Germany
Germany’s healthcare system operates within a nationally standardised reporting structure, ensuring consistent documentation across regions. However, understanding treatment capacity and expertise requires moving beyond administrative reporting. This is particularly limiting for rare conditions, where specialized centres may manage substantial patient volumes but appear to show limited activity in national datasets due to coding constraints. As a result, routine administrative data can underrepresent the true concentration of expertise and care, making it insufficient as a standalone source for identifying where patients are actually treated.
United Kingdom
The UK healthcare system is centrally organised, with specialised services concentrated in a limited number of commissioned centres. Despite national data availability, insight into patient flows and disease-specific expertise is often aggregated and incomplete. Shared-care models further obscure how care is delivered in practice.
If you’re an NHS partner, click here for your NHS solutions
If you’re an NHS partner, click here for your NHS solutions
Ireland
Ireland’s healthcare system is centrally managed through the Health Service Executive, but health data is largely stored within individual hospital and care setting systems. Data exchange between organisations remains limited, with interoperability varying across providers and no fully integrated national infrastructure in place. As a result, information is often siloed at the institutional level, with inconsistent visibility across regions and care settings.
Spain
Spain’s healthcare system is decentralised, with governance carried out by each Autonomous Community. As a result, data structures and care organisation vary by region, leading to inconsistent visibility into patient pathways and specialist activity. While some national datasets exist, hospital-level insight remains uneven, limiting a coherent national view of care delivery.
Italy
Italy’s healthcare system is highly decentralised, with 21 regions operating independently, leading to fragmented data structures and limited interoperability across care centres. The lack of standardization constrains the development of national registries, weakening system-wide oversight. Significant patient movement from southern centres to higher-capacity northern providers further complicates tracking of patient pathways and identification of true centres of expertise nationally. Targeted real-world analysis is therefore essential to accurately map care delivery beyond what official datasets alone can reveal.
France
France has a highly structured healthcare system with formal reference centres and national rare disease networks. While strong national datasets exist, real-world care is distributed beyond designated centres and across multiple administrative sources. This creates a gap between formal labels and how patients are actually treated in practice.
Portugal
Portugal has implemented national initiatives aimed at centralizing and coordinating the management of health information. Nevertheless, the digital health landscape remains structurally fragmented across institutions, driven by inconsistent interoperability standards and the continued reliance on legacy IT systems within hospitals and regional platforms. Consequently, information assets remain operationally siloed, limiting comprehensive data sharing and the seamless exchange of information across the healthcare ecosystem.
Sweden
In Sweden, the collection of public health data is well-stablished with both national-level and disease-specific registries. However, the system remains decentralized, with public health services administered at regional and municipal levels, resulting in data being governed and maintained locally. Within each region, the high share of private healthcare providers adds a further layer of complexity. As a result, there is significant variation in how data is shared and coded, with no mandatory system-wide standards.
Finland
Finland operates a register-based health data system, with extensive national registries capturing detailed clinical information across diseases and care settings. These datasets are collected in a standardised manner and can be linked at the patient level across providers, enabling longitudinal analysis. Data is consolidated within national registries and reporting platforms, supporting consistent system-wide views of care.
Austria
Austria’s healthcare data landscape is largely based on a system of federally mandated health data registers and routine administrative datasets. Data is collected and maintained across multiple organisations rather than within a fully integrated national health information infrastructure. Although relevant health datasets exist, their linkage and availability for secondary use remain limited.
Switzerland
Switzerland’s health data system reflects its highly decentralised federal healthcare structure, with 26 cantons holding authority and diverse private actors driving complexity and fragmentation in data collection and sharing. Structural and legal factors, including a lack of consistent data reporting and varied data-access procedures, limit transparency and use of healthcare data in Switzerland.
Norway
Norway maintains a wide range of national health registries and datasets, capturing detailed clinical and administrative information across diseases and care settings. These data are standardised within individual registries and supported by national reporting systems. However, data is stored across multiple registries and institutions rather than within a single unified infrastructure. As a result, building a comprehensive view of care requires integration across registries to combine information across care domains.
