How do we ensure interoperability between Electronic Health Records (EHRs) across platforms and borders?

1. Introduction to EHR Interoperability

Definition and Core Concept
Electronic Health Record (EHR) interoperability refers to the capability of different healthcare information systems, devices, and applications to access, exchange, and cooperatively use data in a coordinated manner within and across organizational boundaries. The core idea is to enable healthcare providers, regardless of the system they use, to share accurate and up-to-date patient information, thereby supporting clinical decisions, reducing errors, and improving outcomes. Unlike basic data exchange (like PDFs or scanned reports), true interoperability involves standardized, structured, and semantically understandable information that integrates seamlessly into clinical workflows.

Importance of Interoperability in Healthcare
Interoperability is essential for delivering patient-centered, safe, and efficient healthcare. Without interoperability, patient data remains siloed within disparate systems, leading to fragmented care, duplication of tests, medical errors, and administrative burdens. For example, if a patient visits a new hospital or sees multiple specialists, each provider may need to gather the same information again unless interoperable systems allow prior records to be shared. In public health, especially during crises like pandemics, interoperability enables rapid data aggregation for surveillance, resource allocation, and response planning.

Types and Levels of Interoperability
EHR interoperability can be understood in terms of four distinct levels: foundational, structural, semantic, and organizational. Foundational interoperability allows simple data transmission without interpretation. Structural interoperability ensures data follows specific formats and syntax, making it easier to integrate. Semantic interoperability involves the use of common clinical terminologies so that meaning is preserved and understood across systems. Organizational interoperability includes policies, consent mechanisms, and legal agreements that allow institutions in different jurisdictions to share data securely and responsibly. Achieving higher levels of interoperability, especially semantic and organizational, is the most complex and impactful.

Interoperability vs. Health Information Exchange (HIE)
While often used interchangeably, interoperability and health information exchange (HIE) have subtle differences. Interoperability is the broader technical and policy framework enabling systems to work together, while HIE refers specifically to the actual exchange of health information between organizations. An HIE can be viewed as one method of achieving interoperability. Effective HIEs rely on interoperable systems and standards to function and provide real-time or batch access to patient records among participating entities.

Global Need for Cross-Border Interoperability
As globalization increases patient mobility, the need for EHR interoperability across borders becomes more urgent. Migrants, tourists, international students, and telemedicine patients often require healthcare in different countries. Without standardized and interoperable systems, critical patient history may not be available to foreign providers, risking delays and adverse events. Cross-border interoperability also supports global research collaborations and epidemiological studies. However, the effort faces additional challenges due to differing legal frameworks, coding systems, languages, and healthcare infrastructures.

Challenges in Achieving Interoperability
Despite the evident benefits, widespread EHR interoperability remains a difficult goal. Variations in data formats, proprietary systems, misaligned incentives, and inconsistent implementation of standards hinder progress. Legal and regulatory barriers, such as differing privacy laws (e.g., HIPAA vs. GDPR), further complicate cross-border data exchange. In many regions, especially low-resource settings, the lack of IT infrastructure and skilled personnel also limits the deployment of interoperable health systems.

The Future Vision of Interoperability
Looking forward, the vision for EHR interoperability encompasses real-time, secure, and intelligent data exchange powered by emerging technologies like APIs, blockchain, and artificial intelligence. The evolution of standards such as HL7 FHIR (Fast Healthcare Interoperability Resources) enables scalable, app-based integration of clinical systems. In the future, interoperability will be foundational not only for treatment but also for preventive care, genomics, digital therapeutics, and public health surveillance. A truly interoperable global health ecosystem can transform how care is delivered, making it more connected, personalized, and data-driven.

Interoperability in the context of Electronic Health Records (EHRs) refers to the ability of different EHR systems and healthcare applications to access, exchange, integrate, and cooperatively use patient data, both within and across organizational, regional, and national boundaries. The goal is seamless clinical information exchange to enhance patient care, reduce costs, and support research.

2. Levels of Interoperability

Foundational Interoperability

Foundational interoperability refers to the most basic level of data exchange between healthcare information systems. At this level, one system can transmit data to another system, but the receiving system may not be able to interpret or manipulate the data in a meaningful way. This level ensures connectivity and communication only at a transport level—such as sending a PDF or scanned document via email or a health information exchange (HIE). While it enables information sharing, it does not support automatic processing, clinical decision-making, or integration into workflows. It’s commonly used in legacy systems or early stages of interoperability implementations where minimal infrastructure is in place.

Structural Interoperability

Structural interoperability is the intermediate level that allows systems to exchange data with a defined structure and format. This level ensures that the data exchanged between information systems can be interpreted at the data field level, allowing information such as laboratory results, prescriptions, and patient demographics to be parsed and displayed in appropriate fields. Standards like HL7 Version 2, CDA (Clinical Document Architecture), and the initial implementations of HL7 FHIR support structural interoperability. Although this level helps maintain data organization and supports automation, it does not fully enable systems to “understand” the meaning of the data being exchanged.

Semantic Interoperability

Semantic interoperability is the highest functional level of interoperability. It allows systems not only to exchange data with structure but also to interpret and use the information meaningfully. At this level, both sending and receiving systems understand the context, meaning, and relationships within the data through the use of standardized medical terminologies such as SNOMED CT, LOINC, and ICD. This capability enables advanced clinical decision support, population health analytics, and automated alerting mechanisms. Semantic interoperability is critical for delivering truly coordinated, safe, and patient-centered care across different healthcare providers and institutions, especially in cross-border scenarios.

Organizational Interoperability

Organizational interoperability goes beyond technology and data to address the broader ecosystem in which interoperability operates. This level involves the alignment of policies, governance frameworks, workflows, and legal regulations that support and facilitate secure and efficient data exchange between healthcare entities. Organizational interoperability ensures that institutions are legally allowed to share data, that they have aligned incentives to do so, and that patient consent, liability, and compliance issues are properly managed. Without this layer, even the most technically advanced systems may face significant barriers to real-world implementation and scalability of interoperability initiatives.

Level	Description
Foundational	Basic data exchange without interpretation (e.g., PDF transfer).
Structural	Data exchange with structured formatting (e.g., lab results, codes).
Semantic	Shared understanding of exchanged data (e.g., coded data using SNOMED CT).
Organizational	Policies, regulations, and governance enabling interoperability between systems.

Semantic and organizational interoperability are the most challenging, especially across borders due to differences in languages, medical coding systems, and privacy laws.

3. Current Global Interoperability Standards

HL7 Version 2, Version 3, and CDA (Clinical Document Architecture)
HL7 (Health Level Seven) is one of the most foundational standards in health information exchange. HL7 Version 2 (v2) is widely used in hospitals and laboratories for messaging—such as sending lab results or admission notifications. Despite its ubiquity, v2 lacks strong consistency in implementation, leading to interoperability challenges. HL7 Version 3 attempted to resolve this through a more rigorous modeling approach but saw limited adoption due to complexity. CDA (Clinical Document Architecture), also developed by HL7, focuses on document-based data exchange such as discharge summaries. CDA documents are XML-based and structured but often still require manual review and are not natively machine-interpretable, limiting semantic interoperability.

FHIR (Fast Healthcare Interoperability Resources)
FHIR, also developed by HL7, is the most modern and rapidly adopted interoperability standard. It uses RESTful APIs and supports JSON/XML formats, making it compatible with web technologies and mobile applications. FHIR allows granular data exchange—such as individual patient vitals, allergies, or medication records—enabling real-time, app-based healthcare solutions. It is designed to be modular (resources can be shared independently) and is being mandated in regions like the U.S. through the 21st Century Cures Act. Many global healthcare systems are aligning their infrastructure to FHIR for greater scalability and developer accessibility.

DICOM (Digital Imaging and Communications in Medicine)
DICOM is the international standard for handling, storing, and transmitting medical imaging information such as X-rays, MRIs, and CT scans. It ensures that imaging systems like PACS (Picture Archiving and Communication Systems) can communicate and integrate with EHRs and other hospital systems. DICOM includes both image data and associated metadata, such as patient information and imaging parameters. It is widely adopted across the world and forms the backbone of radiology and imaging informatics. However, its focus is limited to imaging, and integrating DICOM with other health data standards like FHIR requires bridging mechanisms or middleware.

SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms)
SNOMED CT is a comprehensive, multilingual clinical terminology standard used to encode diseases, procedures, findings, and other clinical information. It enables semantic interoperability by ensuring that clinical terms have consistent meanings across systems and regions. With over 350,000 concepts, SNOMED CT supports detailed data entry and analysis. It is maintained by SNOMED International and adopted in over 40 countries, including the UK, Australia, and Canada. Its integration with FHIR and other standards helps ensure that clinical data is not just exchanged but also understood in the same way across different systems.

LOINC (Logical Observation Identifiers Names and Codes)
LOINC is a standard for identifying health measurements, observations, and laboratory test results. It assigns universal codes to lab tests, clinical measurements (like blood pressure), and survey instruments. Developed by the Regenstrief Institute, LOINC enables structured and interoperable lab reporting, which is critical for diagnostics and public health surveillance. It is often used in conjunction with HL7 messages or FHIR resources to ensure lab data is machine-readable and comparable across laboratories, regions, and EHR systems. LOINC is especially important for COVID-19 reporting and other epidemic monitoring efforts.

