2. The structure and governance of the Dutch health information system in comparison with OECD countries

This chapter first outlines the key features of the Dutch health system in terms of its structure and organisation, and how these influence the generation, management and use of data. The scope includes the four laws that govern four domains of the Dutch health system: 1. public health, 2. social care, 3. curative care, and 4. long-term care. These laws lay the foundation for not only the overall structure of the health system but also how data flow between the various stakeholders and organisations within it. The result is a fragmented and heterogeneous health information landscape.

The second part of the chapter describes what is meant by a health data infrastructure and an integrated health information system, its key components, and how it can help countries advance policy objectives. Progress across OECD countries in the development of health data governance frameworks and in the development and governance of interoperable electronic health record systems are presented to inform the review of the current situation in the Netherlands.

The Dutch health system (defined here as the overall approach to promote individual and population health through social, preventative and curative means) is a combination of managed competition where individuals, health care purchasers and providers determine price, quality and service based on supply and demand within policy and regulatory parameters set by the government (Van Driesden G, 2021[1]). The system is perhaps best viewed in terms of the laws that govern public health, social care, curative care and long-term care:

  1. 1. Public Health Act:

    1. a) Regulates public health interventions such as population-level screening and control of infectious disease

    2. b) Stipulates the remit of local governments in promoting public health and well-being.

  2. 2. Social Support Act:

    1. a) Stipulates that local governments are responsible for social support, informal care, and volunteer work

    2. b) Governs the provision of domestic help, day centres, support, and short-term stays at health facilities

    3. c) Requires sheltered accommodation for people with psychosocial problems.

  3. 3. Health insurance Act:

    1. a) Provides for basic entitlements to health care through the funding of basic health insurance

    2. b) Requires that individuals purchase basic health insurance

    3. c) Stipulates that health care providers may not exclude anyone from basic health insurance.

  4. 4. Long-term Care Act:

    1. a) Regulates health care for people who require 24-hour care and permanent supervision

    2. b) Provides that people who have received a special-needs assessment are entitled to care either at home or in a designated facility

    3. c) Requires that health care administrative offices procure sufficient care or provide personal budgets.

This arrangement creates the basic architecture for how Dutch health and social care data are collected, stored and managed (Figure 2.1).1

In addition, the Dutch system works largely on a mixture of competition and market mechanisms, and it relies heavily on the private (not-for profit) sector. It has limited government involvement on a national level (health care) and substantive involvement on municipal level (social care). While it has performed very well in comparison to other OECD countries. It is highly fragmented across health settings and sectors – exemplified by the separate approaches toward managing and using public health data, health care data and social care data.

Fragmentation in health systems is certainly not unique. However, in contrast to other most countries where it is a result of either legacy factors or unintended policy consequences, it is a design feature in the Netherlands to ensure market mechanisms can function as intended. For example, the competition law explicitly prohibits exchange of information between providers in order to maintain the market mechanisms. However, an exchange of data can lead to actions that benefit public health, a role allocated to the government via the constitution law. This illustrates the need for some legal reform on data exchange for the benefit of public health.

Fragmentation and the consequent high number of data custodians – does not ipso facto impede nation-wide co-operation, co-ordination, and data standardisation, but it makes indispensable co-ordinated, national policies, legislations, incentives, and governance mechanisms to support and encourage actors toward the common goal of optimising the use of existing data.

Fragmentation characterises not only Dutch health system provision but also its regulation and governance. A high number of institutional actors and organisations have a stake in governance and regulation, data creation and processing, and data interoperability and exchange.

The key regulatory institutions, the Nederlandse Zorgauthoriteit (NZa), the Dutch Health Institute, the Inspection of health care and youth, and the Authority for Consumers and Markets, all have part of the mandate in data governance and part access to the data. Municipalities, health insurers and zorgkantoren have siloed mandates for financing of health and social care.

The central government, meanwhile, is advised by different (independent) committees like the Gezondheidsraad, Sociaal en Cultureel Planbureau, Wetenschappenlijke Raad voor het regeringsbeleid, Raad voor Volksgezondheid en samenleving (curative care, long-term care, social care, and public health), Zorginstituut Nederland (curative care, long-term care), Rijksinstituut voor Volksgezondheid en milieu (RIVM), and the GGD (public health). The Informatieberaad Zorg (IB) is the (informal)advisory body in which parties come together to work towards safe exchange of information, however their focus is on curative care and primary uses of information.

The Dutch system also relies on input from confederations and representatives’ organisations like the Verbond van Nederlandse Ondernemingen en het Nederlands Christelijke Werkgeversverbond (VNO-NCW), the Federatie Medisch Specialisten, Beroepsvereninging Verzorgenden Verpleegkundigen (V&VN), Jeugdzorg Nederlan (JN), Nederlandse Vereniging van Ziekenhuizen (NVZ), the Nederlandse Federatie van Universitaire Medische Centra (NFU), and the Patiënten-federatie Nederland.

Together, health and social care providers generate an immense amount of data but these data are most commonly kept within the organisation/sector. Some providers have realised the potential of integrating data with other providers and multiple initiatives to exchange data have started for example between collaborating hospital groups (Santeon group), regional health and social care provider alliances (Beter Samen in Noord), and municipalities and health insurers (gemeentezorgspiegel). However, not all providers have the capacity to do so, some are not able to access the data they generate for secondary purposes, due to a lack of human capital (skills) or financial resources for EHR data processing and analytics tools.

There is sharing of de-identified personal health data for secondary purposes, for example GP’s sharing data with an academic network for primary care, is done within sector specific research organisations such as Nivel (health care), Vilans (long term care and social care), and Trimbos (mental health and addiction).

The Centraal Bureau voor de Statistiek (CBS) has a lot of experience in data processing, linkage and analytics. However, its mandate is limited in the health arena. The) in co-operation with CBS and the Ministry of internal affairs are trying to standardise data collection and use on a national level for all municipalities working with a diverse range of data from living conditions, education, economy, public health and social care. Noting that most individual municipalities, as most individual health and social care providers, do not have the capacity for data processing and analytics for secondary purposes.

