4. Towards an integrated health information system in Korea

A modern, learning health system utilises health data effectively to create a continuous cycle of improvement through reflection, adjustment and evaluation. The same real-world evidence (RWE) that supports a learning health system and the provision of value-based care, also supports medical and health innovation, including the development of drugs, medical devices, tools and apps. Thus, investments in a learning health system yield benefits for society and the economy.

Korea has many of the building blocks in place to develop a health information system that supports a learning health system that meets the needs of the 21st Century. This report has described how personal health data, as well as other data relevant to health and well-being, are managed, exchanged, and deployed to advance policy objectives in Korea including service improvement, better public health, research outputs and innovation. Despite some considerable strengths and advantages, the current health information landscape in Korea remains fragmented is some aspects such that further reforms and developments are deemed necessary to enable Korea to create a learning health system. These are related to the legacy of past regulations, policies and organisational structures, as well as to current approaches to health data project planning and investment. Further there is a need to strengthen public trust in a learning health system through investment in a trustworthy system that meets reasonable expectations for data protection and security.

This chapter recommends seven requirements that can support Korea in the transition to becoming one of the most successful and high-performing health systems in the world. The requirements are based on two pre-requisites: 1. a mindset that sees data as a public good and a resource that can be harnessed to advance the health and welfare of the Korean people; 2. Establishing trust among all stakeholders, and including public transparency about the availability, uses and benefits, and protections of health data. This needs to be embodied in a national health information strategy that must be developed inclusively and be trusted by all stakeholders.

There was a clear consensus from the interviews conducted for this study that there is not yet such a strategy or roadmap in place for Korea to achieve the integrated health information system that is needed to support a learning health system and fulfil other policy objectives. A range of policies, regulations and enabling legislation will be needed to implement the national strategy. Technical infrastructure and standards will need to be implemented. An overarching governance framework will be required, including greater harmonisation of data privacy and security policies and practices.

The requirements include a new or existing national organisation to act as a hub for efficient and fair access to, and uses of, health data for the public benefit. This would greatly simplify the convoluted arrangements currently in place, enabling medical and health research and innovation as well as the development of information to advance public health, health care quality and health system performance. A single hub would also foster greater co-operation among stakeholders in the health information system and ensure that health data uses are trustworthy.

Experts and stakeholders expressed a high level of interest in improving health data standards, and secure data sharing and uses but those interviewed faced constraints from their existing mandates, legislations and resources. This suggests that that the data integration Korea will need for the 21st Century will not occur without the leadership of the Ministry of Health and Welfare.

The national strategy for a learning health system will steer Korea away from the current situation of data silos toward an integrated system where secure data exchange, linkage and secondary uses are the norm. The strategy should modernise data development, exchange, management, and governance and it will require a change management approach that builds trust among all stakeholders and the public. Some examples of the activities involved include the following:

  • Consult with governmental agencies about their needs for information, analytics and information products.

  • Consult with non-government stakeholders especially patient groups, regions and municipalities, provider organisations, health professional groups, insurers, academia, biomedical industry and software vendors.

  • Develop and implement a public information campaign, public consultations and other avenues for public input into the strategy.

  • Conduct public consultations at all stages of development of the national health data governance framework and provide public information, such as a website, to disseminate information about the development process and its outcome, as part of the national strategy.

  • Launch a campaign with communication experts to promote a dialogue with the public about the benefits of data sharing and exchange, with the goal of valuing health data in Korea as a public good (see below).

    • This public dialogue must assuage public and stakeholder concerns about privacy risks and reassure them by clearly communicating about how privacy will be protected when data are used.

Adequate resourcing of these activities will be critically important. This means allocating sufficient time and resources to consultation with stakeholder bodies and the public at all points in the development of the strategy, so that progress from a draft strategy to a final strategy to roadmaps and implementation will feel natural, expected and safe. It will be essential to build trust with the public and with health care providers by ensuring that obstacles to constructive dialogue are addressed first or, at least, there is a public commitment to address them. These include tension points discussed in this report (perception of privatisation of the health system, health care professionals’ mistrust of government, weak data privacy protections, and illegal uses of personal data that are not adequately penalised). The following Box 4.1 contains some additional information on building trust.