ICD-10 and ICD-11 (International Classification of Diseases)
ICD, maintained by the World Health Organization (WHO), provides a standardized coding system for diseases, symptoms, and other health-related conditions. ICD-10 is the most widely used version globally, though ICD-11 is gradually being adopted. It plays a major role in billing, reporting, epidemiology, and mortality statistics. While ICD is not an interoperability protocol per se, it is often embedded in EHR systems and is crucial for classification, reimbursement, and analytics. Integration of ICD with other standards like SNOMED CT or LOINC can enhance the utility of structured clinical data.

IHE (Integrating the Healthcare Enterprise)
IHE is not a data standard itself but a framework that promotes coordinated use of existing standards like HL7, DICOM, and LOINC to improve interoperability in healthcare. It defines integration profiles that specify how different systems should communicate for specific clinical workflows, such as radiology image sharing or cross-enterprise document exchange. IHE profiles are tested at annual events called Connectathons, where vendors demonstrate interoperability in real-world scenarios. IHE plays a critical role in ensuring that standards are implemented consistently and work together in complex hospital or regional healthcare environments.

OpenEHR
OpenEHR is an open standard focused on the modeling, storage, and management of structured clinical data over time. Unlike HL7, which is focused on messaging and APIs, OpenEHR separates clinical content (archetypes and templates) from technical implementation, allowing greater flexibility and long-term sustainability. It enables semantic interoperability and version-controlled health records, which is crucial for longitudinal patient care and chronic disease management. Countries like the UK, Norway, and Australia have adopted OpenEHR in national or regional EHR initiatives. It is increasingly being explored as a foundation for interoperable clinical repositories that support AI and population health analytics.

Standard	Description	Usage
HL7 v2/v3 & CDA	Messaging and document architecture standards. Widely used but inconsistent implementations.	USA, Canada, Europe
HL7 FHIR	Modern web-based standard for exchanging healthcare info via APIs.	Global (gaining rapid adoption)
DICOM	Standard for imaging data (radiology, etc.)	Worldwide
LOINC	Codes for laboratory tests and clinical observations	USA, Canada, Brazil
SNOMED CT	Standard for clinical terminology and disease classification	40+ countries including UK, Australia, Sweden
ICD-10/11	WHO’s classification system for diseases and health conditions	Universal usage, but national variations exist
IHE (Integrating the Healthcare Enterprise)	Framework to promote coordinated use of standards like HL7, DICOM	Europe, North America

4. Major Challenges to EHR Interoperability Across Platforms and Borders

a) Technical Challenges

Legacy Systems and Proprietary Architectures

One of the most significant technical barriers to EHR interoperability is the widespread use of legacy systems built on outdated, proprietary architectures. Many hospitals and clinics continue to operate software that was developed decades ago, often without open APIs or compatibility with modern standards like HL7 FHIR. These systems were designed primarily for local use, not for seamless data sharing with external networks or across borders. As a result, integrating or upgrading them to exchange data with newer platforms becomes costly, risky, and technically complex. Moreover, in some cases, vendors intentionally use closed standards to maintain customer dependency and prevent easy migration.

Inconsistent Implementation of Standards

Although standards such as HL7, FHIR, and DICOM are widely promoted, their implementation varies significantly across vendors and regions. For example, FHIR is a flexible framework that allows multiple approaches to structuring and accessing data, which means two systems claiming to be “FHIR-compliant” might still be incompatible without further customization. These variations stem from the lack of globally enforced conformance profiles and inadequate certification mechanisms. As a result, developers and IT teams must often build bespoke connectors or adapters to make systems talk to one another, which increases cost and introduces additional points of failure.

Terminology and Semantic Mismatches

Semantic interoperability — the ability for systems to interpret exchanged data meaningfully — remains elusive due to inconsistent use of medical terminologies and coding systems. Different EHR systems may use varying vocabularies for diagnoses, lab tests, or medications. For instance, one system may use ICD-10 for disease classification, while another uses SNOMED CT. Similarly, laboratory results might be coded in LOINC in one region and described in plain text in another. Without a robust mapping layer or terminology service, such differences lead to misinterpretation or loss of critical clinical meaning when data is shared between systems.

Metadata and Data Structure Incompatibility

Even when two systems agree on data formats, subtle differences in data structures and metadata handling can prevent effective interoperability. For example, patient identifiers, timestamp formats, measurement units, and gender notations can differ between systems, causing reconciliation errors. A patient may be listed as “M” in one system and “Male” in another, or blood pressure might be recorded in mmHg in one system but as a string or text field in another. These structural inconsistencies require complex transformation logic to standardize, often involving middleware that adds latency, increases error potential, and requires ongoing maintenance.

Limited or Fragmented API Ecosystems

Modern interoperability heavily relies on the availability of standardized APIs (Application Programming Interfaces). However, many EHR vendors either lack robust APIs or provide them only with limited functionality and access restrictions. In some cases, access to APIs is monetized or gated behind licensing terms that are prohibitive for smaller healthcare providers or startups. Even when APIs are available, rate limits, data scope limitations, and lack of real-time support restrict their usefulness. Furthermore, there is no single universal standard governing how these APIs should behave, leading to fragmentation and inconsistent experiences across systems.

Infrastructure and Network Constraints

In many parts of the world, especially in low- and middle-income countries, healthcare facilities lack the necessary IT infrastructure to support interoperable systems. This includes poor internet connectivity, outdated hardware, limited cloud access, and insufficient cybersecurity measures. Even within developed nations, some rural or underfunded facilities operate in disconnected environments or use offline record-keeping systems. These infrastructure limitations make real-time data exchange or cloud-based interoperability solutions impractical, hindering the broader goal of interconnected healthcare ecosystems.

Lack of Standardized Patient Identification

A foundational requirement for interoperability is the accurate and consistent identification of patients across systems. However, the absence of a universally accepted patient ID system — particularly at a global level — results in duplication, mismatches, and mislinked records. Different health systems may identify patients using national IDs, internal hospital IDs, or a combination of personal details like name and birthdate, all of which can vary or be entered incorrectly. The lack of a unified or federated identity management framework makes cross-border or even cross-facility interoperability fragile and error-prone.

Legacy systems: Old EHRs using proprietary formats are not API-compatible.
Lack of standard implementation: Different vendors interpret HL7 or FHIR differently.
Incompatible metadata schemas: Even shared data may have different formats for timestamps, units, or identifiers.

b) Regulatory and Legal Challenges

Variability in Data Privacy Laws Across Jurisdictions

One of the most significant regulatory challenges to EHR interoperability is the wide variation in data privacy laws across countries and even within regions of the same country. For example, the United States enforces the Health Insurance Portability and Accountability Act (HIPAA), which governs how patient data can be stored and shared, while the European Union follows the General Data Protection Regulation (GDPR), which provides patients with broader rights to control their personal health information. These regulations differ in terms of consent requirements, data retention policies, and rights to data deletion. When health data needs to be exchanged across borders—such as between a U.S. provider and an EU-based research center—these regulatory differences create legal barriers that may halt or severely restrict data sharing, even if the technical infrastructure exists.

Data Residency and Sovereignty Laws

Many countries are enacting data localization or data residency laws that require personal health data to be stored within national boundaries. This stems from concerns over surveillance, data misuse, and control over sensitive personal information. Countries like Russia, China, and India have explicit provisions requiring local storage of sensitive personal data, including health records. These laws can severely impact multinational healthcare organizations or global telehealth platforms that rely on centralized cloud services for data storage and access. Even if the data can be technically transferred and used securely, legal restrictions around where that data is stored or processed can prevent interoperability initiatives from scaling globally.

Fragmented Consent Frameworks

Another major hurdle is the inconsistency in how patient consent for data sharing is defined and managed across jurisdictions. In some countries, broad consent is allowed, meaning patients can authorize their data to be shared for a wide range of future uses, including research and secondary data analysis. In others, like parts of the EU under GDPR, only specific, purpose-limited consent is acceptable. This creates problems when EHR systems attempt to share data across different healthcare providers, systems, or countries, particularly when the consent requirements are not aligned. The lack of standardization in consent frameworks makes it difficult to establish universal APIs or data exchange protocols that respect both legal and ethical guidelines in every jurisdiction.

Legal Ambiguity Around Data Ownership

Interoperability is further complicated by legal uncertainties regarding who owns patient data. While in most jurisdictions, patients are considered the rightful owners or custodians of their own health information, the legal and practical control often rests with healthcare providers or EHR vendors. This tension becomes a serious legal challenge when patients attempt to move their data between platforms or countries. Without clear laws on data ownership, patients may find it difficult to access or transfer their own health records, and healthcare systems may be reluctant to release data, fearing legal liability or competitive disadvantage. This ambiguity creates friction in the development of patient-centered interoperable systems.

Regulatory Lag Behind Technological Innovation

Rapid advancements in health IT, such as AI-based diagnostics, wearable devices, and cloud-based health platforms, are often outpacing the development of relevant legal frameworks. This results in a regulatory vacuum where certain types of data or use cases fall outside of existing laws, leaving providers and developers uncertain about compliance. For example, while a new telemedicine platform might enable real-time data sharing using APIs, regulators may not yet have formalized guidelines for cross-jurisdictional telehealth data exchange. This lag hinders innovation, introduces legal risk, and discourages investment in technologies designed to enhance interoperability.