Dutch claims data are well standardised and have clear custodianship. The Nederlandse Zorgauthoriteit (NZa) collects hospital activity data (DRG), Vektis collects reimbursement data, and the Zorginstituut Nederland (ZiN) collects data to enable risk equalisation among the many insurers in the Dutch health care market and public reporting on providers as part of the existing accountability mechanism.

Data on the quality of specialised care is most often managed through Quality Registries by professional networks and collected via private data custodians in specialised registries (e.g. DICA, DHD, Dutch cancer registry). Data generated by individual providers and health care professionals are less standardised as individual providers and provider organisations have different preferred tools (including indicators), EMR vendors (including some organisations with different content within the ‘same’ EMR system) and priorities in data standardisation. The TWIIN initiative co-ordinated by the Vereniging van Zorgaanbieders voor Zorgcommunicatie (VZVZ) and RSO Nederland has the overarching goal to lay the foundation of rules and infrastructure for these disparate entities to exchange data. The ambition is to create a data infrastructure with nationally co-ordinated authorisation and privacy design through: 1) exchange of medical images between health care providers, 2) exchanging laboratory results with pharmacies, and 3) exchange of data in perinatal health care. This initiative is not structurally funded but received start- up funding from Zorgverzekeraars Nederland (ZN), an umbrella organisation for Dutch health insurers.

The Nederlands Normalisatie-Instituut (NEN) is a non-for-profit private company and the Dutch collaborating partner with the European EN-norms and the international ISO-norms. Ministerie van Volksgezondheid, Welzijn en Sport has asked NEN to develop standards and certification schemes for electronic data exchange in health care together with the health care sector.

Nictiz is one of the important organisations developing standards for health data exchange in the Netherlands. Together with input from other parties that develop standards, like the Zorginsituut Nederlands, they have built up a library of standards on all five levels of interoperability 1) organisational, 2) process, 3) information, 4) application, and 5) IT-infrastructure.

There are initiatives to facilitate data exchange in health care. Medmij is a standard for the exchange of health care data between the care professional and the patient. Vendors of personal health environments can acquire the MedMij label to showcase safe and trustworthy data exchange practices. This initiative from the Informatieberaad Zorg and the Patiëntenfederatie Nederland is voluntary for vendors of personal health environments.

The LSP, co-ordinated by the VZVZ, is a platform in which patients/clients can authorise certain health care providers to share their data when needed. This platform started to facilitate access for health providers to patients’ medication in emergency situations. It is an opt-in system and therefore does not cover the whole population. A proposal was recently heard in the senate that an opt-out system for health care data exchange would still maintain the right to choose and would be more fitting for the needs of patients.

Twenty-first Century health systems will be built around information: the right information reaching the right person at the right time. This enables providing high-quality integrated care to all people in need, as well as better public health practice, health system management, and research and innovation. While health systems will continue to be structured, funded and organised differently, success – in terms of better care, public health, system management and research – will be characterised by a comprehensive, coherent, standardised and integrated approach to managing (electronic) health data.

Any endeavour whose goal is social and economic advancement relies on infrastructure. Putting data to work successfully is no exception. Data infrastructure comprises data assets supported by people, processes and technology (Open Data Institute, n.d.[2]). It includes the bodies or institutions that create, maintain and manage the data as well as the institutions, policies and rules that guide their use. A data infrastructure can be seen as an ecosystem of technology, processes and actors/organisations needed for the collection, storage, maintenance, distribution and (re)use of data by the different end users. As an analogy, a rail infrastructure includes not only the tracks and trains but also the resources, people and equipment to maintain them, regulations and traffic control rules, as well as ticketing and other passenger services. A strong data infrastructure enhances the efficiency and productivity of using data.

It is necessary to distinguish between data and information. Data are raw figures and facts and, in and of themselves, may not be very valuable. Information, on the other hand, is meaning and insights that are obtained from the analysis of data. Thus, this report focusses on obtaining value from health data within the Netherlands by developing a system that yields information. A data infrastructure is the foundation. A health information system not only collects, manages, compiles standardises and exchanges data it also derives meaning and information from health data through analysis and review. It is a system because the focus is on data exchange and integration of information across different stakeholders. This requires supportive laws, policies, governance, hardware and software, expertise and analytical models as well as public communication channels, strategic planning, implementation guidelines, and audit and evaluation mechanisms.

An integrated health information system means that electronic data are FAIR (findable, accessible, interoperable, reusable), and can be exchanged and securely used by other actors and institutions that serve the public interest. The result is that data can flow, safely and securely, to where information can be extracted from them to create knowledge that advances human health and well-being.

An integrated health information system can help not only directly improve care quality, outcomes and patient empowerment by enabling patients and their health care providers to access important information, it would also raise the country’s capacity to use these data for other important purposes including:

  • Managing health system performance on national, regional and network level

  • Public health monitoring and surveillance

  • Opening new communications channels with patients to improve patient-centred care such as the active use of patient-reported metrics (PROMs and PREMs)

  • Introduction of new digital services such as e-prescriptions or telehealth

  • Better targeting of reimbursement for services to reward value

  • Biomedical research and development

  • Innovation such as big data analytics and artificial intelligence that will enhance knowledge-based decisions for patient care and health system governance.

Every data point should serve many uses, from informing a physician caring for a patient to helping patients manage their care, to health care quality monitoring indicators, value-based payments, real-world evaluation of the effectiveness of therapies and contributing to clinical decision support tools (artificial intelligence). Recent advances include that individuals’ data are now used to inform decisions about their care and the care of others. The distinction between using data for primary purposes (direct patient care) and secondary purposes (e.g. research, public health monitoring) is therefore increasingly blurred.