Central leadership means that a central ministry, such as the MoHW or a new authority or agency designated by it, would oversee the development and implementation of the national strategy and its components outlined above. It would develop campaigns and tools to improve public transparency about health information, information governance and public benefits from improvements in health information. It would also develop and maintain analytics products and dashboards for ministerial policy making and reporting, and evaluate and publicly report on progress in the implementation of the national strategy.

It would need to facilitate progress in policy and legal reforms to support the on-going development of an integrated health information system, and co-ordinate planning and funding of health information projects within the ministry to align them with the strategy. This would include continuous or periodic review of planning and funding of health information projects within the ministry to ensure they align with and contribute to the strategy and do not detract from or create disincentives to advance the strategy.

Perhaps most importantly, the MoHW would need to ensure that the transition to, and maintenance of, the new arrangements across all levels of the system are adequately and intelligently resourced.

Several key competencies would be required to achieve this:

  • Strategic planning of health information projects,

  • Evidence-based indicator development and policy analysis,

  • Informatics (IT architecture, data exchange standards, semantic interoperability),

  • Health data science (statistical and software development competencies, interoperability of analytics),

  • Legislative frameworks,

  • Privacy-by-design (privacy protection, data security and related information technology competencies), and

  • Public consultation and communications/public relations.

A central authority such as the Ministry of Health and Welfare (MoHW) or an appointed authority should lead the development of a national health information strategy. The ministry must be supported in developing the strategy by experts, particularly external experts in health data informatics, data interoperability and health data science, as well as external experts in “privacy-by-design” approaches to health data governance.

MoHW has already laid out several important initiatives including the first Comprehensive Health Insurance plan, adoption measures for My Healthway (February 2021), a road map for health data standardisation (April 2021), and innovative strategies for health data and AI (June 2021). A national health information strategy could tie these and other initiatives together into a coherent framework.

An important part of the strategy (and the need for central leadership) is to rationalise the functions, and improve collaboration among, the relevant agencies: HIRA and NHIS as well as KDCA, KOSTAT and KHIS. This report has identified that the current institutional arrangements are characterised by overlap, duplication, and inconsistent approaches to managing and using health data. This is not only wasteful and inefficient but creates a major barrier to a learning, high-performing health system that uses its data efficiently, intelligently and securely for a range of purposes.

Internal support will be needed to build a team to take the lead. The ministry could consider creating a new unit or separate authority/agency that engages or seconds experts in health information systems, health data science and informatics and health data governance. This expertise will be essential to ensuring an effective national strategy is developed and implemented.

Another key issue will be to have the right input in terms of technical, IT, policy, and legal expertise to develop a worthwhile and trustworthy strategy. Stakeholders will then be more at ease and comfortable to share their needs, their constraints, and their hopes for the strategy.

The national strategy for a learning health system should align with the broader policy frameworks to build a digital society (such as the Digital New Deal or subsequent strategy). In fact, most countries that are successfully digitalising their health systems have a national digital strategy – and data governance – that encompasses all areas of public policy including health. Estonia, for example, decided over two decades ago to become a “digital society” meaning that 99% of public services, including health care are accessible virtually.1 This has paid not only immense dividends during the COVID-19 pandemic, enabling the country’s health, education and welfare systems to continue to function as normal, it has also promoted technological and policy advances in privacy and digital identity, made Estonia into Europe’s top entrepreneurial hotspot according the World Economic Forum.2

The advantages of a cross-sector approach are particularly strong in the health arena given the value placed on privacy and security, the key role of non-health data (which can greatly enhance knowledge-generation), and the fact that health data infrastructure make a country more attractive for investment of biotech capital.

Legal authority will be needed to authorise and finance the national strategy and its implementation. Legal reforms are also needed to bring the health data governance law within Korea closer to the OECD Recommendation on Health Data Governance which aligns with the EU General Data Protection Regulation (GDPR) (OECD, 2019[2]).

The OECD Recommendation calls on countries to implement a national health data governance framework and sets out the principles for the development, content and evaluation of the framework. Implementation of this framework may require legal reforms or the publication of guidelines to ensure that all stakeholders in the health information system have a common understanding of their roles and responsibilities with respect to health data development and use and privacy and security protections. The national data governance framework should emphasise privacy-by-design and adherence to FAIR principles, that is that data are findable, accessible, interoperable and reusable.