Lack of International Regulatory Harmonization

Despite ongoing global health initiatives, there is no comprehensive, binding international legal framework for health data exchange. Efforts by organizations such as the World Health Organization (WHO) and the Global Digital Health Partnership aim to foster cooperation, but the implementation is inconsistent and largely voluntary. The absence of universally accepted legal norms or treaties governing cross-border health data exchange means that organizations must navigate a patchwork of national and regional laws. This makes international interoperability highly complex, resource-intensive, and prone to legal disputes. Without legal harmonization, achieving seamless, scalable interoperability remains aspirational.

Data privacy regulations vary:
- HIPAA (USA) vs. GDPR (Europe) vs. local acts (India, Japan).
- Data residency rules often prohibit storing health data outside country borders.
Consent management: Countries have different laws on how patients must authorize data sharing.
Cross-border governance: No global oversight for international health data exchange.

c) Economic and Strategic Barriers

Vendor Lock-in and Proprietary Systems

One of the most significant economic barriers to EHR interoperability is the widespread issue of vendor lock-in. Many healthcare organizations are tied to specific EHR vendors whose systems are built using proprietary data formats and interfaces. These vendors often create ecosystems that make it difficult to export data in standardized, interoperable formats. As a result, migrating to a new system or enabling third-party access can be costly, time-consuming, or technically restricted. Vendors may also charge premium fees for API access or integrations, which discourages smaller providers or public institutions from enabling data sharing. This lack of openness strategically benefits the vendor but limits the broader goal of health data fluidity and interoperability across systems.

High Cost of Integration and Maintenance

Establishing and maintaining interoperability between disparate EHR systems requires significant financial investment. Hospitals and clinics need to invest in IT infrastructure, secure API management, data normalization, and integration middleware to translate between incompatible formats. Additionally, ensuring that systems comply with standards such as HL7 FHIR or SNOMED CT requires ongoing resources for development, testing, and certification. These costs are particularly burdensome for small practices, rural hospitals, or institutions in low- and middle-income countries. As a result, many organizations postpone or avoid integration efforts altogether, perpetuating data silos and reducing the efficiency of health information exchange.

Limited Financial Incentives for Data Sharing

In many health systems, especially those operating under fee-for-service models, there is little direct financial reward for enabling interoperability. Sharing patient data with external providers may result in patient leakage, where a patient chooses another provider for follow-up care. This discourages institutions from investing in interoperable systems that may benefit competitors. Moreover, reimbursement models rarely account for the operational and maintenance costs associated with data exchange infrastructure. Without a robust framework of economic incentives—such as government subsidies, performance-based reimbursement, or public-private partnerships—many healthcare organizations deprioritize interoperability in favor of initiatives with clearer returns on investment.

Strategic Misalignment and Competitive Priorities

Healthcare organizations often see data as a strategic asset that offers a competitive advantage, particularly in markets where reputation, research output, or specialized care are key differentiators. This leads to strategic misalignment where individual institutions prioritize control over their patient data rather than contributing to a broader, shared health information ecosystem. Additionally, leadership teams may be reluctant to allocate budgets or change workflows unless interoperability initiatives are aligned with business objectives such as growth, efficiency, or risk mitigation. Without a clear link between interoperability and organizational success metrics, strategic priorities often remain narrowly focused, hampering the adoption of interoperable solutions.

Fragmented Policy and Funding Landscape

Interoperability efforts are further hindered by the fragmented nature of policy and funding environments. In countries without a unified national health IT strategy or funding mechanism, healthcare organizations are left to pursue integration projects in isolation. This leads to inconsistent adoption of standards, duplicated efforts, and missed opportunities for economies of scale. Even in regions with supportive policies, such as the EU or the US, the absence of enforceable timelines or funding continuity creates uncertainty that discourages long-term planning. Public funding is often project-based and may not cover long-term operational expenses, which makes it difficult to sustain interoperability efforts once initial grants expire.

Vendor lock-in: EHR companies may resist interoperability to retain market dominance.
Cost of integration: Small and mid-size clinics often cannot afford APIs, cloud migration, or compliance audits.
Lack of incentives: Many health systems lack reimbursement models that reward data sharing.

5. Case Studies and Regional Efforts

a) United States – 21st Century Cures Act & TEFCA

Overview of the 21st Century Cures Act

The 21st Century Cures Act, signed into law in December 2016, is a landmark piece of U.S. legislation aimed at accelerating medical product development, advancing research, and improving the interoperability and usability of health information technology. One of its most significant impacts in the healthcare IT domain is the push for greater data sharing, reduced information blocking, and increased patient access to health records. The law mandates that certified health IT systems support the secure exchange of electronic health information (EHI) without undue burden or delay, emphasizing the need for open APIs, standardized data formats, and greater transparency from EHR vendors and healthcare providers.

Information Blocking Provisions

A cornerstone of the Act is its prohibition against information blocking, which refers to practices that interfere with, prevent, or materially discourage access to or use of electronic health information. Under this provision, health IT developers, networks, and healthcare providers are legally obligated to provide patients with access to their own health data, unless a clear exception applies (such as privacy or security concerns). The Office of the National Coordinator for Health Information Technology (ONC) enforces this provision and has outlined penalties, including monetary fines and disincentives for non-compliance. This initiative seeks to dismantle data silos and ensure that patients and clinicians can make informed decisions with timely, complete data.

Role of FHIR APIs in Promoting Interoperability

The 21st Century Cures Act mandates the use of HL7 FHIR (Fast Healthcare Interoperability Resources) APIs to facilitate standardized and real-time data exchange between health IT systems. All ONC-certified EHR vendors must provide FHIR-based APIs that allow patients to access their health information through third-party apps (such as mobile health apps). These APIs enable users to retrieve clinical data such as medications, lab results, immunizations, and problem lists in a structured and readable format. This shift toward API-first design has created a more open and modular ecosystem, encouraging innovation in app development, patient portals, and remote care solutions.

Introduction to TEFCA

The Trusted Exchange Framework and Common Agreement (TEFCA) is a complementary policy initiative developed by the ONC in response to the interoperability goals set forth by the 21st Century Cures Act. TEFCA aims to create a national, unified framework for data exchange across disparate health networks. It establishes a governance model and technical framework that allows qualified health information networks (QHINs) to interoperate with one another under a standardized set of rules and trust principles. TEFCA’s primary objective is to simplify and scale health information exchange, especially for providers, payers, and public health agencies that need seamless access to EHI across organizational and geographic boundaries.

Structure and Functions of TEFCA

TEFCA operates through a federated model comprising multiple QHINs, each responsible for managing data exchange within and across participating organizations. The Common Agreement, developed in partnership with the Sequoia Project (acting as the Recognized Coordinating Entity), outlines the legal, technical, and policy terms that all participants must follow. These include data privacy and security standards, patient identity matching requirements, consent policies, and operational obligations. TEFCA supports multiple exchange modalities, including treatment, payment, public health reporting, and individual access, thereby extending interoperability beyond direct clinical care.

Impacts and Future Outlook

Together, the 21st Century Cures Act and TEFCA mark a pivotal shift in U.S. health IT policy, moving the industry toward greater transparency, patient empowerment, and system-wide interoperability. While early implementation has been uneven—due in part to technological disparities, varying levels of readiness, and concerns over compliance burdens—the long-term impact is expected to be transformative. As more QHINs become operational and FHIR APIs mature, the U.S. healthcare system will be better positioned to support coordinated care, value-based models, and data-driven research. Moreover, these frameworks serve as a potential model for other countries seeking scalable and secure approaches to national health data exchange.

Mandates patient access to health data and penalizes “information blocking”.
TEFCA aims to create a national “network of networks”.
FHIR APIs now required by ONC for certified EHRs.

b) European Union – eHealth Digital Service Infrastructure (eHDSI)

Overview of eHDSI (eHealth Digital Service Infrastructure)
The eHealth Digital Service Infrastructure (eHDSI) is a strategic European Union initiative developed under the framework of the Connecting Europe Facility (CEF) to enable secure, efficient, and standardized cross-border health data exchange between EU Member States. Its primary goal is to ensure that European citizens can access and share their health data—such as electronic prescriptions and patient summaries—when traveling or living in another EU country. This infrastructure forms the technical and legal backbone of the MyHealth@EU network, which is the brand name used for public communication of eHDSI.

Key Services: ePrescription and Patient Summary
eHDSI currently supports two core cross-border digital health services. First is the ePrescription/eDispensation service, which allows a patient to receive medication in another EU country that was prescribed electronically in their home country. The second is the Patient Summary service, which provides essential health information (e.g., allergies, current medications, medical history) to a healthcare provider abroad in a language-neutral and standardized format. These services aim to improve care continuity, avoid medication errors, and support emergency medical treatment while abroad.

Technical Architecture and Standardization
The eHDSI functions through a decentralized architecture where each participating country implements a National Contact Point for eHealth (NCPeH). These NCPs act as gateways to connect domestic healthcare systems with those of other EU countries. The infrastructure relies heavily on international standards such as HL7 CDA, LOINC, SNOMED CT, and ICD-10, which are harmonized and mapped through centrally maintained Master Value Sets Catalogue (MVC) and Master Translation Catalogue (MTC). These ensure that medical terminology and structured data are correctly interpreted across languages and systems.