For this reason, health data today cannot be easily categorised as personal or non-personal when the data pertain to individuals. A simple data processing step, such as removing personal identifying information like names, addresses, health insurance numbers and birth dates from a data set, does not yield anonymous data because it is increasingly easy to re-match the data to other datasets and re-identify individuals with some probability of success. More complex manipulations or aggregations of data to try to guarantee anonymity may destroy the quality, validity and usefulness of the data to produce valid information and research results.

Even the simple data processing step of removing personal identifying information must be carefully considered, as the linkage of datasets may require this information, for example to link hospital inpatients to mortality data to find out how many patients died in the weeks following a procedure. Mechanisms that allow re-identification for approved data uses, such as investing in pseudonymisation and secure storage of re-identification keys, are recommended by the OECD (see Annex B).

The key elements of an integrated system that enables primary and secondary uses of data are: approaching health data as a public good; implementing standardised data terminologies and formats (a single ‘language’); a common data model and standardised analytics; and comprehensive data governance that uses a ‘privacy-by-design’ approach. These are outlined next, followed by a section on the interoperability of electronic medical records.

Countries making strides in putting their data to work have recognised that data are a valuable resource that should be used to generate public benefits. Significant public investment in health and health care are a key reason why health data are a public good – this includes public investment in health care provision, in health data development and in funding health research.

But there is also an economic argument for seeing data as a public good in the modern era of Big Data, high performance computing and modern analytical techniques including machine learning and artificial intelligence. Data represent immense value both because of the information they potentially contain and because they can be used and re-used ad infinitum. Their use by one actor does not preclude their use by others. More importantly, like other public goods such laws or language, data are instrumental in building social value through knowledge and information. Their exclusivity is not intrinsic, but is imposed by man-made laws, conventions, and institutions. In net terms, their commodification hampers human development.

Moreover, the social and economic value of data increase exponentially with their size. For example, a researcher looking for biomarkers that will uncover a precision therapy will find a single dataset comprising 10 million records is much more valuable than 100 separate datasets of 100 000 patients that cannot be linked or analysed as a whole (such as via the personal data train). In the private sector, forward-looking firms have realised that even a small slice of analytics on a huge data pool can generate far greater returns than hoarding much smaller puddles of data for proprietary use.

But to fulfil their potential in secondary uses as well as the primary objectives of improving patients’ care, experience and outcomes, data held in various places by different custodians must be coded in formats and languages that enable them to be exchanged and linked.

The main reason why health data are not put to work is a lack interoperability. This happens when the information systems of data holders have been developed without the use of common standards which prevent data from being exchanged or when data are exchanged, make it very difficult for the data to be interpreted or integrated with other data. Without the ability to share and interpret data easily, every data exchange becomes a costly and time-consuming data integration project.

Data standards in health and health care include the methods, protocols, terminologies, and specifications for the collection, exchange, storage, and retrieval of health data from many different sources such as electronic medical records, insurance claims, laboratory test results, prescription medicine dispensing records, vaccination and public health records, population surveys and more (see Box 2.1).

Therefore, the most efficient solution to maximise the value of data held in silos is to agree on and adopt common standards for data terminology and exchange. Increasingly, such standards are becoming global, enabling multi-country collaboration in the development of IT systems and tools, cross-border access to clinical information for travellers who fall ill, as well as in undertaking multi-country medical and health research.

An intermediary solution exists to improve health data interoperability – mapping data from multiple organisations that use different data standards to a Common Data Model (CDM). A CDM organises data into a standard structure that makes it possible for data and the meaning of data to be shared for analytical applications, allowing for efficient data pooling and data integration for health statistics and research. The CDM is not, however, a practical solution for all situations where interoperability is needed such as the exchange of data among health care providers for direct patient care or the development of a patient portal.

It should be stressed that an integrated health information system does not require all data to be stored in a single location. It is quite possible to achieve the key objectives outlined earlier in this report without central storage or even aggregation. A unified and co-ordinated approach to national data governance can enable smooth information exchange and use for a range of purposes without compromising privacy, security and ownership of data. In fact, in some ways data protection can be enhanced under a federated data structure.

Further, ensuring that data can be exchanged across national borders into Europe and beyond can amplify the benefits of data analytics and research in, for example, the context of public health, rare diseases, pharmacovigilance, and precision medicine (see next section). An information system that follows international data standards facilitates within-country and cross-border health care delivery and business opportunities for the Netherland’s research and technology sectors; and is better prepared to participate in and adapt to European regulations and initiatives.

The considerable potential to advance health and welfare as well as providing commercial opportunities for European companies are the motivation to create an EU Health Data Space as part of the EU Digital Health Strategy (EC, 2021[5]). A new regulation is proposed to support Data Spaces in key economic sectors to create a single market for data, where data from public bodies, businesses and citizens can be used safely and fairly for the common good. An EU Health Data Space is proposed to “promote better exchange and access to different types of health data (electronic health records, genomics data, data from patient registries etc.), not only to support health care delivery (so-called primary use of data) but also for health research and health policy making purposes (so-called secondary use of data)” (EC, 2021a[6]).

Three pillars to support the Health Data Space are proposed:

  1. 1. Developing a health data governance framework for EU member states that provides guidance toward secure and privacy protective primary and secondary uses of health data that foster the accessibility and sharing of data. Such guidance would support greater harmonisation of the implementation of EU GDPR requirements in practice.

  2. 2. Data quality and interoperability including technical and semantic (terminology) interoperability between the different infrastructures and IT systems and ensuring health data in Europe are FAIR (Findable, Accessibly, Interoperable and Re-Usable).

  3. 3. Technical infrastructure that builds upon and scales up EU infrastructure, including the eHealth Digital Service Infrastructure, the European Reference Networks and the Genomics Project.

The technical and semantic interoperability standards for the Health Data Space are expected to include international standards for data exchange and terminology and favour exchange standards that support protection of health data privacy and security. For example, a 2021 policy report of the Standing Committee of European Doctors which represents medical associations across Europe, calls for the Health Data Spaces to adopt the HL7 FHIR standard for data exchange and the SNOMED CT clinical terminology standard (CPME, 2021[7]).

