frequently lead to fragmented data exchange during the most critical moments of hospital-to-SNF transfers, emergency admissions, and medication reconciliation events. The lack of consistent structure and semantic clarity increases the risk of incomplete information transfer, delayed care decisions, and compliance gaps as TEFCA adoption accelerates nationwide.

FHIR R4 and TEFCA: The Modern Interoperability Framework

Fast Healthcare Interoperability Resources (FHIR R4), developed by HL7 International, provides the modern standard for secure, API-based healthcare data exchange. It enables structured, consistent, and real-time sharing of patient information through RESTful APIs, standardized resource models (Patient, Observation, Encounter, MedicationRequest), and structured coding support using SNOMED CT, ICD-10, and LOINC terminologies.

TEFCA builds upon FHIR as its technical foundation, establishing a “network of networks” that enables secure, standardized health information exchange across QHINs nationwide. The Sequoia Project’s FHIR Roadmap for TEFCA Exchange outlines a four-stage progression: Stage 1 enabled FHIR content support at launch; Stage 2 introduced QHIN-facilitated FHIR API exchange; Stage 3 pilots QHIN-to-QHIN FHIR exchange (underway in 2025–2026); and Stage 4 will enable full end-to-end FHIR exchange between all participants and subparticipants.

For adult care facilities, TEFCA readiness requires alignment with FHIR R4 and US Core profiles, standardized clinical terminology, secure transport protocols with identity verification, audit logging and consent management, and the ability to respond to authorized data queries across networks.

Being TEFCA-ready means that during admissions, discharges, and emergency transfers, critical resident data — medications, diagnoses, allergies, recent labs, and care plans can be securely exchanged without delays or manual reconciliation. For adult care facilities, this eliminates the phone calls, faxes, and manual chart reviews that currently consume staff time during every care transition. It also enables participation in query-based exchange, where authorized providers can request a patient’s medical history from any QHIN-connected system nationwide a capability that is transformative for SNFs receiving residents from distant hospital systems. Ultimately, TEFCA readiness strengthens both regulatory compliance and patient safety at the point of care.

How LLMs Bridge HL7 v2 and FHIR R4: The Technical Architecture

Many adult care facilities cannot simply replace their legacy systems. HL7 v2 workflows are deeply embedded in daily operations. Large Language Models provide an intelligent semantic transformation layer that modernizes interoperability without disrupting existing infrastructure.

Stage 1 | Intelligent Message Parsing

The LLM ingests raw HL7 v2 messages and parses pipe-delimited segments (PID, PV1, OBX, DG1, RXA, IN1, and others) into structured components. Unlike rigid rule-based parsers, the LLM handles vendor-specific deviations, non-standard field placements, and legacy customizations  interpreting the intent of each segment even when implementations vary between source systems.

Stage 2 | Semantic Interpretation

Adult care documentation frequently includes free-text clinical notes, abbreviations (PRN, BID, NPO), locally defined codes, and inconsistent field formatting. The LLM applies natural language understanding to extract clinical meaning from these unstructured elements. A 2025 arXiv study demonstrated that LLM-driven FHIR transformation using GPT-4o achieved 94 percent accuracy on real-world clinical data from the MIMIC-IV database, with resource identification reaching a perfect F1-score in initial benchmarks.

Stage 3 | Terminology Normalization

The LLM maps extracted clinical concepts to standardized vocabularies  SNOMED CT for conditions and procedures, ICD-10 for diagnoses, LOINC for laboratory observations, and RxNorm for medications. This semantic normalization is essential for TEFCA compliance, ensuring that data exchanged across QHINs maintains consistent meaning regardless of the originating system.

Stage 4 | FHIR R4 Resource Generation

Normalized data is assembled into properly structured FHIR R4 resources Patient, Encounter, Condition, Observation, MedicationRequest, DiagnosticReport with correct inter-resource references, US Core profile conformance, and appropriate coding systems. The LLM ensures that lab results link to the correct Observation resources, medications include proper dosage instructions, and encounter records maintain temporal and relational integrity.

Stage 5 | Validation and Quality Assurance

Before data enters the exchange network, an AI-driven validation layer identifies missing required fields, ambiguous entries, coding mismatches, and potential PHI compliance issues. The system cross-checks generated FHIR resources against US Core profiles and TEFCA technical framework requirements, ensuring structural conformance before transmission. This pre-exchange quality gate improves data accuracy, reduces rejection rates from receiving systems, and supports TEFCA-aligned governance and audit requirements critical for facilities participating in QHIN networks where data quality directly impacts care decisions at the receiving end.