Governance and Legal Framework
The implementation of eHDSI is governed by the eHealth Network, a voluntary collaboration of EU Member States established under Article 14 of Directive 2011/24/EU on patients’ rights in cross-border healthcare. The eHealth Network defines the rules, standards, and requirements that each country must follow to become a participating node in the eHDSI. These requirements cover areas such as data privacy (in compliance with GDPR), patient consent, security protocols, and semantic interoperability. Additionally, the Joint Action projects (such as X-eHealth and Joint Action eHDSI) are used to support technical development and onboarding of new Member States.

Progress and Adoption Status
As of 2025, more than 20 EU countries have begun implementing one or both eHDSI services, with gradual expansion expected. Countries like Estonia, Finland, Croatia, Portugal, Czech Republic, and Luxembourg have been early adopters. Real-life use cases demonstrate patients receiving prescriptions abroad or healthcare providers accessing patient summaries in emergency situations, with clear benefits in terms of care quality and efficiency. However, full-scale implementation across all 27 EU Member States is ongoing, with challenges such as national policy alignment, local system integration, and semantic harmonization still to be addressed.

Impact and Future Vision
The long-term vision for eHDSI aligns with the broader strategy of the European Health Data Space (EHDS). While eHDSI focuses on clinical data exchange for direct patient care, EHDS aims to expand this to secondary uses like research, policymaking, and AI development. The success of eHDSI provides a foundational infrastructure upon which future capabilities, including genomic data sharing and AI-powered decision support, can be built. The initiative represents a key step in creating a digitally unified European healthcare ecosystem where citizens enjoy secure, portable, and high-quality digital health services regardless of borders.

Cross-border health data sharing via MyHealth@EU initiative.
Standardizes ePrescription and Patient Summary formats.
Aligns with GDPR for privacy control.

c) India – Ayushman Bharat Digital Mission (ABDM)

Overview of Ayushman Bharat Digital Mission (ABDM)

The Ayushman Bharat Digital Mission (ABDM), launched by the Government of India in 2021, is a flagship initiative under the Ministry of Health and Family Welfare aimed at building a robust digital health ecosystem. It seeks to digitally integrate the entire healthcare system in India by connecting patients, healthcare providers, insurance systems, and other stakeholders. The mission focuses on providing every Indian citizen with a unique Ayushman Bharat Health Account (ABHA) number that serves as a digital health identity, facilitating seamless access to health records and services across different care settings.

Objectives and Vision

The primary goal of ABDM is to create an integrated digital health infrastructure that bridges the gap between multiple healthcare stakeholders using open and interoperable standards. It aims to enable the secure exchange of health information with patient consent and promote portability of health records across states and healthcare institutions. The mission supports India’s broader vision of Universal Health Coverage (UHC) by enhancing efficiency, accessibility, inclusiveness, and transparency in healthcare delivery, particularly in rural and underserved areas.

Key Components of ABDM

ABDM consists of several foundational building blocks designed for interoperability and scalability. These include the ABHA Number, which serves as a unique digital health ID for every individual; the Health Facility Registry (HFR), a repository of all registered public and private health facilities; the Healthcare Professionals Registry (HPR), a database of licensed doctors and healthcare workers; and the Personal Health Record (PHR) system that enables citizens to store, access, and share health data securely. Additionally, the Unified Health Interface (UHI) acts as a digital platform facilitating discovery and delivery of health services across the country.

Use of Open Standards and Interoperability

ABDM is built using open digital health architecture and adheres to global standards like HL7 FHIR (Fast Healthcare Interoperability Resources), SNOMED CT, and LOINC for clinical terminologies. These standards ensure that health information is interoperable, portable, and semantically meaningful across different IT systems and providers. The mission is also aligned with India’s National Digital Health Blueprint (NDHB), which outlines governance, privacy, security, and consent management protocols for digital health data exchange.

Data Privacy, Consent, and Governance

One of the critical pillars of ABDM is its consent-based data sharing model. Health data can only be shared or accessed with the explicit consent of the individual, which is managed through digital consent artifacts and governed under the Health Data Management Policy. This aligns with India’s draft Digital Personal Data Protection Act and emphasizes data minimization, purpose limitation, and user control. ABDM operates under a federated architecture, ensuring that data remains within the custody of the data principal or the originating facility rather than being stored in a centralized repository.

Public and Private Sector Participation

ABDM is designed to be a public digital infrastructure that encourages both government and private entities to build applications and services on top of its open APIs. Startups, hospitals, insurers, health tech companies, and diagnostic labs can integrate with ABDM using sandbox environments provided by the National Health Authority (NHA). This open ecosystem approach fosters innovation and competition, enabling the development of diverse digital health solutions like teleconsultation apps, online pharmacies, insurance claim systems, and AI-based clinical tools.

Implementation Progress and Challenges

As of mid-2025, over 50 crore ABHA IDs have been generated, and thousands of healthcare professionals and facilities are enrolled in the HPR and HFR, respectively. Pilot projects have demonstrated effective use cases in states like Andhra Pradesh, Maharashtra, and Kerala. However, challenges remain, including limited digital infrastructure in rural areas, digital literacy gaps among both patients and providers, and concerns about data protection and operational sustainability. Addressing these challenges is key to ensuring equitable and inclusive implementation of the mission.

Future Outlook

The future of ABDM lies in its ability to scale responsibly while maintaining privacy, interoperability, and accessibility. Integration with health insurance programs like Ayushman Bharat – Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), along with linkages to the CoWIN platform, Aarogya Setu, and the eSanjeevani telemedicine platform, are already underway. Looking ahead, ABDM is poised to transform India’s healthcare delivery model into one that is data-driven, patient-centric, and digitally enabled, positioning the country as a global leader in digital public health infrastructure.

Launched a nationwide Health ID (ABHA) and Unified Health Interface (UHI) using open standards.
Adopts FHIR and SNOMED CT.
Aims for a federated and inclusive health data ecosystem.

d) Global – WHO Global Strategy on Digital Health (2020–2025)

Background and Purpose

The World Health Organization (WHO) launched the Global Strategy on Digital Health 2020–2025 to guide member states in developing digital health ecosystems that strengthen health systems and promote universal health coverage (UHC). The strategy was adopted to recognize the growing importance of digital technologies in achieving global health goals and to ensure that digital health solutions are safe, effective, accessible, and equitable. Its core aim is to advance the use of digital technologies to improve health outcomes globally, especially in low- and middle-income countries (LMICs) where digital transformation is often underfunded or fragmented.

Vision and Strategic Objectives

The WHO envisions a world where digital health is universally accessible, supports healthier populations, and contributes to the achievement of the Sustainable Development Goals (SDGs). The strategy sets out four strategic objectives:

Promote global collaboration and advance the transfer of knowledge;
Advance the implementation of national digital health strategies;
Strengthen governance for digital health at global, regional, and national levels;
Advocate for people-centered digital health systems that protect individual rights and ensure equitable access. These objectives are supported by the principle of “leave no one behind” and emphasize inclusivity, security, interoperability, and ethical data use.

Priority Areas and Implementation Framework

The strategy focuses on several priority areas: the development of digital health infrastructure, creation of enabling policies and legislation, implementation of interoperability standards (such as HL7 FHIR and SNOMED CT), investment in workforce digital literacy, and scaling up of proven digital health solutions like telemedicine and mobile health (mHealth). WHO recommends that each country align its national digital health strategy with this global framework. The implementation roadmap includes timelines, tools, and metrics to measure progress. WHO also provides technical assistance, policy guidelines, and readiness assessment tools to support countries.

Governance, Data Protection, and Ethics

A strong emphasis is placed on governance and regulation to ensure that digital health initiatives protect patient privacy and data security. The strategy advocates for clear national policies on data protection, cybersecurity, cross-border data flows, and informed consent. Ethical considerations are central, especially around AI-enabled technologies and patient surveillance. The WHO encourages governments to adopt regulatory frameworks that ensure transparency, accountability, and alignment with human rights principles, thereby building public trust in digital health solutions.

Global Cooperation and Capacity Building

The strategy underlines the importance of international collaboration between countries, development agencies, private sector stakeholders, academia, and civil society. It supports knowledge sharing, technology transfer, and the use of open-source platforms to reduce duplication and cost. WHO promotes capacity-building efforts, including the training of digital health professionals, establishment of public-private partnerships, and development of local innovation ecosystems that can sustain long-term digital health interventions.

Monitoring and Evaluation

To ensure accountability, WHO introduced a monitoring framework using indicators related to infrastructure development, workforce capacity, interoperability, regulatory maturity, and actual health outcomes. Countries are encouraged to conduct digital health maturity assessments regularly. The goal is not just to track technology adoption but also to assess how effectively digital tools are integrated into healthcare delivery and whether they lead to better clinical outcomes and patient satisfaction.

Impact and Challenges

While the strategy has provided a solid foundation for coordinated global action, its success has varied by country. High-income countries have made significant strides in adopting and regulating digital health, while many low- and middle-income countries still face challenges such as poor infrastructure, lack of technical expertise, and fragmented digital ecosystems. The COVID-19 pandemic accelerated digital health adoption but also exposed inequalities in access and readiness. WHO continues to address these gaps through advocacy, funding mobilization, and regional technical support.