In alignment with the EU Health Data Space, the European Medicines Agency (EMA) is developing the DARWIN (Data Analysis and Real-World Interrogation Network) (EMA, 2021[8]). DARWIN will be a co-ordination centre to provide timely and reliable evidence on the use, safety and effectiveness of medicines for human use, including vaccines, from real world health care databases across the European Union (EU). The 2021 call for tender for DARWIN requires all bidders to implement a common data model (CDM).

New national bodies in France and Finland have characteristics and functions that are similar to the health data spaces envisaged by the EU. France introduced the Health Data Hub in 2019 and Finland launched FinData in 2020 to provide a unique entry point for secure and privacy-protective data linkage services and access to health microdata that are EU GDPR compliant (see next section for descriptions of FinData and the Health Data Hub).

A key component of a well-functioning health information system is data governance that avoids the over-use of consent to authorise data exchange, in favour of legal authorisation and requirements for an approach that protects privacy, ensures data security while enabling data to be exchanged and used for legitimate purposes. The OECD Council Recommendation on Health Data Governance sets out the elements for a national health data governance framework and fosters a ‘privacy-by-design’ approach that is consistent with emerging transnational requirements such as those set out in the EU General Data Protection Regulation (GDPR) (See Annex B).

Privacy-by-design involves designing IT systems in a way that pro-actively anticipates and addresses risks to data privacy and security so they may be mitigated. In such approaches, the privacy of all individuals whose data is within the system is protected by default. The protection of individuals’ privacy and data security is embedded within the architecture and functionality of the IT system. At the same time, the IT system supports all uses and re-uses of data that are in the public interest (Cavoukian, 2006[9]).

Privacy-by-design is important because health data are often personal and sensitive, particularly health micro-data where there is a data record for each individual. The EU Data Protection Regulation (GDPR) [Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016] places personal health data in a special category with the highest standards of protection.

The OECD Recommendation on Health Data Governance responds to the growing need for a consensus about the framework conditions within which health data can be appropriately governed to enable health data processing to take place both domestically and transnationally. Such health data governance frameworks require a whole of government approach; given that the public interests served span the domains of health, justice, industry, science, innovation and finance. The OECD Council Recommendation on Health Data Governance is compliant with the EU GDPR and encourages ‘privacy-by-design’.

The OECD Recommendation on Health Data Governance was adopted by the OECD Council on 13 December 2016 and was welcomed by OECD Health Ministers at their meeting in Paris on 17 January 2017. The Recommendation provides policy guidance to:

  • Encourage the availability and use of personal health information, to the extent that this enables significant improvements in health, health care quality and performance and, thereby, the development of healthy societies while, at the same time, continuing to promote and protect the fundamental values of privacy and individual liberties;

  • Promote the use of personal health data for public policy objectives, while maintaining public trust and confidence that any risks to privacy and security are minimised and appropriately managed; and

  • Support greater harmonisation among the health data governance frameworks of Adherents so that more countries can benefit from statistical and research uses of data in which there is a public interest, and so that more countries can participate in multi-country statistical and research projects, while protecting privacy and data security.

Governments adhering to the Recommendation will establish and implement a national health data governance framework to encourage the availability and use of personal health data to serve health-related public interest purposes while promoting the protection of privacy, personal health data and data security.

The Recommendation sets out 12 key elements of the development and implementation of national health data governance frameworks. The elements encourage greater cross-country harmonisation of data governance frameworks so that more countries can use health data for research, statistics and health care quality improvement.

The 2019/20 Survey of Health Data and Governance measured implementation of national health data governance frameworks and related regulations and policies. The 23 respondents to the 2019/20 survey were officials of national health ministries or national health data authorities.

A national health data governance framework can encourage the availability and use of personal health data to serve health-related public interest purposes while promoting the protection of privacy, personal health data and data security. Overall, 17 of 23 respondents reported that a national health data governance framework is established or is being established (Table 2.1).

Most respondents reported health data falling under a national health data privacy legislation; other data used in health studies falling under a national privacy legislation; and certain health datasets or health data programmes falling under other legislations governing ministries, data collections or registries. Some countries have legislations at different levels of government. Overall, 21 of 23 respondents reported that a national law or regulation exists that speaks to the protection of health information privacy and/or to the protection and use of electronic clinical records.

European Union (EU) member states implement the European Union (EU) Data Protection Regulation (GDPR) [Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016]. The GDPR places personal health data in a special category with the highest standards of protection. Compliance requires that personal health data are very well organised and portable. For example, organisations must have data systems that allow them to fulfil individuals’ rights to access their own personal data, to rectify or restrict their processing and to request data portability from one organisation to another; as well as to assure data are correctly categorised and demonstrate compliance with the regulation. In addition to national privacy laws compliant with the GDPR, most EU member states reported other national legislations with provisions specific to the protection of health data such as laws regarding patient rights, the collection and management of health information, the provision of medical care and health care organisations, electronic clinical record systems and health research.

Six respondents reported that their health data governance framework is set out in law (Austria, the Czech Republic, Denmark, Finland, France, Germany). In Austria, there are elements of data governance within legislation governing health telematics, documentation and research organisation. In the Czech Republic, the National Health Information System and its governance are defined in the Act on Health Services. Finland’s health data governance framework is set out in legislation regarding digitisation and management of client and patient information as well as in regulations and guidelines of the health ministry (THL) (Box 2.2). Health data governance requirements, including GDPR requirements, are set out in federal and state laws in Germany.

In France, principles of data governance are set out in an Act on the Modernisation of the Health Care System which unified the governance of administrative health data in the custody of three organisations and enabled dataset linkages and set out principles and procedures for data access. The 2019 Act on the Organisation and Transformation of the Health System broadened the definition of the national health data system to include additional datasets and their custodians and set out data sharing principles among these custodians. A Health Data Hub is defining the elements of shared data governance with stakeholders. The Health Data Hub (HDH) was launched in 2019 to support France in becoming a leader in Artificial Intelligence in health and to overcome barriers to the re-use of health data for research (Box 2.3).