Areas for potential legal reforms noted in this study include a revision to the Data Protection Law to enable national agencies who are already trusted to collect and process health data to be legally permitted to link data between them for legitimate purposes within the health-related public interest. Further revisions to privacy law should strengthen safeguards to protect data privacy and security, such as requiring data pseudonymisation and having penalties for data misuse that discourage illegal data uses that have damaged public trust.

A unifying policy framework is also necessary that will support a learning health system. Different bureaus within the MoHW are developing policies and funding projects that will affect the health information system and this needs to be co-ordinated within the MoHW; as well as the need to establish greater co-ordination among national agencies who have their own health information projects. Further co-ordination is needed at the whole of government level as other ministries are also funding health information projects for purposes of scientific or economic development.

HIRA was launched to be an intermediary between the National Health Insurance Service and health care providers, to protect health care providers from any potential unfairness that might have arisen from the consolidation of numerous insurers toward a single public insurance system. This role as a fair and objective intermediary (honest broker) could be strengthened by ensuring both legally and in its funding that HIRA is fully independent of NHIS and the government and focussed upon health care improvement for the benefit of all of the stakeholders in the health information system.

To fulfil this role, and to operate at arm’s length from the government, the governance of the National Health Information System would require representation of all key stakeholder groups in the health information system, especially patients, consumers, health care providers, health care institutions, governmental agencies and businesses that contribute to and depend upon the health information system. It is critical that a learning health care system is jointly developed with patient groups and providers.

Reporting of data on quality and safety developed from enhanced data collection and information systems should be shared with the public and with the NHIS and the government only after development, testing and evaluation conducted by the HIRA governing board. Testing would build trust and confidence in the value, usefulness and accuracy of reporting tools among patients, the public and health care providers.

The range of data that could be linked and integrated to realise a learning health system would need to be expanded to include:

  • EMR data, particularly lab results, and imaging results

  • Data related to patient outcomes such as present on admission (POA) flags, Patient-reported outcomes (PROMS) and experience (PREMS), and clinical outcomes

  • Environmental, behavioural and socio-economic characteristics of patients

  • Private insurance claims and uninsured health care services.

  • Patient Registry

Where such data are already available within other national governmental organisations, such as the data held by the NHIS on medical check-ups, long-term care and home-care benefit claims, and social care data, they should be securely shared with HIRA to be linked to HIRA data holdings for the purpose of improving the Korean health system for the benefit of the Korean people.

Organisational changes at HIRA would also help to both minimise the burden of reporting born by health care providers and maximise the clinical value of the quality registries HIRA would be supporting. The real-time microdata HIRA collects currently from health care providers for the purpose of adjudication of health care insurance claims must be integrated with real-time clinical data to provide real-time clinical care quality and safety monitoring that is useful for health care providers and supports the continual improvement of patient outcomes and health care workplaces.

The current process of duplicative data collection, with a separate and non-real time data collection system for the assessment side of HIRA, should be phased out to improve the current process with quality indicators on the assessment side lagging health care events by several years.

Instead, the collection of real-time data from health care providers should be based on the collection of clinically relevant and timely data for a full monitoring, reflection and evaluation cycle of improvement of the health system. Priority should be given to designing a data collection and reporting system that provides high quality and timely information supporting decision making of different stakeholders including clear provision of information to consumers about the quality of health care services, useful and valuable information within clinical workflows to support clinical decision-making, and useful and timely dashboards for health care organisations and for the government to support continuous improvement, organisational planning, policy planning and evaluation.

The importance of data to support public health policy decisions in real time has been made clear to all OECD countries because of combatting the COVID-19 pandemic. The focus of planning within the OECD, the WHO and the European Union has turned to examining the data flows needed to be resilient to future public health emergencies from multiple sources including environmental, radiological, biological and other threats. The integration of patient-level health care data within HIRA with NHIS data on medical check-ups, KDCA data on COVID-19 cases and international travellers (ITS), and KOSTAT data on mortality provided a powerful tool for policy planning and management of the pandemic. It also provided a basis for global research into the pandemic and potential treatments.

Such data integration and timely exchange among HIRA, NHIS, KDCA and KOSTAT will be essential to creating a learning health system that includes the surveillance, evaluation and improvement of health outcomes of patients with infectious and chronic diseases. Such surveillance is part of the mandate of the new KDCA, but its mandate cannot be fulfilled without data exchange and integration with the data collected by the other agencies.