Conclusion and Future Directions

The WHO Global Strategy on Digital Health (2020–2025) is a landmark initiative that lays out a structured, inclusive, and future-ready approach to harness digital technologies in healthcare. As it approaches its final implementation year, WHO is expected to publish an updated version or follow-up strategy that builds on lessons learned, integrates emerging technologies like generative AI and precision medicine, and continues to promote ethical, equitable, and interoperable digital health systems worldwide.

Promotes use of global standards, especially for LMICs (Low and Middle-Income Countries).
Supports capacity building and interoperability tools.

6. Technical Frameworks and Solutions

a) SMART on FHIR

1. Introduction to SMART on FHIR
SMART on FHIR is a widely adopted open standard that enables the development of secure, interoperable healthcare applications capable of integrating with Electronic Health Records (EHRs) and other health IT systems. It stands for Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (FHIR). Developed by the SMART Health IT project in partnership with HL7, SMART on FHIR leverages modern web technologies and standardized data models to enable a plug-and-play app ecosystem across different healthcare environments.

2. Technical Foundations
At its core, SMART on FHIR combines HL7’s FHIR standard for structured healthcare data exchange with secure authorization protocols such as OAuth 2.0 and OpenID Connect. This integration allows third-party apps to safely access clinical data in EHR systems through RESTful APIs. Developers can create apps that interact with a patient’s lab results, medications, allergies, or even entire medical history, without worrying about vendor-specific customizations. SMART on FHIR also defines a set of FHIR profiles and scopes that help control data access based on user roles and use cases.

3. Key Functionalities
SMART on FHIR enables single sign-on (SSO) capabilities and context-aware launching, meaning apps can be launched directly from within an EHR or patient portal, with relevant patient or encounter data already in context. This creates a seamless workflow for clinicians, allowing them to access tools such as clinical calculators, medication support apps, or patient education modules within the same interface they use daily. The framework ensures that access is role-based—what a physician can access may differ from what a patient sees.

4. Interoperability and Vendor Adoption
Major EHR vendors like Epic, Cerner, Athenahealth, and Allscripts have incorporated SMART on FHIR into their platforms, largely driven by compliance requirements under the U.S. 21st Century Cures Act and ONC interoperability rules. This wide adoption has allowed developers to build once and deploy across multiple health systems without rewriting code for each environment. SMART on FHIR has become the de facto standard for interoperable health apps in the United States and is being explored globally in countries like Canada, the UK, and Australia.

5. Use Cases in Healthcare
SMART on FHIR supports numerous use cases, including clinical decision support, remote patient monitoring, patient-facing apps, care coordination, and public health reporting. For example, an app could alert a provider to potential medication interactions based on a patient’s current prescriptions, or a diabetes management app could pull blood glucose readings from a connected device and display them in the EHR. In research, SMART-enabled apps can help extract de-identified patient cohorts based on structured clinical criteria.

6. Security and Privacy Considerations
Security is a central component of the SMART on FHIR architecture. By using OAuth 2.0, the framework ensures that applications must request and be granted specific permissions (scopes) to access user data. It also supports token-based authentication and allows for robust auditing and revocation mechanisms. Because patient privacy regulations such as HIPAA (in the U.S.) and GDPR (in the EU) impose strict rules on data sharing, SMART on FHIR helps enforce these regulations through technical guardrails built into the app launch and data access workflows.

7. Challenges and Limitations
Despite its strengths, SMART on FHIR faces challenges such as inconsistent implementation across vendors, limitations in available data fields (especially in older EHRs), and the need for better developer tools and documentation. Another concern is the lack of universal standards for how patients manage app permissions, which can result in usability and trust issues. Furthermore, while SMART on FHIR supports robust authorization, it does not itself address broader governance issues like consent management, data residency, or cross-border data sharing.

8. Future Directions
The future of SMART on FHIR includes expanding support for bulk data access (via the FHIR Flat File Export or “Flat FHIR”) to enable population health management and research. There is also a growing interest in extending SMART capabilities beyond traditional EHRs to include wearables, IoT devices, genomic platforms, and public health systems. The SMART Health Cards initiative, which builds upon SMART on FHIR for sharing verifiable health information like COVID-19 vaccination records, exemplifies how the framework can extend to consumer-facing and global use cases.

9. Conclusion
SMART on FHIR represents a transformative leap in healthcare IT by creating a standardized, secure, and scalable way for applications to interact with health data. Its ability to bridge gaps between disparate systems and enable innovation through third-party apps is helping reshape the digital health landscape. As adoption grows and the ecosystem matures, SMART on FHIR is likely to play a foundational role in achieving true interoperability and advancing patient-centered, data-driven care.

Adds authentication and app integration capabilities to FHIR.
Enables third-party apps to safely interact with EHRs (e.g., Apple Health).

b) OpenEHR

Introduction to OpenEHR

OpenEHR is an open standard specification designed for the storage, exchange, and management of electronic health records (EHRs). Unlike traditional EHR systems that tightly couple software with data models, OpenEHR provides a platform-independent, vendor-neutral architecture that separates clinical knowledge (what is recorded) from technical implementation (how it is stored and processed). The core idea is to create interoperable, reusable, and long-term computable health records that can evolve with clinical practice over time.

Architecture and Core Components

The OpenEHR framework is built on a multi-level modeling approach. It separates information into three layers: Reference Model, Archetypes, and Templates. The Reference Model defines the technical structure for health data, while Archetypes define the clinical content such as blood pressure or allergies using domain expert inputs. Templates are combinations of archetypes tailored for specific forms or use cases. This structure allows clinical professionals—not just software developers—to define how clinical concepts should be represented and exchanged, improving clinical relevance, adaptability, and semantic interoperability.

Advantages of OpenEHR

OpenEHR provides several strategic benefits. First, it supports data longevity and vendor-independence, ensuring that clinical data remains usable even as technology platforms change. Second, the use of archetypes and templates allows for flexible customization without altering source code, making system updates and localization easier. Third, OpenEHR enables semantic interoperability by standardizing clinical concepts across systems and regions. This makes it highly suitable for research, public health reporting, and clinical decision support systems.

Adoption and Global Use Cases

Several countries and healthcare organizations have adopted or piloted OpenEHR. The United Kingdom’s National Health Service (NHS) has adopted it in regions to future-proof EHR investments. Australia, Norway, Slovenia, and Brazil have also embraced OpenEHR for national health record initiatives. In addition, it has been successfully implemented in large hospital systems, military health systems, and regional health information exchanges. The open and collaborative nature of the OpenEHR community encourages participation from clinicians, developers, and policy-makers, leading to a continuously improving and expanding ecosystem.

Technical Implementation and Tools

OpenEHR can be implemented using a variety of programming languages and database backends. Tools such as EHRbase, openEHR REST APIs, and Archetype Designer allow developers to build robust systems that comply with the standard. These tools enable integration with existing hospital information systems (HIS), mobile health apps, and decision support engines. The OpenEHR Foundation also provides an open governance framework and supports a community-led Clinical Knowledge Manager (CKM) where archetypes and templates are shared, reviewed, and published collaboratively.

Challenges and Considerations

Despite its strengths, OpenEHR also presents certain challenges. Its learning curve can be steep for teams unfamiliar with archetype-driven development. Integration with traditional EHR systems may require significant adaptation or middleware solutions. Moreover, the success of an OpenEHR implementation depends heavily on well-governed clinical modeling and stakeholder collaboration. Without sufficient investment in training, clinical validation, and governance, organizations may struggle to achieve the full benefits of the model.

Future Outlook

As healthcare moves toward patient-centered, data-driven, and interoperable systems, OpenEHR is increasingly being seen as a foundational standard for long-term health data management. Its focus on semantic consistency and reusability aligns with global initiatives for digital health transformation. In the future, OpenEHR may be used more extensively in conjunction with other standards like HL7 FHIR for API-level exchange, creating a hybrid model that balances data persistence and real-time access. With the rise of AI and advanced analytics in healthcare, OpenEHR’s structured data approach also holds promise for training and deploying clinical decision support models.

Focuses on long-term clinical data modeling and computable care pathways.
Used by NHS England, Australia, and some Nordic countries.

c) API Gateways & Middleware

Definition and Role in Healthcare IT

API Gateways and Middleware in healthcare serve as crucial intermediaries that facilitate secure and efficient communication between disparate healthcare systems, such as Electronic Health Records (EHRs), mobile health apps, laboratory information systems, and payer databases. In simple terms, an API gateway acts as a centralized interface through which all API requests from external systems are routed, managed, authenticated, and logged. Middleware, on the other hand, functions as the software layer that transforms, maps, and transports data between systems with differing architectures, standards, or formats. In a healthcare context, these tools help bridge gaps between legacy systems and modern, standards-compliant solutions like HL7 FHIR APIs.

Importance for EHR Interoperability

Healthcare organizations operate a variety of information systems built over decades, often with proprietary or incompatible data formats. Middleware and API gateways address this heterogeneity by enabling real-time data translation, normalization, and secure routing. For instance, a lab result from an HL7 v2 system can be converted into a FHIR resource and sent to a modern EHR or mobile app without data loss or format conflict. These platforms also ensure that data conforms to terminology standards such as LOINC or SNOMED CT during the transfer, enabling semantic interoperability. This functionality is particularly essential when integrating third-party systems like telemedicine platforms, wearables, or health information exchanges (HIEs).