In the Netherlands, the Informatieberaad Zorg works on the development and sustainability of national health information and includes health care organisations and the Ministry of Health. The Council has four information system development goals: data to monitor the safety of prescription medicines; citizen access to their own medical data and the ability to link their own health and medical data; digitisation and exchange of data between health care professionals; and that data is recorded once and reused. A sub-group of the Council is the Community of Data Experts which advises the Council about the secondary use of health data for statistics, research and health and health care policy. Several laws include rules that make it mandatory to keep a medical record, to provide patients with digital access to their medical records and regarding system quality. A new framework law that passed the parliament in 2021 requires the electronic exchange of medical records among health care providers.

In Korea, the Ministry of Health established a health data governance framework in 2018 and set up a Healthcare Big Data Policy Deliberation Committee which is responsible for data development, use and dataset linkages. The COVID-19 pandemic has inspired an expansion of health data under a “Digital New Deal” which allows for the expansion and linkage of national health insurance data with other relevant data and for the accessibility of data for global research (Box 2.4). Latvia developed a Health System Performance Assessment Framework in 2019 (including health care quality, patient safety and efficiency indicators). Within this framework, principles and procedures for data provision, data linkage, health data protection, and access to data for research are set out.

The United States Department of Health and Human Services proposed in 2020 a new rule within the 21st Century Cures Act to support seamless and secure access, exchange and use of electronic health records (Box 2.5). The rule aims to increase innovation and competition by giving patients and their health care providers secure access to health information; allowing more choice in care and treatment. A provision in the rule requires that patients can electronically access all their electronic health information (both structured and unstructured data) at no cost and deters blocking authorised access to and exchange of data. It calls on the health care industry to adopt standardised application programming interfaces (APIs) to allow individuals to securely and easily access structured electronic clinical data using smartphone applications.

The Department of Health and Human Services and the Office of the National Co-ordinator have also released a Trusted Exchange and Common Agreement (TEFCA) which sets out principles, terms and conditions for a common agreement to enable nationwide exchange of electronic health information across disparate health information networks. It aims to ensure that health information networks, health care providers, health plans, individuals and other stakeholders can have secure access to their electronic health information when and where it is needed.

In Australia, governmental responsibility for national health datasets is shared between Federal and State/Territorial jurisdictions. At each level of government, there are a range of agencies with responsibility for specific datasets and there is no overarching health data governance framework. However, all jurisdictions have signed the 2020-25 National Health Reform Agreement which includes an action to scale up a national approach to data governance arrangements, structures and processes, to facilitate clear and efficient mechanisms for sharing and developing data in a sustainable, purpose-based and safe way. There is an Australian data governance framework for electronic clinical data exchanged as part of the My Health Record System. A Data Availability and Transparency Bill was introduced in 2020 to implement a scheme to authorise and regulate access to Australian Government data (Box 2.6).

Ireland’s Department of Health is currently working on a national health information strategy. In this strategy, Ireland is planning a National Health Observatory which would be authorised by law and include the development of a national health data governance framework.

In Israel, responsibilities for national health data governance are shared between the Ministry of Health and the Israel Innovation Authority. Israel’s government has been working on designing a policy framework for secondary use of health data for research to enable collaborative data research initiatives. This framework is not yet finalised. As a result of the COVID-19 pandemic, the government has been accelerating work toward data sharing and access (Box 2.7).

The Government of Canada, together with provinces and territories, is leading the development of a Pan-Canadian Health Data Strategy to improve Canada’s collection, sharing and use of health data while protecting privacy. An Expert Advisory Group (EAG) was established in December 2020 to provide advice and guidance as work on the Pan-Canadian Health Data Strategy evolves.

Slovenia began developing a national health data governance framework in 2019. Luxembourg is planning a National Health Observatory which will be authorised by law and will support the development of a national health data governance framework. Belgium reported an intention to increase co-operation among several federal health administrations (Federal Public Service Health (FPS Health), RIZIV-INAMI, FAGG) regarding data policy.

The United Kingdom (Scotland) has an information governance framework for personal data, within which is a Public Benefit and Privacy Panel (PBPP) for health and social care data. The PBPP is a patient advocacy panel which scrutinises applications for access to NHS Scotland health data for secondary purposes with respect to the public benefit and privacy implications of proposed projects.

While many countries are extracting data from electronic clinical records to develop their key national datasets and for research (as will be discussed in the next section), 10 survey respondents in a 2019-20 survey on health data governance reported barriers to doing so.

In Luxembourg, data extraction from electronic clinical records for secondary uses is only lawful with the prior written consent of patients. Similarly, in Canada, electronic medical records in primary health care are in the custody and control of care providers who have no obligation and sometimes, depending on the jurisdiction, no legal authority to share data with public authorities, without express consent. As in Canada, the federal structure of Germany leads to different legal frameworks at the state level (state data protection laws, state hospital laws) that govern whether data may be extracted for secondary purposes. In Australia, data extraction is restricted by a number of legislative, privacy, secrecy and confidentiality requirements and medical records can be disclosed with consent, or in specified circumstances where authorised by law.

In France, extracting data from the electronic health record or DMP (dossier médical partagé) for the purposes of sharing and linking data is legally prohibited. France reports the legal prohibition came about because the national health insurance fund (CNAM) provides operational management of the linked health care administrative database and patients’ associations sought a guarantee that clinical data within the DMP would not be accessible to the insurer. It is, however, legally possible to create a dataset of anonymised data from DMP records.

In Japan, there is no national electronic health record system within which data might be contributed by each medical institution. Further, medical institutions require patient consent for each research or statistical project where data would be extracted and shared from their electronic records.