Belgium
Belgium’s healthcare system operates within a federal structure, with responsibilities shared between national and regional authorities. Health data is captured through national insurance, administrative, and disease-specific registry systems, alongside datasets generated by regions and healthcare providers. While federal platforms support data exchange and some cross-source integration, data remains distributed across multiple institutions and governance levels rather than consolidated within a single national infrastructure. As a result, building a complete view of care still requires coordination across federal, regional, and provider-level systems
Netherlands
Healthcare data in the Netherlands is stored across a large number of separate data custodians, provider systems, and sector-specific initiatives. Many of the data needed to support care, research and policy exists, but there is an absence of an integrated national health information system which limits interoperability, consistent data exchange and efficient data linkage. Structural, legal, and governance factors across custodians continue to constrain the effective sharing and reuse of healthcare data in the Netherlands.
Denmark
Denmark’s healthcare system is nationally regulated and regionally delivered, within a unified framework with consistent governance and reporting requirements. Health data is systematically collected across providers and consolidated within a comprehensive set of national registries and central data platforms, covering population health, hospital activity, prescriptions, and clinical outcomes. This infrastructure supports consistent integration of data across care settings, enabling standardised, longitudinal, and system-wide analysis.
Hungary
Hungary’s healthcare system is centrally managed, with national authorities overseeing financing, data collection, and professional registries. Health data is reported through central administrative and insurance systems, capturing activity across providers. However, data is primarily structured for reporting and reimbursement purposes, with limited integration across datasets and care settings. As a result, information remains structured across separate administrative datasets, limiting cross-setting analysis of care delivery.
Slovakia
Slovakia’s healthcare system is centrally regulated, but care is financed through multiple public health insurance funds, each maintaining its own data on healthcare activity. In parallel, national health information systems and registries are managed centrally, creating a dual structure between insurer-based and state-managed data. While both layers capture complementary aspects of care, they are not fully integrated, reflecting a structural split between insurer-based and state-managed data.
Poland
Poland’s healthcare system is centrally regulated, with the National Health Fund acting as the primary payer and a key source of nationwide claims data. In parallel, national eHealth platforms have been introduced to capture digital records such as prescriptions and patient interactions. However, these data streams remain organised across separate systems rather than a single integrated infrastructure. As a result, combining information across clinical, administrative, and digital sources remains complex.
Czech Republic
The Czech Republic’s healthcare system is centrally regulated, with care financed through multiple public health insurance funds that generate detailed claims data. In parallel, national registries and statistical systems are coordinated through a central health information authority, consolidating data across providers and care settings. This creates a structured national data layer alongside insurer-based datasets, but information remains organised across distinct systems rather than a single unified infrastructure.
Brazil
Brazil’s healthcare system operates within a federal structure, with responsibilities shared across national, state, and municipal levels, alongside a large private sector. Public health data is reported through multiple national information systems, while private-sector data sits in separate insurer and provider environments. As a result, health information is distributed across parallel systems rather than stored within a single national infrastructure, limiting consistent exchange and consolidation across public and private sectors.
USA
The United States healthcare system is highly decentralised, with data generated across private insurers, healthcare providers, and public programmes. Health information is captured through separate claims, electronic health record, and registry-based systems, while provider and disease-specific data is often maintained in distinct professional and organisational platforms. Although large federal and specialty datasets exist, they are distributed across payers and providers rather than consolidated within a national infrastructure. This results in parallel data environments across the healthcare system, with no single unified view of care activity.
Japan
Japan’s healthcare data infrastructure is undergoing significant digitalisation. Healthcare data, including both clinical and administrative data, are increasingly generated and stored, improving the accessibility, accuracy, and reliability of available data. However, adoption among smaller providers remains low and uneven, contributing to variation in data completeness across the healthcare system. Interoperability between institutions is also limited, restricting the efficient use and exchange of data. As a result, generating meaningful insights requires the linkage and analysis of data across separate systems.
Taiwan
Taiwan’s healthcare data infrastructure is characterised by a highly centralised, nationally governed system. Healthcare data are collected at national level, covering more than 99% of the population and providing longitudinal, population-level data. This centralised structure enables standardised data collection, data linkage, and extensive use of healthcare data for system management and research. However, while these national datasets are comprehensive, they have limitations in capturing detailed information, meaning that generating more clinically meaningful insights often requires linkage with additional datasets.