However, at the present time, negotiating data sharing agreements among national agencies where data linkage is necessary has been very complicated and resource consuming for all national agencies. Further, where national agencies do not see the exchange and data linkage as a specific win-win for them, they may not engage in negotiations or may drag out work over a long period of time.

Moving forward, it will be essential to incentivise co-operation among national agencies toward a common shared goal of developing a learning health system that improves the outcomes of patients and the effectiveness and efficiency of health care services. Data held by NHIS and HIRA could be linked so that HIRA data on prescribed medications, for example, integrates in real-time with data within NHIS on patients’ socio-economic characteristics; or HIRA data on health care pathways could be linked with NHIS data on long-term care patients.

Reforms are needed to encourage the large health data custodians (HIRA, NHIS, KOSTAT and KDCA) to collaborate and to link and integrate data in a secure manner to advance the health-related public interest and to improve the coherence and usability of personal health records for the public. These changes must ensure that data linkages among agencies the government has deemed trustworthy can be undertaken in a privacy protective and efficient manner. In this context, a single entry point (or health data hub) for accessing linked data would benefit all stakeholders and is proposed later in this chapter.

A first-rate health data infrastructure and information system in Korea will require change of the KHIS remit to cover secondary uses and, as described in the previous section, greater collaboration with other key actors.

Global standards for data exchange and semantic interoperability, administered and governed by KHIS, must include privacy-by-design protections, particularly federated learning (distributed analytics) building on the recent experience of HIRA with OHDSI. Standards should include interoperability in analytics, information and knowledge and foster the broad adoption of the OMOP common data model (CDM), building from recent investment of the MoHW to code some data holdings within HIRA, NHIS, the National Cancer Centre and KDCA to OMOP CDM and recent investments of the Ministry of Industry to code data within private hospitals to OMOP CDM. The standards should Include lifecycle interoperability to ensure analytical uses of historical data as the information system evolves (i.e. ensure health trajectories and longitudinal data analysis are supported).

As has been discussed throughout this report, clinical data are an integral part of a learning health system and make a fundamental contribution both directly and indirectly to health care quality and safety, value-based health care, public health surveillance and to biotechnology, medical and life-sciences research and innovation. These objective encompass primary and secondary uses of EMR data. The KHIS, however, has no mandate for considering secondary use of these data, or the contribution of the OMOP Common Data Model to realising intermediate goals for clinical data interoperability, nor how it might be used to, for example, realise a personal health record years before all health care institutions are conforming to national clinical data terminology and exchange standards. Further there is a role envisaged for KHIS in providing health information governance, but it would not be possible for KHIS to fulfil such a role without close collaboration with the other national agencies responsible for health data. The KHIS should therefore be involved in developing and implementing a learning health system, as all key stakeholders should be.

KHIS should be supported to achieve its long-term goal of supporting secondary use of clinical data and R&D in the medical field, and to build a legal basis which would enable KHIS to collaborate with all stakeholders in health information system.

To complement laws and policies, funding and financial incentives will be needed to encourage compliance with national data standards, for demonstrating (verifiable) data interoperability, for launching a modern health data hub (see below) and to ensure national agencies responsible for health data have the resources needed to support greater inter-agency collaboration to realise the strategy.

This will require a review of government funding and financial incentives related to the exchange and use of health data, including research projects funded by government ministries. It may also require explicit financial incentives to encourage health care providers, national agencies responsible for health data and other actors to move to certified IT solutions and succeed in achieving verifiable interoperability.

Currently, the Korean Government plans call for financial incentives to EMR software vendors to adopt national standards for data terminology and exchange and to health care providers/organisations who demonstrate they are using an EMR that conforms with national standards.

Current plans for financial incentives to adopt standards for clinical data content and exchange have not included funding for the transition costs that may be faced by health care institutions as they convert from their existing system to the new standards. Concerns were raised in this study regarding the costs for infrastructure, such as upgrades to software, hardware and networking, and softer costs related to staff training and lower productivity during the transition. These up-front costs may be too high for small clinics and hospitals to self-fund and therefore they may not be able to convert, despite the attractiveness of the incentive payment.

The MoHW should also evaluate how plans for broader reforms to health care funding and remuneration that reward care co-ordination and value-based care would affect the design and functioning of the learning health system.

In short, this will include the following requirements:

  • Provide needs-based funding to hospitals and provider organisations for transitioning their local data systems and infrastructure to an agreed national format and standard.