In Korea, it is legally possible to extract data from electronic health records for secondary uses but the interpretation of the law is strict so doing so is difficult in practice. In Belgium there is no real policy about the extraction of data from electronic records for secondary uses. In Latvia, there is no experience yet with data extraction as the implementation of the national e-health system has only started recently. In Ireland, most health records remain paper-based in acute care hospitals.

Concerns were further echoed by respondents to the 2021 EHR survey. In 2021, 15 respondents reported that problems with the quality of data within electronic clinical record system created a barrier to developing national health datasets from this data source. The most common concern was with unstructured (free text) data within EHRs that need to be structured following common terminology standards to be readily useable for statistics and research. Thirteen respondents also reported legal or policy barriers to public authorities extracting data from within EHRs to develop national health datasets.

Perhaps the most difficult barrier is in Switzerland, where the law which authorises the creation of electronic clinical records did not foresee the use of data from within this information system for national statistics or research and, as a result there is a total ban on utilising this information resource for any purpose within the public interest other than directly caring for an individual patient. Similarly, in Korea, the law authorising the Information Exchange Program only authorised the exchange of EHR records for direct patient care and there is no legal basis for the secondary use of EHR data.

In Sweden, whether data can be extracted from EHRs for a statistical purpose is limited to the legal authorisation of the specific use. Statistics and research uses that have not been already foreseen and legally authorised are restricted. Similarly, Finland’s law authorising the EHR system did not specify that health care quality monitoring could be undertaken with data from within the EHR system and are facing restrictions to this activity which is within the public interest. In Iceland, health data registries (datasets) are each authorised by a separate legislation. If a new registry (dataset) is needed, then it is necessary to pass a new legislation to authorise it. Similarly, Portugal reports a lack of legal authorisation to extract data for statistical purposes.

Japan and Turkey report concerns that the national data privacy law restricts their ability to extract data from within their EHR systems to build national datasets that are within the public interest. Canada reports the challenge of having different data protection laws within its 13 provinces and territories.

EU Members are also reporting challenges implementing the EU General Data Protection Regulation (GDPR). Italy reports that the GDPR provisions are complex and require the involvement of the data protection authority to develop effective solutions that support extraction of data from EHRs for statistical purposes. Similarly, Slovenia reports that the national legislation is very sophisticated and restrictive which limits their ability to extract data for statistical purposes.

In the Netherlands, problems have arisen following the introduction of the EU GDPR. Dutch health datasets are in the custody of various public sector organisations (such as the Dutch Hospital Data institute, and the Perined (child birth data) institute). Among the custodians of health data, there are different interpretations of the EU GDPR and some have determined that past data exchange arrangements are no longer legally permitted. To clarify that data exchange is lawful, some organisations and institutes are asking government for legislation authorising the exchange of electronic clinical data (see Chapter 3 for further discussion).

Clinical data are a key component of any health information system looking to improve care quality as well as enabling research and innovation. This section outlines the current situation in OECD countries regarding the exchange and interoperability of electronic health records data, and the key elements of successful integration.

Most OECD countries, 21 of 27 countries surveyed in 2021, are exchanging electronic clinical records among physicians, medical specialists and hospitals for the direct care of patients. Sixteen countries report one country-wide EHR system is in place. Thirteen countries reported that a nationally standardised patient summary is exchanged among health care providers at a national level, and a broader array of patient data are exchanged among health care providers at the sub-national (state, regional) level. In three countries, Belgium, Canada and the Czech Republic, patient data is exchanged among health care providers only at the sub-national (regional, state) level.

In 2021, the OECD surveyed countries regarding the readiness of their electronic health record systems to contribute to national information and research. Twenty-three of 27 countries reported a national organisation with primary responsibility for national EHR infrastructure development. Twenty countries reported that their national organisation is responsible for setting national standards for both clinical terminology within EHRs and standards for data exchange (electronic messaging).

Fourteen countries reported in 2021 that the national organisation responsible for EHR infrastructure development had a multidisciplinary governing body with representation from various stakeholder groups. Multi-disciplinary governance supports the development of standards that meet the needs of different stakeholders in the health information system.

Global consensus regarding terminology standards for key clinical terms has not been reached yet. There are, however, a few international terminology standards that are used by a significant share of countries.

In 2021, 18 respondents reported using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) for diagnostic terms; 16 respondents reported the Anatomical Therapeutic Chemical (ATC) Classification System for medication terms; 13 respondents reported the Logical Observation Identifiers Names and Codes (LOINC) for laboratory test terms; and 10 respondents reported DICOM standards for medical image terms. These results for 2021 are a small improvement from 2016, as the number of respondents adopting the ICD-10 diagnostic terms and ATC medication terms has grown by a few countries.

Twelve respondents reported adopting the Systematised Nomenclature of Medicine-Clinical Terms (SNOMED CT) for at least one key term within their EHR. SNOMED CT is a comprehensive set of terminology standards covering key terms within EHR records. The cost of deployment; however, is a barrier to widespread adoption and the number of respondents is unchanged from 2016.

However, there remain key terms within clinical records where there is no consensus among countries about which international standard could apply. These include surgical procedures, vital signs, healthy behaviours, socio-economic status, clinically relevant cultural and psychosocial characteristics, and patient reported outcomes and experiences. Further, there are often local standards that have been adopted or, in some cases, these elements are not coded to a terminology standard but recorded as free text.

The legacy of fragmented deployment of EHRs has resulted in 11 respondents reporting clinical terminology standards are inconsistent among different networks or regions within their country. While this remains a significant problem, it has improved from 2016 when 20 respondents reported this issue.

Twenty-one respondents in 2021 reported implementing policies or projects to improve the interoperability of data within electronic health record systems (EHRs). Seventeen respondents are adopting the HL7 Fast Healthcare Interoperability (Resource) standard and a further two respondents are considering adoption. The HL7 FHIR standard supports web-based applications in health care as they exist for other sectors such as for e-commerce, banking, and travel booking; and utilises commonly used web development tools which allow for a larger pool of developers and faster development.