South Korea
South Korea has a highly digitised healthcare system with strong health data reporting capabilities. However, hospitals and clinics largely operate separate information systems, limiting seamless data exchange across providers. The patient’s journey can therefore also not be easily followed across institutions. As a result, while health data is reported, system-wide analysis are constrained by organizational silos. Relying solely on statistics will not provide the full picture of where real clinical expertise lies.
Israel
Israel has invested significantly in advancing its national digital health infrastructure, with a strong focus on improving interoperability and data exchange across the healthcare system. This is because the landscape is characterised by heterogeneity in data systems across hospitals, limiting the use of healthcare data. National standards for data reporting have been introduced to facilitate more standard and efficient sharing of healthcare data. However, participation in these initiatives remains limited to a subset of institutions, meaning that the full benefits of improved data integration have not yet been fully realised. Therefore, a complete understanding of healthcare activity still requires the integration and analysis of data across multiple sources.
Turkey
Turkey’s healthcare system is centrally managed, with the Ministry of Health overseeing a large, nationally coordinated hospital network and health reporting infrastructure. Data is captured through administrative, hospital, and statistical systems that cover both public and private providers. While this provides broad national coverage, information is organised across operational platforms aligned to service delivery and reporting functions, rather than a single unified data environment. This structure reflects a system designed for central oversight, with data organised by function rather than fully consolidated across care settings.
Hong Kong
Hong Kong’s healthcare system is characterised by a dual structure, with a highly centralised public sector led by the Hospital Authority alongside a large private provider market. Within the public system, health data is captured through unified clinical systems and national reporting platforms, supported by disease-specific registries and government datasets. This enables consistent, system-wide tracking of care within the public network. However, data from the private sector is maintained separately across providers, resulting in a split data landscape between a consolidated public infrastructure and a more dispersed private care environment.
Australia
Australia’s healthcare system operates within a federal structure, with responsibilities shared between national and state governments. Health data is generated across state-managed hospital systems, primary care providers, and national programmes such as Medicare, alongside disease-specific registries and national provider databases. A national digital health infrastructure, including My Health Record, provides a layer for aggregating patient information across care settings. However, data remains organised across jurisdictional systems and programme-specific datasets, reflecting the division between federal coordination and state-level service delivery.
Germany
Germany’s healthcare system operates within a nationally standardised reporting structure, ensuring consistent documentation across regions. However, understanding treatment capacity and expertise requires moving beyond administrative reporting. This is particularly limiting for rare conditions, where specialized centres may manage substantial patient volumes but appear to show limited activity in national datasets due to coding constraints. As a result, routine administrative data can underrepresent the true concentration of expertise and care, making it insufficient as a standalone source for identifying where patients are actually treated.
United Kingdom
The UK healthcare system is centrally organised, with specialised services concentrated in a limited number of commissioned centres. Despite national data availability, insight into patient flows and disease-specific expertise is often aggregated and incomplete. Shared-care models further obscure how care is delivered in practice.
If you’re an NHS partner, click here for your NHS solutions
If you’re an NHS partner, click here for your NHS solutions
Ireland
Ireland’s healthcare system is centrally managed through the Health Service Executive, but health data is largely stored within individual hospital and care setting systems. Data exchange between organisations remains limited, with interoperability varying across providers and no fully integrated national infrastructure in place. As a result, information is often siloed at the institutional level, with inconsistent visibility across regions and care settings.
Spain
Spain’s healthcare system is decentralised, with governance carried out by each Autonomous Community. As a result, data structures and care organisation vary by region, leading to inconsistent visibility into patient pathways and specialist activity. While some national datasets exist, hospital-level insight remains uneven, limiting a coherent national view of care delivery.
Italy
Italy’s healthcare system is highly decentralised, with 21 regions operating independently, leading to fragmented data structures and limited interoperability across care centres. The lack of standardization constrains the development of national registries, weakening system-wide oversight. Significant patient movement from southern centres to higher-capacity northern providers further complicates tracking of patient pathways and identification of true centres of expertise nationally. Targeted real-world analysis is therefore essential to accurately map care delivery beyond what official datasets alone can reveal.
France
France has a highly structured healthcare system with formal reference centres and national rare disease networks. While strong national datasets exist, real-world care is distributed beyond designated centres and across multiple administrative sources. This creates a gap between formal labels and how patients are actually treated in practice.