A continuous feedback loop captures physician overrides, correction patterns, and downstream system responses to refine mapping accuracy over time, reducing false positives and improving the precision of semantic interpretation with each iteration.

Benefits and Risks: The Business Case for Upgrading

Operational Efficiency: LLM-assisted transformation reduces manual reconciliation during admissions, discharges, and transfers. Real-time FHIR API access enables instant retrieval of patient data, minimizing duplicate entries and streamlining coordination with hospitals, specialists, and payers. Facilities adopting standardized interoperability can reduce administrative workload by up to 30 percent.

Cost Reduction: Modern interoperability lowers IT maintenance costs for multiple custom HL7 v2 interfaces, minimizes manual errors, and accelerates billing and claims processing. Facilities can save 15 to 25 percent in administrative expenses over time while improving compliance posture.

Regulatory Risk of Inaction: Facilities relying solely on HL7 v2 face increasing risk: limited participation in TEFCA networks, stricter regulatory scrutiny as CMS and ONC expand interoperability mandates, and challenges meeting value-based care reporting requirements. Data delays during care transitions can compromise patient safety and create compliance penalties.

HL7 v2 vs. FHIR R4: Key Technical Comparison

Segment-Specific Applications

Providers (Hospitals, Health Systems): Seamless ADT notifications, real-time lab result exchange, and discharge summary sharing with SNFs and post-acute care facilities through TEFCA-connected QHINs reducing readmission risk and improving care transition quality.

Payers and Health Plans: Automated eligibility verification, claims attachment exchange, and prior authorization workflows grounded in standardized FHIR data  supporting payment and healthcare operations exchange purposes now live on TEFCA.

Long-Term Care and Skilled Nursing: Medication reconciliation automation, ADT event processing, care plan synchronization, and MDS/OASIS documentation support enabling TEFCA participation without full EHR replacement.

Public Health and Government: Real-time reporting to CDC, SSA disability benefits determination (now being onboarded to TEFCA via eHealth Exchange), and immunization registry integration all requiring FHIR-based structured data exchange.

Why WinFully on Technologies

Building TEFCA-ready interoperability requires deep expertise across HL7 v2 legacy systems, FHIR R4 implementation, clinical terminology standards, and AI-powered transformation. WinFully on Technologies (winfully.digital) brings 17+ years of healthcare IT experience specializing in FHIR-based integrations, HL7 data exchange, DICOM imaging interoperability, and compliance frameworks (HIPAA, HITECH, SOC-2).

Their approach combines standards-based interoperability architecture with AI-driven semantic intelligence enabling adult care facilities to bridge legacy infrastructure to modern exchange networks without operational disruption.

Conclusion: Bridging Legacy to the Future of Connected Healthcare

Adult care and skilled nursing facilities serve some of the most vulnerable patient populations. As TEFCA expands nationwide with over 12,000 participating organizations and federal agencies actively joining the network, relying solely on legacy HL7 v2 messaging is no longer sufficient for safe, efficient, and compliant health information exchange.

TEFCA-ready interoperability requires structured FHIR-based data, semantic consistency, and robust governance controls. Large Language Models provide a practical, proven bridge transforming legacy HL7 v2 messages into interoperable FHIR R4 resources while preserving operational stability. With LLM-driven transformation achieving 94 percent accuracy on real-world clinical data, the technology is ready for production deployment.

By combining AI-driven semantic intelligence with standards-based frameworks, adult care facilities can enhance care coordination, reduce transition risks, strengthen compliance readiness, and position themselves confidently within the future of connected healthcare. The facilities that invest in this transformation today will not only meet current regulatory expectations but will be prepared for the full end-to-end FHIR exchange capabilities that TEFCA’s Stage 4 roadmap will bring where seamless, real-time data sharing across every participant in the national network becomes the standard of care.

Ready to Achieve TEFCA-Ready Interoperability?

WinFully on Technologies helps healthcare organizations design and implement HL7 v2 to FHIR R4 transformation, TEFCA-aligned interoperability infrastructure, and compliant digital solutions across provider, payer, long-term care, and public health segments.

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