Twelve respondents are also adopting SMART on FHIR standards (or similar) and a further 4 respondents are considering adopting SMART on FHIR. Substitutable Medical Applications and Reusable Technologies (SMART) is a standard used on top of FHIR to develop web-browser and mobile/smartphone apps that can be connected to/interact with any EHR system. For example, an app to assist patients with managing their medications or an app for secure communication with a health care provider.

Fourteen respondents reported developing public application programming interfaces (APIs) and an additional respondent is considering adopting this standard. Application programming interfaces (APIs) allow data sharing among different EHR software and Health Information Technologies, overcoming blockages to data interoperability.

Encouragingly, respondents reported participation in global collaborative work toward agreed international standards for clinical terminology and data exchange (electronic messaging). In 2021, 15 respondents reported participating in the Integrating the Healthcare Enterprise International collaboration and 10 respondents reported participating in the Global Digital Health Partnership.

There is extensive work underway within the European Union (EU) toward improving the accessibility, sharing and use of health data that, if successful, would have an influence on the evolution of global collaboration in the sharing, use and protection of health data. A key EU project is the eHealth Digital Service Infrastructure (eHDSI) for cross-border health data exchange under the Connecting Europe Facility (CEF) that is supporting EHR data exchange at the country level and the provision of core services at the EU level.

Another key project is the Joint Action Towards the European Health Data Space (TEHDAS). TEHDAS is developing European principles for the secondary use of health data, building upon successful development of health data hubs in a few countries, such as France and Finland, and aiming to develop health data governance and rules for cross-border data exchange, improve data quality and provide strong technical infrastructure and interoperability (EC, 2021[5]). The European Health Data Space has the potential to act as a powerful federator between national data hubs, promoting interoperability standards, best practices for data sharing across the European Union and setting a coherent governance framework.

The 2021 survey also asked respondents about the coding of health data to CDMs which facilitate within country statistical and research projects. In 2021, five respondents reported coding data within their EHR systems to a CDM. When the common data model is international in scope, such as the OMOP (Observational Medical Outcomes Partnership) CDM, such coding efforts support internationally comparable data for a wide array of research and statistical uses. There were some applications of the OMOP CDM reported by Australia and Israel in 2021. The Health Insurance Review and Assessment Agency (HIRA) in Korea coded linked health data to the OMOP CDM, including HIRA’s national insurance claims data, for the purposes of encouraging secure access to timely data for global COVID-19 research as part of the OHDSI project. France is coding data within the Health Data Hub to the OMOP CDM as part of the EU EHDEN project which is affiliated with OHDSI.

Surprisingly, given the mounting volume of data created, only 8 of 26 respondents in 2021 reported that EHR data are stored or processed using Cloud Computing services (Australia, Israel, Japan, Korea, Luxembourg, the Netherlands, Portugal and the United States). The majority of respondents are still managing EHR data on dedicated servers.

Essential to data security, integration and patient safety are unique identifiers. In 2021, 24 of 27 countries reported that they have a unique national number that identifies patients to build and electronic health record. Further, 23 countries reported having a unique national number that identifies health care providers or other authorised persons who are entering data into an electronic health record.

Fourteen respondents reported that clinical data are encrypted when they are exchanged to protect privacy and data security. Nine respondents reported that clinical data are exchanged using a dedicated, secure network. Security measures for these networks included a digital signature for ID (Denmark), digital signature with smartcard (Luxembourg, the Netherlands), multi-factor authentication (Canada, Italy, the Netherlands, Switzerland), digital certificates for ID verification (Japan, Lithuania), virtual safeboxes for data exchange (Israel), channel encryption (Italy), and IP security and Internet key exchange (Japan). A few respondents also noted data de-identification and pseudonymisation (Italy) and even data anonymisation (Costa Rica).

Respondents reported methods they are using to secure EHR data from unauthorised access, hacking and malware. These include virus scanning, firewalls, controlled access, access logs, audit logs, automated log-out, timely software updates, network separation, auditing hardware and databases, physical security for networked hardware, staff training in data security including how to identify phishing schemes, malware and other malicious programs, penetration tests (ethical hacking), vulnerability scanning, national authorities supervising cybersecurity among data processors, and business continuity and disaster recovery planning.

In the 2021 survey, 17 respondents reported that there are laws or regulations requiring health care providers to meet standards for national electronic health record interoperability. Sixteen respondents reported that laws or regulations require electronic messaging standards and 16 also respondents reported that laws or regulations require terminology standards.

In the 2021 EHR survey, 16 respondents reported that they have a certification process for the vendors of electronic health record system software that requires vendors to conform to particular health information exchange (electronic messaging) standards. Thirteen respondents reported a certification process that requires adherence to national standards for clinical terminology and 13 reported certifying vendors for adherence to requirements or standards for national EHR interoperability.

While not a national certification of software vendors, reimbursement for medical expenditures requires that providers follow certain terminology and exchange requirements in Israel. In Luxembourg, there is a national labelling process for software vendors to access the national EHR system. In Italy, there are no national requirements for certification, but individual regions may impose requirements. In Slovenia, certification has been legally authorised, but it is not yet implemented due to resource constraints. However, to connect to the national EHR system in Slovenia, vendors must use nationally standardised APIs (Application Programming Interfaces).

Another mechanism to verify if health data meet national expectations for data quality is to conduct audits of clinical records. In the 2021 EHR survey, 13 respondents reported that the electronic records of physicians, medical specialists and hospitals are audited to verify quality. An additional three respondents indicated that at least one of these three groups are audited to verify quality. In most cases, it is a national authority that is responsible for undertaking quality audits. In Canada and Sweden, regional authorities conduct audits. In Switzerland, private sector organisations can be certified to then conduct audits as part of certifying the compliance of communities to national requirements including auditing clinical records for quality. Under law in the United States, health care providers are responsible for generating auditing reports on the quality of their clinical records and ensuring data quality.