Portugal
Portugal has implemented national initiatives aimed at centralizing and coordinating the management of health information. Nevertheless, the digital health landscape remains structurally fragmented across institutions, driven by inconsistent interoperability standards and the continued reliance on legacy IT systems within hospitals and regional platforms. Consequently, information assets remain operationally siloed, limiting comprehensive data sharing and the seamless exchange of information across the healthcare ecosystem.
Sweden
In Sweden, the collection of public health data is well-stablished with both national-level and disease-specific registries. However, the system remains decentralized, with public health services administered at regional and municipal levels, resulting in data being governed and maintained locally. Within each region, the high share of private healthcare providers adds a further layer of complexity. As a result, there is significant variation in how data is shared and coded, with no mandatory system-wide standards.
Finland
Finland operates a register-based health data system, with extensive national registries capturing detailed clinical information across diseases and care settings. These datasets are collected in a standardised manner and can be linked at the patient level across providers, enabling longitudinal analysis. Data is consolidated within national registries and reporting platforms, supporting consistent system-wide views of care.
Austria
Austria’s healthcare data landscape is largely based on a system of federally mandated health data registers and routine administrative datasets. Data is collected and maintained across multiple organisations rather than within a fully integrated national health information infrastructure. Although relevant health datasets exist, their linkage and availability for secondary use remain limited.
Switzerland
Switzerland’s health data system reflects its highly decentralised federal healthcare structure, with 26 cantons holding authority and diverse private actors driving complexity and fragmentation in data collection and sharing. Structural and legal factors, including a lack of consistent data reporting and varied data-access procedures, limit transparency and use of healthcare data in Switzerland.
Norway
Norway maintains a wide range of national health registries and datasets, capturing detailed clinical and administrative information across diseases and care settings. These data are standardised within individual registries and supported by national reporting systems. However, data is stored across multiple registries and institutions rather than within a single unified infrastructure. As a result, building a comprehensive view of care requires integration across registries to combine information across care domains.
Belgium
Belgium’s healthcare system operates within a federal structure, with responsibilities shared between national and regional authorities. Health data is captured through national insurance, administrative, and disease-specific registry systems, alongside datasets generated by regions and healthcare providers. While federal platforms support data exchange and some cross-source integration, data remains distributed across multiple institutions and governance levels rather than consolidated within a single national infrastructure. As a result, building a complete view of care still requires coordination across federal, regional, and provider-level systems
Netherlands
Healthcare data in the Netherlands is stored across a large number of separate data custodians, provider systems, and sector-specific initiatives. Many of the data needed to support care, research and policy exists, but there is an absence of an integrated national health information system which limits interoperability, consistent data exchange and efficient data linkage. Structural, legal, and governance factors across custodians continue to constrain the effective sharing and reuse of healthcare data in the Netherlands.
Denmark
Denmark’s healthcare system is nationally regulated and regionally delivered, within a unified framework with consistent governance and reporting requirements. Health data is systematically collected across providers and consolidated within a comprehensive set of national registries and central data platforms, covering population health, hospital activity, prescriptions, and clinical outcomes. This infrastructure supports consistent integration of data across care settings, enabling standardised, longitudinal, and system-wide analysis.
Hungary
Hungary’s healthcare system is centrally managed, with national authorities overseeing financing, data collection, and professional registries. Health data is reported through central administrative and insurance systems, capturing activity across providers. However, data is primarily structured for reporting and reimbursement purposes, with limited integration across datasets and care settings. As a result, information remains structured across separate administrative datasets, limiting cross-setting analysis of care delivery.
Slovakia
Slovakia’s healthcare system is centrally regulated, but care is financed through multiple public health insurance funds, each maintaining its own data on healthcare activity. In parallel, national health information systems and registries are managed centrally, creating a dual structure between insurer-based and state-managed data. While both layers capture complementary aspects of care, they are not fully integrated, reflecting a structural split between insurer-based and state-managed data.
Poland
Poland’s healthcare system is centrally regulated, with the National Health Fund acting as the primary payer and a key source of nationwide claims data. In parallel, national eHealth platforms have been introduced to capture digital records such as prescriptions and patient interactions. However, these data streams remain organised across separate systems rather than a single integrated infrastructure. As a result, combining information across clinical, administrative, and digital sources remains complex.
