3 Key Takeaways
Plain English Summary- 01Core Finding
As these data sources proliferate, interoperability has emerged as a foundational requirement for high-quality neurologic care.
- 02Study Evidence
Tested in a ehr cohort study at the real-world cohort stage.
- 03Practical Meaning
Observational cohort findings reveal real-world links, though lifestyle or unmeasured factors cannot be fully ruled out without an RCT.
What This Means for You
These examples highlight the potential of shared terminologies, structured neurologic observations, and API-enabled exchange, while underscoring persistent challenges, including limited availability of discrete neurologic data elements and the substantial effort required for mapping and governance. As the neurology ecosystem prepares for USCDI-aligned interoperability mandates taking effect in 2026, the neurology community plays a critical leadership role. Priorities include expanding neurologic data models and terminologies, ensuring that dQMs reflect clinical realities, strengthening governance across registries and networks, and enabling interoperable workflows that support remote monitoring and AI-enabled analytics. Together, these strategies position interoperability as a strategic imperative for the future of neurologic care, quality measurement, and research.
The Discovery
Neurology is entering a decade in which rapid growth in diagnostic technologies, data streams, and care delivery models collides with persistent fragmentation of neurologic information across electronic health records, imaging systems, and remote monitoring platforms. As these data sources proliferate, interoperability has emerged as a foundational requirement for high-quality neurologic care. The current landscape limits longitudinal understanding of disease trajectories, impedes care transitions, burdens quality measurement, and constrains research. This article articulates the current and future challenges of interoperability for neurology by characterizing the current state of interoperability; surveying relevant national standards and platforms; identifying key technical, semantic, and organizational barriers; and proposing future goals for improving interoperable neurologic care. Part and parcel of these challenges and goals are the evolving regulatory and policy frameworks, including the Trusted Exchange Framework and Common Agreement, provisions of the 21st Century Cures Act, and federal interoperability rules that mandate standardized, Fast Healthcare Interoperability Resources (FHIR)-based application programming interfaces (APIs), and a common data foundation such as the United States Core Data for Interoperability Version-3 (USCDI v3). In parallel, Centers for Medicare & Medicaid Services (CMS) initiatives have accelerated the transition to digital quality measures (dQMs), increasing the need for automated, multisource, standards-based data exchange aligned with real-time quality improvement. Using hypothetical use cases in vascular and behavioral neurology, we demonstrate how FHIR-based implementation guides could replace manual stroke quality reporting and enable the migration of cognitive assessment measures from legacy electronic clinical quality measures (eCQMs) to interoperable FHIR eCQMs, laying the groundwork for future dQMs.
How It Works, Simply
This neurology research focuses on AI model. Researchers evaluated it using an eHR Cohort study at the real-world cohort stage of medical research.
3 Key Takeaways
Clinical Takeaways- 01Core Finding
As these data sources proliferate, interoperability has emerged as a foundational requirement for high-quality neurologic care.
- 02Study Evidence
Tested in a ehr cohort study at the real-world cohort stage.
- 03Practical Meaning
Observational cohort findings reveal real-world links, though lifestyle or unmeasured factors cannot be fully ruled out without an RCT.
Biological Mechanism & Study Design
Study Design & Cohort
EHR Cohort study
EHR Cohort study · Real-World Cohort
Biological Pathway & Modality
AI model
Intervention: When Systems Talk-Measuring Neurology Care in the Digital Era: Emerging Issues in
Experimental Findings & Endpoints
Neurology is entering a decade in which rapid growth in diagnostic technologies, data streams, and care delivery models collides with persistent fragmentation of neurologic information across electronic health records, imaging systems, and remote monitoring platforms.
As these data sources proliferate, interoperability has emerged as a foundational requirement for high-quality neurologic care. The current landscape limits longitudinal understanding of disease trajectories, impedes care transitions, burdens quality measurement, and constrains research.
This article articulates the current and future challenges of interoperability for neurology by characterizing the current state of interoperability; surveying relevant national standards and platforms; identifying key technical, semantic, and organizational barriers; and proposing future goals for improving interoperable neurologic care. Part and parcel of these challenges and goals are the evolving regulatory and policy frameworks, including the Trusted Exchange Framework and Common Agreement, provisions of the 21st Century Cures Act, and federal interoperability rules that mandate standardized, Fast Healthcare Interoperability Resources (FHIR)-based application programming interfaces (APIs), and a common data foundation such as the United States Core Data for Interoperability Version-3 (USCDI v3).
In parallel, Centers for Medicare & Medicaid Services (CMS) initiatives have accelerated the transition to digital quality measures (dQMs), increasing the need for automated, multisource, standards-based data exchange aligned with real-time quality improvement. Using hypothetical use cases in vascular and behavioral neurology, we demonstrate how FHIR-based implementation guides could replace manual stroke quality reporting and enable the migration of cognitive assessment measures from legacy electronic clinical quality measures (eCQMs) to interoperable FHIR eCQMs, laying the groundwork for future dQMs.
Clinical Translation & Practice Context
These examples highlight the potential of shared terminologies, structured neurologic observations, and API-enabled exchange, while underscoring persistent challenges, including limited availability of discrete neurologic data elements and the substantial effort required for mapping and governance.
As the neurology ecosystem prepares for USCDI-aligned interoperability mandates taking effect in 2026, the neurology community plays a critical leadership role. Priorities include expanding neurologic data models and terminologies, ensuring that dQMs reflect clinical realities, strengthening governance across registries and networks, and enabling interoperable workflows that support remote monitoring and AI-enabled analytics.
Together, these strategies position interoperability as a strategic imperative for the future of neurologic care, quality measurement, and research.
The Bottom Line · Clinical Pearl
“Together, these strategies position interoperability as a strategic imperative for the future of neurologic care, quality measurement, and research. These examples highlight the potential of shared terminologies, structured neurologic observations, and API-enabled exchange, while underscoring persistent challenges, including limited availability of discrete neurologic data elements and the substantial effort required for mapping and governance.”
Published in Neurology · Current Stage: Real-World Cohort