  • Institute incentives for verifiable interoperability and meaningful use of health data including auditing data quality, interoperability and privacy and security protections AND successful and consistent provision of data to authorised agencies (in addition to KHIS certification process).

  • Together with stakeholders, develop a fair and balanced method to sanction and penalise lack of compliance, comprising financial and other levers.

Paying organisations such as hospitals an explicit financial reward to provide data to agencies such as HIRA has outward appeal. Learning from experiences in other OECD countries, financial incentives for data exchange are insufficient because they do not incentivise data quality, completeness and usability. Demonstrating verifiable interoperability would include incentive payments, funding or accreditation that is conditional upon passing data quality checks and passing thorough (random) data quality audits, as well as meeting national requirements for data privacy and security protection (see also the next section). As was reported in Chapter 2, 13 countries reported in 2021 that the electronic clinical records of physicians, medical specialists and hospitals are audited to verify quality.

Pay-for-data schemes can be problematic. For one, it frames the exchange of data in transactional terms as opposed to a collaboration that is not only mutually beneficial but also extends benefits to patients and to society. Second, paying for data implies ownership and signals to providers that they could be paid for a data point every time it is used. This not only goes against the concept of health data as a public good and a resource to increase public welfare (see Chapter 2) but could end up being costly for agencies such as HIRA (while profitable for providers). Unconditional payment does not incentivise providers to ensure data quality, completeness, useability and privacy and security protection, nor does it create a “win-win” where data providers invest in health data quality because the data help them in a cycle of continuous improvement of their patients’ outcomes, the quality of their services and their own workplace environment. If the data are viewed by providers as only useful for HIRA, there is no incentive to invest in data quality.

Investing in, and rewarding, verifiable interoperability is preferred. This includes earmarked funding to ensure data that are exchanged are of high-quality (complete, timely, formatted and coded to required standards, and ready for key primary and secondary uses), that the data exchanged are secure and patients’ privacy has been protected. Metadata (the who, what, where, when and why of data without the actual data content) plays a critical here because it provides the contextual information needed to put data to use. It makes data findable and verifiable, and is one of the elementsof common a standard format to enable interoperability. Developing data quality metrics based on can support verifiable interoperability, along with an auditing process to provide a detailed review of data quality, interoperability and privacy and security protections.

Legal and policy reforms (outlined above) would ensure that providers and other actors are legally obligated to share and exchange their data with agreed agencies as part of their contractual obligations under the NHI system, with a failure to comply attracting sanction and/or penalties. These penalties can be financial or other, such as exerting social or cultural pressure (e.g. publicly naming organisations that do not contribute data for the benefit of the Korean public).

While financial penalties may appear the equivalent to pay-for-data, a key difference is that they frame data exchange in contractual, not transactional, terms. This is an important distinction because it removes implication of data ownership as well as reducing the temptation to sell data, which could create privacy risks.

Verifiable interoperability should also include measures of the meaningful use of electronic patient data by health care providers for direct patient care and for the efficient management of their clinic or hospital. Meaningful use incentives – like those implemented by the US Centers for Medicare & Medicaid Services – could be considered. This US programme provides financial rewards to health care providers who use appropriate EHR technologies in demonstrated, meaningful ways (as opposed to simply paying for provision of data to a third party). Meaningful uses could include, for example, providing contributing towards a public reporting initiative on health care quality or outcomes throughout Korea.

OECD countries are increasingly providing a unique entry point for access to all public sector health data. The unique entry point could be through an expanded mandate of an existing national organisation or through the creation of a new organisation. This “health data hub” has a primary aim of improving access to health data for uses that are within the public interest while protecting data privacy and security. Consider the examples of such access points presented in Chapter 2 such as the French Health Data Hub, Findata in Finland, the Australian Health Dataplace and the new European initiative to encourage Health Dataspaces in all EU countries.

The current arrangements in Korea for accessing data for secondary uses are fragmented. To create a world-leading data infrastructure as a basis for a learning health system, this must be consolidated into a one-stop-shop for secure access to various health data from a variety of sources outlined in Chapter 1, Figure 1.1. Such consolidation would simplify the process for researchers and other secondary users of Korean health data, and enable secure, record-level linkage of all relevant datasets to create valuable knowledge. It would make access to data for research and other secondary purposes in Korea more secure, more efficient and easier. Further, it would make public sector health data collection, data use and data protection more fully transparent to the public and to the research community.