Czech Republic
The Czech Republic’s healthcare system is centrally regulated, with care financed through multiple public health insurance funds that generate detailed claims data. In parallel, national registries and statistical systems are coordinated through a central health information authority, consolidating data across providers and care settings. This creates a structured national data layer alongside insurer-based datasets, but information remains organised across distinct systems rather than a single unified infrastructure.
Brazil
Brazil’s healthcare system operates within a federal structure, with responsibilities shared across national, state, and municipal levels, alongside a large private sector. Public health data is reported through multiple national information systems, while private-sector data sits in separate insurer and provider environments. As a result, health information is distributed across parallel systems rather than stored within a single national infrastructure, limiting consistent exchange and consolidation across public and private sectors.
USA
The United States healthcare system is highly decentralised, with data generated across private insurers, healthcare providers, and public programmes. Health information is captured through separate claims, electronic health record, and registry-based systems, while provider and disease-specific data is often maintained in distinct professional and organisational platforms. Although large federal and specialty datasets exist, they are distributed across payers and providers rather than consolidated within a national infrastructure. This results in parallel data environments across the healthcare system, with no single unified view of care activity.
Japan
Japan’s healthcare data infrastructure is undergoing significant digitalisation. Healthcare data, including both clinical and administrative data, are increasingly generated and stored, improving the accessibility, accuracy, and reliability of available data. However, adoption among smaller providers remains low and uneven, contributing to variation in data completeness across the healthcare system. Interoperability between institutions is also limited, restricting the efficient use and exchange of data. As a result, generating meaningful insights requires the linkage and analysis of data across separate systems.
Taiwan
Taiwan’s healthcare data infrastructure is characterised by a highly centralised, nationally governed system. Healthcare data are collected at national level, covering more than 99% of the population and providing longitudinal, population-level data. This centralised structure enables standardised data collection, data linkage, and extensive use of healthcare data for system management and research. However, while these national datasets are comprehensive, they have limitations in capturing detailed information, meaning that generating more clinically meaningful insights often requires linkage with additional datasets.
South Korea
South Korea has a highly digitised healthcare system with strong health data reporting capabilities. However, hospitals and clinics largely operate separate information systems, limiting seamless data exchange across providers. The patient’s journey can therefore also not be easily followed across institutions. As a result, while health data is reported, system-wide analysis are constrained by organizational silos. Relying solely on statistics will not provide the full picture of where real clinical expertise lies.
Israel
Israel has invested significantly in advancing its national digital health infrastructure, with a strong focus on improving interoperability and data exchange across the healthcare system. This is because the landscape is characterised by heterogeneity in data systems across hospitals, limiting the use of healthcare data. National standards for data reporting have been introduced to facilitate more standard and efficient sharing of healthcare data. However, participation in these initiatives remains limited to a subset of institutions, meaning that the full benefits of improved data integration have not yet been fully realised. Therefore, a complete understanding of healthcare activity still requires the integration and analysis of data across multiple sources.
Turkey
Turkey’s healthcare system is centrally managed, with the Ministry of Health overseeing a large, nationally coordinated hospital network and health reporting infrastructure. Data is captured through administrative, hospital, and statistical systems that cover both public and private providers. While this provides broad national coverage, information is organised across operational platforms aligned to service delivery and reporting functions, rather than a single unified data environment. This structure reflects a system designed for central oversight, with data organised by function rather than fully consolidated across care settings.
Hong Kong
Hong Kong’s healthcare system is characterised by a dual structure, with a highly centralised public sector led by the Hospital Authority alongside a large private provider market. Within the public system, health data is captured through unified clinical systems and national reporting platforms, supported by disease-specific registries and government datasets. This enables consistent, system-wide tracking of care within the public network. However, data from the private sector is maintained separately across providers, resulting in a split data landscape between a consolidated public infrastructure and a more dispersed private care environment.
Australia
Australia’s healthcare system operates within a federal structure, with responsibilities shared between national and state governments. Health data is generated across state-managed hospital systems, primary care providers, and national programmes such as Medicare, alongside disease-specific registries and national provider databases. A national digital health infrastructure, including My Health Record, provides a layer for aggregating patient information across care settings. However, data remains organised across jurisdictional systems and programme-specific datasets, reflecting the division between federal coordination and state-level service delivery.
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