Key Functionalities and Capabilities

API gateways typically offer features like request throttling, load balancing, security enforcement (OAuth 2.0, OpenID Connect), audit logging, and version control. Middleware extends these capabilities by offering message transformation (e.g., JSON to XML), data enrichment (adding missing fields), protocol mediation (e.g., HTTP ↔ SOAP), and routing logic. In healthcare, these capabilities are customized to meet regulatory requirements such as HIPAA or GDPR, ensuring that only authorized users access sensitive patient data and that audit trails are maintained for compliance. Many middleware platforms also support consent management modules, enabling patient-controlled data access and usage transparency.

Leading Vendors and Tools in the Market

Several commercial and open-source API and middleware platforms are actively used in healthcare IT ecosystems. Redox and Health Gorilla are notable vendors that specialize in healthcare data exchange using FHIR and legacy protocol translation. Others like Mirth Connect (by NextGen Healthcare) and InterSystems Ensemble offer robust HL7 integration engines and middleware services. General-purpose API gateway providers like Apigee (Google Cloud), AWS API Gateway, and Microsoft Azure API Management are also widely used, with healthcare-specific configurations and compliance modules. These platforms are often integrated into cloud-based EHR environments, enabling scalable and flexible data exchange across health systems.

Challenges and Considerations in Implementation

Despite their benefits, deploying API gateways and middleware in healthcare comes with challenges. Integration complexity is a major hurdle, especially when connecting deeply customized legacy systems. There is also the issue of vendor lock-in, where organizations become reliant on proprietary middleware or APIs that are costly to replace or scale. Security is another concern, as middleware platforms become central access points that must be protected against breaches, denial-of-service attacks, and misconfigurations. Furthermore, maintaining real-time data accuracy and synchronization across systems adds to operational complexity. Successful implementation requires a strong governance framework, skilled IT personnel, and ongoing auditing to ensure reliability and compliance.

Future Trends and Strategic Importance

Looking forward, API gateways and middleware will play an even more strategic role in the evolution of digital health. As AI-driven tools, mobile health apps, and global health data networks proliferate, the need for seamless, real-time data integration across systems will intensify. Modern middleware solutions are increasingly adopting event-driven architectures, microservices, and edge computing capabilities to handle high-throughput, low-latency transactions. The adoption of FHIR APIs as regulatory mandates (e.g., ONC’s final rule in the US) will also shift middleware design toward greater standardization and modularity. Ultimately, API gateways and middleware will serve as the backbone of a truly interoperable, patient-centered, and intelligent global healthcare ecosystem.

Vendors like Redox, Particle Health, and Health Gorilla offer data normalization, translation, and routing.
Acts as a bridge between incompatible systems.

d) Cloud-based EHRs

Introduction to Cloud-Based EHRs

Cloud-based Electronic Health Records (EHRs) are medical record systems hosted on remote servers and accessed via the internet, rather than being stored on local hospital servers or on-premises data centers. These solutions use cloud infrastructure—typically provided by major vendors like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)—to deliver health IT services. The core goal is to provide a secure, scalable, and accessible environment for healthcare providers to manage patient information, coordinate care, and comply with regulatory standards, while reducing the burden of maintaining local IT infrastructure.

Advantages of Cloud-Based EHRs

One of the most significant advantages of cloud-based EHRs is scalability. Healthcare organizations can expand or reduce their storage and computing resources on demand, paying only for what they use. This is particularly beneficial for small practices and clinics that cannot afford large upfront investments in IT infrastructure. Additionally, cloud EHRs enable real-time data synchronization across multiple locations, enhancing care coordination and reducing clinical errors. The cloud model also simplifies software updates, as vendors can roll out new features or compliance patches universally and instantly, without the need for individual installations.

Data Security and Compliance

Cloud-based EHRs often come with built-in data security features such as encryption at rest and in transit, role-based access control, automatic backups, and disaster recovery options. These systems are typically designed to meet strict regulatory requirements like HIPAA (USA), GDPR (Europe), and ISO/IEC 27001 for information security management. Major cloud service providers also offer compliance certifications and audit trails, allowing healthcare organizations to demonstrate adherence to privacy laws. However, data security still depends heavily on how these systems are configured and managed by the healthcare provider.

Integration and Interoperability

Modern cloud-based EHRs are typically built with interoperability in mind. They often support standards like HL7 FHIR, SMART on FHIR, and Open APIs that enable seamless integration with third-party applications, diagnostic devices, telemedicine platforms, and public health reporting tools. Cloud infrastructure allows these systems to process large volumes of structured and unstructured data, making them suitable for supporting population health management, AI-driven diagnostics, and personalized medicine. Interoperable cloud-based systems are also better equipped to support regional or national health information exchanges.

Cost and Operational Considerations

From a financial standpoint, cloud-based EHRs usually follow a subscription or pay-as-you-go model, reducing capital expenditures and converting IT costs into predictable operational expenses. This model includes costs for hosting, maintenance, support, and licensing. While cloud EHRs can be more affordable in the long term, initial migration costs—such as data transfer, staff training, and custom integrations—can be significant. Additionally, organizations must consider internet connectivity and bandwidth requirements, as cloud EHR performance relies heavily on stable and fast network access.

Vendor Landscape and Market Trends

The market for cloud-based EHRs is growing rapidly, with leading vendors like Epic (via Cosmos and Garden Plot), Cerner (Oracle Cloud), Athenahealth, eClinicalWorks, and NextGen Healthcare offering full-suite cloud solutions. Startups and digital-first platforms such as Elation Health, Practice Fusion, and DrChrono are also innovating in this space. Major cloud providers are increasingly partnering with EHR vendors to provide healthcare-specific infrastructure, analytics, and AI capabilities. As of 2025, more than 70% of new EHR implementations in high-income countries are cloud-based, with growing adoption in middle-income regions due to their lower barrier to entry and ease of deployment.

Challenges and Risks

Despite their benefits, cloud-based EHRs come with potential risks and challenges. These include data sovereignty issues—where laws may prohibit storage of patient data in foreign jurisdictions—as well as vendor lock-in, where switching between cloud providers becomes technically and contractually difficult. There are also concerns about service outages and reliance on third-party providers for mission-critical systems. In rural or low-connectivity areas, dependence on cloud infrastructure may be impractical, necessitating hybrid or offline-compatible solutions. Ensuring that the cloud-based EHR provider has robust service-level agreements (SLAs) and uptime guarantees is essential for maintaining trust and reliability.

Vendors like Epic, Cerner, and Athenahealth are shifting to cloud-hosted platforms with interoperable APIs.
Cloud-based models enhance scalability and compliance updates.

7. Recommendations to Achieve True EHR Interoperability

a) Policy-Level

Establishing Global Governance Frameworks

One of the most pressing policy-level actions to ensure interoperability of Electronic Health Records (EHRs) across platforms and borders is the establishment of global governance frameworks. At present, the international exchange of health data is largely uncoordinated, with countries operating under their own sets of rules and standards. A global framework—ideally driven by intergovernmental organizations such as the World Health Organization (WHO), the G20, or the Organisation for Economic Co-operation and Development (OECD)—could provide a foundational set of principles, security benchmarks, and interoperability requirements. Such a framework would serve as a baseline that nations could adapt to their specific legal and healthcare contexts, helping align cross-border exchange without imposing a one-size-fits-all model. It would also support coordination during global health crises, where data fluidity across countries becomes essential.

Enforcing Data Portability and Anti-Information Blocking Laws

Data portability—giving individuals the right to obtain and transfer their health data between providers and platforms—is central to interoperability. Policies modeled on the European Union’s General Data Protection Regulation (GDPR) and the United States’ 21st Century Cures Act can play a critical role in enforcing this right. These laws should prohibit practices such as information blocking, where vendors or providers intentionally prevent the free flow of data. Enforcing such rules requires not only clear legal definitions of what constitutes information blocking but also a regulatory body with the authority to audit, penalize, and enforce compliance. Globally harmonized data portability regulations would empower patients and care providers to control and access data across systems, even in different countries.

Incentivizing Adoption of Interoperable Systems

Many healthcare organizations, particularly in low- and middle-income countries (LMICs), struggle to adopt interoperable systems due to financial and technical constraints. Policymakers can address this by creating incentive structures—such as grants, subsidies, and tax benefits—to encourage EHR vendors and healthcare institutions to adopt standards like HL7 FHIR, SNOMED CT, and LOINC. Governments and international development agencies (e.g., the World Bank, USAID) can fund programs that assist hospitals in migrating from legacy systems to interoperable platforms. These incentives should be tied to measurable benchmarks, such as the completion of interoperability assessments or integration with national health information exchanges.

Promoting Public-Private Collaboration

Government policy can create environments that promote meaningful public-private collaboration in interoperability projects. Policymakers can establish consortia that include government agencies, technology vendors, healthcare providers, and standards bodies. These collaborative frameworks can serve as incubators for scalable, testable solutions that align with national health objectives. Public-private partnerships (PPPs) can also address innovation gaps, for example by co-developing open-source interoperability toolkits or shared cloud infrastructure for rural health systems. Moreover, governments can use their purchasing power to set interoperability requirements in procurement contracts, thereby nudging the entire health tech industry toward common standards.