In 2021, OECD countries reported several different policy levers supporting EHR interoperability and the increased use of data from within EHR systems for direct care, patient centred services, research, statistics, applications development and other uses within the public interest. This section reviews countries use of laws or regulations requiring data standards; certification of software vendors; and incentive payments.

In 2021, 13 countries reported implementing laws or regulations that require health care providers to adopt electronic health record systems that meet national standards for both clinical terminology and electronic messaging (data exchange).

Sixteen countries reported laws or regulations requiring health care providers to meet standards for national EHR interoperability. In Iceland, regulations require that health care providers can connect to the Icelandic HealthNet (national EHR network). In Italy, the law defines a national federated system with a mandatory, nationwide, interoperability. In Lithuania, data is structured and standardised by law and must be suitable to be forwarded smoothly to the ESPBI IS (central EHR system). In Luxembourg, connecting to the DSP (central EHR system) requires meeting legal requirements for data standardisation. In Slovenia, IHE XDS and OpenEHR standards are required with proprietary modifications that are set out in law. In Switzerland, certifying communities and software vendors are required to meet national standards including HL7 FHIR and IHE. In Portugal, by law, health care providers IT systems must conform to a catalogue of standards to exchange data.

Another policy lever is requiring vendors of electronic health records systems to be certified to be in conformance with national data standards. Overall, 13 countries have a software vendor certification that requires vendors to meet national standards for both clinical terminology and electronic messaging.

Finally, 8 countries have incentive payments or penalties for health care providers to install EHR systems from a certified software vendor, 9 have these payments to health care providers to keep EHR systems up-to-date regarding changes to national standards over time and 11 have incentives or penalties to meet national requirements for EHR interoperability.

In most countries, patients have access to and can interact with their own medical records within a secure Internet portal. ‘Access’ means patients can view information contained in their own record and ‘interact’ means that patients can amend information, upload data or interact with their health care provider. Thirteen countries reported that 100% of patients have access to their own medical records through an Internet portal and 12 reported that 100% of patients can interact with their portal. Eighteen countries reported that patients can view their own records from all of their current health care providers and containing their current medications, lab tests, and imaging results.

Most respondents are regularly extracting data from the EHR system for public health monitoring (16 countries). Such uses have been accelerating in response to the COVID-19 pandemic. Further, countries have been increasingly depending upon data with EHR systems for their superior timeliness, enabling analysis of the pandemic situation and response in near real time. Ten countries reported regularly extracting EHR data to monitor the performance of the health system including, treatments, costs and health outcomes. Twelve countries regularly rely upon EHR data to monitor patient safety, including post-market surveillance of medications. Ten countries report that EHR data are extracted for health and medical research to improve patient care, health system efficiency or population health, such as long-term follow-up studies of patients experiencing different risk factors, health conditions and treatments. Five countries are regularly relying upon EHR data to facilitate and contribute to clinical trials, such as following clinical cohorts to measure health outcomes and health care encounters over time. Five countries also enable physicians to query the data to inform themselves about previous treatments and treatment outcomes when caring for patients.

The Netherlands, Denmark and Israel are the three countries with the most applications of machine learning, artificial intelligence algorithm development and other more advanced analytics based on EHR data that were measured in the 2021 survey. Overall, 8 countries reported data mining to find or extract data from the EHR; 8 countries are using EHRs to develop messages and alerts for patient care or managerial decision-making; and 7 countries are using EHRs to develop predictive analytics trained on EHR data for patient care or managerial decision-making. Six countries report national projects to integrate or link EHR data with genomic, environmental, behavioural, economic or other data. Three countries are also using natural language processing to convert free text to standardised (coded) data.

In 2021, most OECD countries surveyed had: 1. established a national organisation that was responsible for setting national clinical terminology and electronic messaging (exchange) standards; 2. created a multidisciplinary governing body for the national organisation that represents key stakeholders; 3. use unique identification of patients and health care providers; 4. adopted international terminology standards for diagnoses, medications, laboratory tests and medical images; 5 adopted the HL7 FHIR standard for data exchange (electronic messaging); and participate in global collaborative projects to improve international data standards.

Most countries have one country-wide electronic health record system and are exchanging EHRs at the national level including data sharing among physician offices and hospitals about patients’ treatment, medication use, laboratory tests and images.

Most countries have a Patient Internet Portal where patients can access their own medical records from all of their current health care providers. Most are extracting data from their EHR system for public health monitoring. Many countries are also utilising EHRs for other secondary purposes including health system performance monitoring, patient safety surveillance and health and medical research. Some are also developing big data analytics including machine learning, artificial intelligence algorithms with EHRs.

Countries reported several levers to improve the spread and interoperability of their electronic clinical data.

  • Sixteen had a legal requirement for health care providers to meet national standards for EHR interoperability and 13 had a legal requirement for health care providers to adopt an electronic health record system (software) that conformed with national standards for both clinical terminology and electronic messaging (exchange).

  • Thirteen countries had a certification of eHR system (software) vendors that required them to adopt national standards for both clinical terminology and electronic messaging and 13 had a certification that required software vendors to meet requirements for national EHR interoperability.

  • Eleven countries had financial incentives (or penalties) for health care providers to install an EHR system that meets national standards and requirements for national EHR interoperability. Nine countries report incentives for health care providers to keep their EHR system up-to-date as clinical terminology and electronic messaging standards change over time; and 8 reported incentives for health care providers to install and EHR system from a certified software vendor.


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[2] Open Data Institute (n.d.), What is data Infrastructure, https://theodi.org/topic/data-infrastructure/.

[4] Schulz, S., R. Stegwee and C. Chronaki (2018), “Standards in Healthcare Data”, in Fundamentals of Clinical Data Science, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-99713-1_3.

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← 1. In addition, the Youth Act, which regulates assistance provided to children, adolescents and their parents – which is a municipal responsibility. It covers developmental and parenting support for families, psychosocial and psychiatric problems, supplementing what families cannot do themselves.

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