Importantly, this would not require all data to be copied, transferred, or held in one place or repository because it is now possible to perform complex analyses across a distributed or federated network. Under this approach, data always remain with their custodians. Only queries (research questions), and the aggregate results or results of statistical modelling are sent back to the researcher (via the “hub”) who submitted the query.

Recent advances in analytics have meant that, in terms of statistical power, there is virtually no disadvantage in this approach. Modern data science has ensured that this approach is equivalent to analyses of data as if they were aggregated in one place. For example, the OHDSI initiative applied Cox Proportional Hazard regression across a federated structure without accessing the patient-level data in a Korean study comparing two drugs used to treat acute coronary syndrome (You, 2020[3]).

However, the precondition of a distributed network is that all data sources have already been coded to the same Common Data Model (such as the OMOP CDM which Korea has already invested in) – again underscoring the importance of data harmonisation and standardisation in a learning health system.

A single hub or entry point would serve as a portal where authorised agencies, organisations or individuals can securely access the data held by participants in the federated network. Obviously, the inclusion of EMR data would be a major advantage in such a federated structure, and (as discussed above) incentives will be required for private providers and hospitals to not only standardise and map their data to common formats, but make their data available and participate actively in initiatives.

The data hub can also securely integrate, link and standardise data to a Common Data Model for analytical applications where integrated data are required, such as the linkage of clinical data, medical check-ups, claims data, and cause of death information. The data within the hub can then be securely accessed by researchers using a distributed/federated network.

The functions of the data hub would include:

  • Creation of a national catalogue of all public sector health data

  • Support for organisations coding data to the Common Data Model (OMOP CDM)

  • Support for researchers applying for data linkages and access to data

  • Regular feedback to organisations processing health data regarding data useability, standardisation and quality

  • Regular feedback to organisations development standards for health data terminology, exchange and interoperability regarding the data needs of the research community

  • Approval (permitting) of applications for access to microdata from multiple national organisations

  • Data linkage of microdata from multiple national organisations

  • Secure access to microdata through real time remote services, including requests through the OHDSI distributed network

  • Data de-identification and pseudonymisation and secure storage of linkage keys

  • Public transparency regarding data collection, data linkages and approved projects

  • Public transparency regarding the process to apply for and be approved access to data and mechanisms to appeal a decision.

Governance would need to include regular consultation with all of the key stakeholders in the health information system including the key national organisations (HIRA, NHIS, KDCA, KHIS and KOSTAT), health care providers and health care organisations and patients/consumers. The hub would also need to be resourced to provide a timely service with qualified staff and appropriate computing facilities to support research work including software development, machine learning and development of AI algorithms.

There are many benefits of consolidating access to health data under the roof of an existing institution, such as HIRA, NHIS or KHIS. However, one potential drawback is that data linkage and access would not be at “arm’s length” from organisations with direct involvement in the provision or assessment of health insurance or other services. It would therefore be important to ringfence this function from other activities to build trust among the stakeholders (especially provider organisations) and clarify that the purpose of health data linkages and uses are to serve the public interest in better health, high quality health care and in privacy protection and data security.

More important than who is responsible for this hub is that it benefits national organisations as well as health care providers and hospitals, including the provision of data linkage services that meet their needs and provide them with support to grow the research uses of health data without each developing its own full suite of data access services. Again, the involvement of all key stakeholders in its governance would also create the opportunity to engage these stakeholders in a collaborative effort to develop and improve the quality and efficiency of standards for health data terminology, exchange and interoperability for both primary and secondary data uses.

However, a hub alone will not be sufficient to improve collaboration and data sharing among the large national organisations who are processing personal health data. The national strategy must emphasise the importance of secure access to, linkage of and sharing of health data to serve the public interest and include the necessary changes to organisational mandates, legislations and resources to ensure that exchanging data becomes the default position, where the exchange is secure and the purpose of the exchange is to serve the public interest.


[2] OECD (2019), “Recommendation of the Council on Health Data Governance”, OECD, Paris, http://www.oecd.org/health/health-systems/Recommendation-of-OECD-Council-on-Health-Data-Governance-Booklet.pdf.

[1] OECD (2015), Health Data Governance: Privacy, Monitoring and Research, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264244566-en.

[3] You, S. (2020), “Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention”, JAMA, Vol. 324/16, pp. 1640-1650, http://doi:10.1001/jama.2020.16167.

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