Aligning Cross-Border Data Regulations

As patient mobility increases through medical tourism, international employment, and refugee migration, there is an urgent need to align cross-border data exchange policies. National laws on data protection, storage, and sharing often conflict with one another, creating legal roadblocks for international interoperability. Policymakers need to negotiate bilateral or multilateral agreements that define how health data can be legally transferred across jurisdictions while maintaining privacy and security. These agreements must address data localization laws, consent frameworks, breach notification responsibilities, and third-party data access. Regional examples like the EU’s MyHealth@EU initiative provide templates for how cross-border data sharing can be securely regulated at a policy level.

Standardizing Certification and Compliance Protocols

A consistent global policy framework also requires a standardized approach to EHR certification and compliance. Just as electrical equipment must conform to safety standards, health IT systems should meet minimum interoperability and security requirements before being deployed. Policymakers should collaborate with international standards organizations (e.g., ISO, HL7, IHE) to define technical certification pathways. Once defined, governments can create certification bodies or authorize existing ones to conduct regular compliance assessments. This approach helps build trust in the health information ecosystem, enabling safe integration of diverse EHR systems across different providers and nations.

Establish global governance frameworks for health data exchange (e.g., G20 digital health summit outcomes).
Encourage data portability mandates similar to GDPR or ONC rules.
Promote public-private partnerships for funding open standards adoption.

b) Technical-Level

Standardized FHIR API Implementation

One of the most critical steps toward interoperability is mandating the adoption of standardized FHIR (Fast Healthcare Interoperability Resources) APIs across all healthcare IT systems. FHIR provides a modern, web-based framework using RESTful APIs and JSON/XML formats, enabling real-time data exchange. By enforcing uniform implementation guides and requiring vendors to use consistent FHIR profiles, organizations can reduce variability and ensure that clinical data—such as medications, allergies, vitals, and encounters—are structured in a universally understandable way. Governments like the U.S. (via ONC) and India (via ABDM) have already made FHIR API implementation a legal requirement, which is driving broader industry compliance.

Interoperability Testing and Certification

Developing standardized interfaces is only part of the solution. Ensuring that systems interact correctly in real-world settings requires extensive conformance testing and certification. Interoperability test beds, such as the IHE Connectathon and SMART Health IT Sandbox, allow vendors and institutions to validate their solutions against accepted standards under simulated and live conditions. Certification programs from organizations like the ONC (Office of the National Coordinator for Health IT) or EuroRec in Europe help validate that systems not only meet technical requirements but also maintain security and usability during data exchange. Such structured testing is essential to avoid misinterpretations or mismatches in clinical information during integration.

Terminology Mapping and Data Normalization

Despite standardization efforts, different systems often use varying medical terminologies. One EHR might use SNOMED CT codes while another uses ICD-10 or LOINC for representing clinical concepts. To overcome this challenge, technical solutions must include robust terminology mapping and data normalization engines. These tools translate data from one coding system to another while preserving clinical meaning. Middleware platforms and open-source tools like OpenMRS or Apelon Terminology Services are increasingly being adopted to automate this translation process. Accurate mapping ensures semantic interoperability, enabling systems to “understand” each other’s data, not just exchange it.

Use of API Gateways and Integration Middleware

Many legacy healthcare systems lack native support for FHIR or modern APIs. To bridge this gap, organizations are implementing middleware platforms and API gateways that act as intermediaries. These tools extract data from legacy systems, convert it into standard formats (like FHIR), and manage secure API exposure for external applications. Companies such as Redox, Health Gorilla, and InterSystems provide API infrastructure to handle data normalization, token-based access control, rate limiting, and audit logging. By centralizing integration logic, these platforms significantly reduce the burden on hospitals to directly upgrade or replace their core EHR systems.

Transition to Cloud-Based Architectures

A fundamental technical enabler of interoperability is the shift from on-premises EHRs to cloud-native or hybrid-cloud architectures. Cloud platforms such as Microsoft Azure for Health, Amazon HealthLake, and Google Cloud Healthcare API offer scalable environments with built-in support for FHIR, AI/ML analytics, and data compliance frameworks. These platforms allow easier API access, support global scalability, enable real-time data sharing, and simplify compliance updates. Cloud hosting also facilitates disaster recovery, backup, and security management, which are vital for trusted cross-border data exchange. The transition to cloud-based EHRs is particularly critical for smaller healthcare organizations that lack internal IT resources.

Adoption of Open-Source and Modular Tools

Another key technical strategy is the adoption of open-source frameworks and modular health IT solutions that promote plug-and-play interoperability. Platforms like OpenEHR, OpenMRS, and DHIS2 provide reusable, standards-compliant components for clinical data modeling, storage, and exchange. These tools empower national health systems and startups to develop interoperable health records without depending on proprietary software. Moreover, open-source ecosystems foster community-driven innovation, accelerate updates, and ensure long-term sustainability, especially in resource-limited settings. Modular toolkits also enable faster customization for local languages, workflows, and regulatory environments, which is essential in cross-border health initiatives.

Mandate standardized FHIR API layers for all new and legacy EHR systems.
Create interoperability test beds and certification (e.g., IHE Connectathons).
Develop open-source tools for mapping between terminologies like SNOMED CT ↔ ICD ↔ LOINC.

c) Patient-Level

Patient Empowerment through Data Ownership

One of the most transformative aspects of EHR interoperability is the shift toward patient-centric health data management. Rather than restricting medical information within hospital or clinic systems, modern interoperability strategies advocate for granting patients full access to and control over their own health records. This includes lab results, imaging, prescriptions, clinical notes, and even wearable device data. Empowered with this access, patients can better manage chronic conditions, share data with second-opinion providers, and improve treatment adherence. Initiatives like the U.S. Blue Button project and India’s ABHA (Ayushman Bharat Health Account) are built around this idea of data democratization, where patients can seamlessly carry and share their health history with consent.

Portable Personal Health Records (PHRs)

A core element of patient-level interoperability is the development and adoption of Portable Personal Health Records (PHRs), which are digital repositories that aggregate health data from multiple sources and store it in a unified, patient-controlled format. These records can be mobile app-based (e.g., Apple Health, Google Fit), web-based portals provided by insurers or hospitals, or part of government health ID programs. The goal is to enable a lifelong, longitudinal record accessible through authenticated platforms. However, their effectiveness relies heavily on underlying EHR systems supporting standards like HL7 FHIR, and on enabling patients to download or transmit their data in structured, machine-readable formats.

Consent Management and Privacy Control

Ensuring transparent and granular consent mechanisms is a major requirement at the patient level, especially when EHRs are exchanged across borders or used for secondary purposes such as research. Effective consent systems allow patients to specify who can access which parts of their data, for what purpose, and for how long. Technologies such as smart contracts and blockchain-based health ID systems are emerging to support secure, traceable, and revocable permissions. In jurisdictions under regulations like the GDPR, patients also have the “right to be forgotten” and the right to request corrections in their health data, making consent workflows and audit trails essential for compliance.

Literacy and Accessibility Challenges

Despite technological advances, digital health literacy remains a significant barrier to meaningful patient-level interoperability. Many patients, especially the elderly, those in rural areas, or those with lower education levels, struggle to access, understand, or utilize digital records. User interfaces must be inclusive, intuitive, and available in multiple languages to ensure no patient is left behind. Additionally, accessibility tools such as voice interfaces, large-font displays, and AI-driven summaries can improve usability for people with disabilities or complex health conditions. Bridging the digital divide is essential for ensuring that patient-centric interoperability does not inadvertently widen existing health inequities.

Trust and Ethical Considerations

Finally, building trust in digital health systems is fundamental for successful patient-level interoperability. Patients need confidence that their data is being stored securely, used ethically, and protected from misuse by insurers, employers, or hackers. Transparency in data-sharing policies, proactive communication of privacy practices, and providing visible control dashboards can enhance patient trust. Moreover, public education campaigns and physician-patient dialogue are needed to reassure individuals about the safety and benefits of data-sharing across interoperable networks. Without trust, even the most advanced technological infrastructure will fail to achieve meaningful patient engagement and participation.

Empower patients via personal health records (PHRs) accessible on mobile devices.
Use blockchain or smart contracts to manage consent and audit trails securely.

8. Comparative Summary: Interoperability Models

Centralized Interoperability Model

In a centralized interoperability model, all healthcare data from participating organizations is collected, stored, and managed within a central repository maintained by a single governing body or authority. Countries like Estonia have adopted this model successfully, where a national database integrates patient records, prescriptions, lab reports, and imaging under one digital health infrastructure. The major strength of this model lies in its ability to enforce uniform standards, facilitate real-time data access, and enable holistic health analytics and planning. However, centralized models also pose significant challenges. They are inherently more vulnerable to single-point security breaches and often raise data sovereignty and privacy concerns, particularly when the control of data is in the hands of a government or large organization without transparent oversight. Additionally, scaling and modifying such systems across diverse regions with differing policies can be bureaucratically intensive.

Federated Interoperability Model

The federated interoperability model is a decentralized approach in which data remains locally managed and stored within the systems of hospitals, clinics, or regional health networks. A federated network allows these independent systems to share data through agreed-upon standards and protocols (e.g., FHIR APIs or XDS-based registries). A prominent example is India’s Ayushman Bharat Digital Mission (ABDM), which uses a federated model where data resides with the data creator and can only be accessed with the patient’s consent. This model offers greater autonomy, privacy, and scalability, particularly in diverse and populous countries. However, it comes with complexities related to data discovery, network latency, inter-organizational coordination, and standard harmonization. Establishing trust frameworks and consent mechanisms across multiple nodes becomes critical for effective operation.

Hybrid Interoperability Model

The hybrid interoperability model blends both centralized and federated elements to strike a balance between standardization and flexibility. In this model, certain data elements such as patient summaries, immunization records, or identifiers might be stored in a central system, while other data such as full medical histories remain distributed and accessible through federated networks. The European Union’s MyHealth@EU is a notable implementation of this model. It enables cross-border health data exchange for services like ePrescriptions and patient summaries, while allowing individual member states to maintain their own health data systems under the regulatory framework of GDPR. The hybrid model provides greater resilience, scalability, and compliance with regional laws, but it also demands complex governance structures, policy harmonization, and significant technical infrastructure to ensure secure, seamless data exchange between central and local systems.

Model	Strengths	Weaknesses
Centralized (e.g., Estonia)	High control, uniform standards	Data privacy concerns; vulnerable to central attacks
Federated (e.g., India’s ABDM)	Local autonomy; scalable; privacy by design	Coordination and discovery challenges
Hybrid (e.g., EU’s MyHealth@EU)	Balanced governance; harmonized standards	Complex legal harmonization

9. Future Outlook (2025–2030)

Expansion of FHIR-Based Ecosystems

Between 2025 and 2030, HL7 FHIR (Fast Healthcare Interoperability Resources) is expected to become the global standard backbone for health data exchange. National healthcare systems and private EHR vendors are likely to fully adopt FHIR not only for data exchange but also for clinical workflows, patient access, and analytics. The move toward FHIR-enabled APIs will allow smoother app integration and real-time data exchange across different EHR platforms. Many governments are expected to mandate FHIR as a compliance requirement, and international alignment on FHIR profiles will be essential for cross-border interoperability. Efforts like the International Patient Summary (IPS) and SMART on FHIR apps will further enhance usability.

Integration of Generative AI and Interoperable Health Systems

Generative AI, combined with interoperable data platforms, will radically reshape clinical documentation, diagnostics, and decision support. By 2030, AI-driven interfaces like ambient listening tools and AI scribes will leverage structured EHR data through APIs to assist clinicians in real-time. These tools will depend on seamless access to up-to-date, standardized health records, making interoperability foundational to AI success. Natural language processing will also evolve to convert unstructured notes into standardized formats (e.g., FHIR resources), thus enhancing machine readability and exchangeability. Ensuring trust, explainability, and regulatory approval for AI use in such environments will be a priority.

Rise of Decentralized Identity and Consent Mechanisms

To tackle privacy, consent, and cross-border legal complexities, the use of decentralized identity (DID) systems and blockchain-based consent management will grow. Patients will increasingly control their data via digital health wallets, granting time-bound, granular access to different providers and systems. Technologies like verifiable credentials and self-sovereign identity (SSI) will empower individuals to manage their health information across jurisdictions. This model will also simplify compliance with region-specific laws like GDPR, HIPAA, or India’s DPDP Act, by giving explicit, traceable control to the data subject. Adoption will depend on policy alignment and advances in secure identity verification.

International Health Data Exchanges and Global Coordination

Driven by pandemic preparedness, international medical tourism, and refugee health needs, governments and organizations like WHO, OECD, and the G20 will support the creation of global health data corridors. These exchanges will facilitate real-time access to critical patient data such as immunization records, medication lists, and chronic disease profiles. Standards like the International Patient Summary (IPS) will be expanded and harmonized with local systems. Multinational treaties and shared governance frameworks will be necessary to handle data residency, sovereignty, liability, and language localization. Pilots in the European Union and ASEAN countries are expected to lead the way in model development.

Shift Toward Event-Driven and Real-Time Interoperability

The next generation of interoperability will go beyond passive data sharing to active, event-driven systems. These architectures will support real-time clinical alerts, referrals, medication updates, and public health notifications. This means systems will push information (e.g., a new diagnosis or medication prescribed) rather than wait for manual querying. Technologies like publish-subscribe messaging models and asynchronous FHIR messaging will power this shift. Healthcare providers will benefit from automated care coordination, reduced errors, and proactive population health interventions. This real-time exchange will be especially crucial in critical care, emergency medicine, and outbreak surveillance.

Growing Role of Cloud and Platform-as-a-Service (PaaS) Models

Cloud infrastructure will become the foundation of global health data interoperability by 2030. Major EHR vendors and health systems will migrate to cloud-native or hybrid cloud platforms that offer scalable, secure, and compliant environments for health information exchange. Platform-as-a-Service (PaaS) offerings will allow developers to build modular health applications that plug into core EHRs via open APIs. This will reduce costs and enhance flexibility for hospitals, especially in low-resource settings. Cloud platforms will also enable cross-border disaster recovery, backup, and AI workload processing, provided that data localization and encryption standards are met.

Enhanced Public-Private Partnerships and Regulatory Alignment

Governments, multilateral agencies, and private health tech firms will need to work in close collaboration to establish interoperable digital health infrastructure. These partnerships will support the development of open-source toolkits, interoperability testbeds, and cross-border certification programs. Regulations will evolve to harmonize data sharing laws while still respecting national security and sovereignty concerns. By 2030, we are likely to see international accreditation bodies for health IT interoperability, similar to ISO or IHE Connectathon certifications. Incentive schemes, like those used in the US Meaningful Use program, may be replicated globally to encourage adoption and conformance.

Generative AI + FHIR: Context-aware AI tools embedded in interoperable systems to assist clinicians.
Decentralized Identity: Self-sovereign IDs using blockchain for health data access.
International Health Data Exchanges: Driven by WHO, G20, and G7 with disaster recovery and pandemic response in mind.
Real-time Interoperability: Event-driven architecture for care continuity (e.g., push-based lab updates, medication alerts).

Conclusion

The Complexity of EHR Interoperability

Ensuring interoperability between Electronic Health Records (EHRs) is one of the most complex challenges in modern healthcare IT. It is not merely a matter of enabling systems to “talk” to each other. Instead, it requires aligning multiple domains—technical standards, clinical semantics, national policies, legal regulations, and healthcare economics. Achieving full interoperability necessitates integrating structured, meaningful data in real-time, across different vendors, geographic borders, and care levels, all while ensuring data privacy and patient safety.

The Shift from Data Exchange to Data Utility

Historically, the focus of interoperability was on simply transmitting patient information between systems. However, the contemporary need goes beyond data exchange—it emphasizes data utility. Clinicians, researchers, and patients must be able to interpret, analyze, and act upon the data seamlessly. This requires not only technical standards like HL7 FHIR but also semantic alignment using ontologies such as SNOMED CT, ICD, and LOINC, along with clear metadata structures, consistent labeling, and actionable formats. Without this, the value of shared data is severely diminished.

The Role of Global Cooperation and Policy Harmonization

Cross-border interoperability is heavily influenced by national laws and policies. Variations in data privacy regulations (such as HIPAA in the U.S. vs. GDPR in the EU) often create barriers to international health data exchange. Therefore, global cooperation is essential. Countries must work towards harmonized digital health frameworks through alliances such as the WHO’s Global Digital Health Strategy, G7 and G20 health task forces, and multi-country interoperability testbeds. Regulatory convergence, combined with technical standardization, can pave the way for seamless global data exchange.

The Importance of Open Standards and Innovation

To scale interoperability affordably and equitably, open standards and open-source tools play a vital role. Standards such as HL7 FHIR, SMART on FHIR, and OpenEHR, when implemented uniformly, can significantly reduce vendor lock-in and improve compatibility across systems. Meanwhile, innovations in blockchain, AI, and cloud-native architectures are enabling new models for decentralized identity, consent management, and real-time data synchronization. Investing in such technologies, along with capacity building and education, is essential for sustained progress.

Putting the Patient at the Center

Ultimately, the purpose of interoperable health systems is to improve patient outcomes and experiences. Patients must have the right to access, control, and share their health data across any healthcare setting or country. Interoperability should enable continuity of care, regardless of geography, provider, or economic status. By designing systems with patients at the core—through personal health records, mobile apps, and consent-driven architectures—healthcare becomes more participatory, inclusive, and responsive.

A Roadmap for the Future

The path to global EHR interoperability will be incremental but achievable. It requires collaborative governance, political will, investment in infrastructure, and above all, a patient-centric approach. Governments, healthcare providers, technology vendors, and civil society must coordinate efforts to create a trusted, interoperable ecosystem. If done correctly, interoperable health IT systems will not only streamline care delivery but also enable population health management, precision medicine, and global public health surveillance.

Ensuring cross-platform and cross-border EHR interoperability is no longer a technical problem alone—it is a multi-dimensional challenge that involves legal, regulatory, economic, and ethical dimensions. The combination of FHIR APIs, open standards, international cooperation, and patient-centric design is essential for creating a truly interoperable global health ecosystem.