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Germany – AKEK – Secondary Use of Patient Data for Research Purposes

RWE 201 – Germany – AKEK – Secondary Use of Patient Data for Research Purposes

 

AKEK Statement (Aug 2023): https://www.akek.de/wp-content/uploads/Stellungnahme-EHDS_V7.1.pdf

The Working Group of Medical Ethics Commissions in Germany (AKEK) has emphasized the importance of secondary patient data use for research in public health. Its relevance was magnified during the COVID-19 pandemic, showing the need for efficient data collection and evaluation. Germany requires a robust regulatory framework promoting both data privacy and research.

During the pandemic, the lack of standardization in country-specific regulations made data handling challenging. The upcoming European Health Data Space Regulation (EHDS-R) and the proposed German Health Data Use Act (Gesundheitsdatennutzungsgesetz – GDNG) aim to harmonize regulations, facilitating responsible health data access at multiple governance levels.

The pandemic also spurred interest in alternative consent models, like “broad consent” and “dynamic consent”, aiming to balance personal data rights, research freedom, and public health. As obtaining individual consent becomes complex, the German law needs clear rules to reconcile these rights.

Germany’s legislators need to evaluate consent models, considering pandemic-driven challenges. They should provide compensatory measures, such as defining standards for data pseudonymisation and anonymisation. Ensuring transparency in research results and balancing intellectual property rights are also vital.

For effective secondary patient data use in research, a strong governance structure, inclusive of ethics committees, is essential. These committees are pivotal in ensuring data protection and balancing the risks and benefits of research.

AKEK highlights key demands for promoting secondary patient data use:

  1. Nationwide standardisation of legal regulations to reduce bureaucratic hurdles.
  2. A balance between individual data rights, research freedom, and societal welfare.
  3. Risk-adapted methods to reconcile scientific evidence with data privacy.
  4. The involvement of ethics committees in governance for a balanced approach.

In conclusion, secondary patient data use is crucial for medical advancements. Germany needs a comprehensive strategy that balances individual rights with research benefits. The urgency brought by the COVID-19 pandemic necessitates swift legislative action by Germany.

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EU – EMA’s 2025 Vision for RWE

November 5, 2023|2023, RWE 201|

RWE 201 - EU – EMA’s 2025 Vision for RWE   EMA 2025 Vision: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299492/ EMA RWE Framework Report: https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf The European Medicines Agency (EMA) isn't simply relying [...]

EU – The Data Governance Act

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EU – The European Health Data Space

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EU – EU4Health Program

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Germany – AKEK – Secondary Use of Patient Data for Research Purposes2023-10-14T08:46:40+00:00

UK – NHS England – Federated Data Platform – Lessons Learned from the General Practice Data for Planning and Research Initiative

RWE 201 – UK – NHS England – Federated Data Platform – Lessons Learned from the General Practice Data for Planning and Research Initiative

NHS England – Federated Data Platform: https://www.gov.uk/government/news/the-nhs-federated-data-platform-the-importance-of-building-bridges-with-the-public

The National Health Service (NHS) in England is in the process of procuring a Federated Data Platform (FDP), aiming to streamline IT systems and improve healthcare delivery. However, the initiative has come under scrutiny, particularly around ethics, privacy, and commercial involvement. The National Data Guardian’s role is to offer advice and ensure that the NHS maintains public trust, especially concerning data management.

Public concerns mainly revolve around commercial interest in NHS data. Research indicates that the public is generally open to commercial involvement if conditions such as transparent communication, public benefit, and adequate safeguards against misuse are met. Previous initiatives like the General Practice Data for Planning and Research (GPDPR) serve as cautionary tales. Lack of communication led to misinformation and resulted in a significant number of people opting out of data-sharing, negatively affecting health research and planning.

To avoid repeating past mistakes, the National Data Guardian advocates for meaningful and open dialogue between NHS England and the public. They outline three key focus areas:

  1. Value to Patients and the NHS: Clearly communicate the FDP’s purpose and benefits. Answer critical questions like the scope of the program, public choice in data sharing, and how it solves real-time problems in healthcare delivery.
  2. Integrity of Decision-making: Given the sizable contract and existing relationships with potential suppliers, the NHS must demonstrate a transparent, fair procurement process. Information should be provided about who makes decisions, how they are made, and how safeguards are in place to ensure fair competition.
  3. Relationship with the Supplier: The public needs assurance that NHS maintains control over the data and the system. This includes clarifying what data the supplier can access and the safeguards against misuse. Concerns about vendor lock-in and the ability for the NHS to terminate partnerships without compromising patient care or incurring significant costs should also be addressed.

In summary, the key to the FDP’s success lies in transparent, meaningful engagement with the public and healthcare professionals. Filling the existing knowledge gap with accurate information can quell mistrust and speculation, thereby earning public trust for a smoother implementation. Open communication, even when there are no complete answers, can prevent suspicion and ensure ongoing public support for the FDP initiative.

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EU – EMA’s 2025 Vision for RWE

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RWE 201 - EU – EMA’s 2025 Vision for RWE   EMA 2025 Vision: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299492/ EMA RWE Framework Report: https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf The European Medicines Agency (EMA) isn't simply relying [...]

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EU – The European Health Data Space

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EU – EU4Health Program

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UK – NHS England – Federated Data Platform – Lessons Learned from the General Practice Data for Planning and Research Initiative2023-10-14T08:39:42+00:00

UK – NICE – RWE Framework

RWE 201 – UK – NICE – RWE Framework

 

NICE RWE Framework: https://www.nice.org.uk/corporate/ecd9/chapter/overview

Real-world data (RWD) encompasses various types of information on patient health, care delivery, and experiences collected outside traditional clinical trials. This data is crucial in forming National Institute for Health and Care Excellence (NICE) guidance.

NICE utilises RWD to:

  1. Characterise health conditions, care pathways, and patient outcomes.
  2. Design and validate economic models related to healthcare.
  3. Evaluate digital health technologies.
  4. Address health inequalities.
  5. Assess the safety of medical technologies.
  6. Examine the effects of interventions on care delivery.

While Randomised Controlled Trials (RCTs) are preferred for studying interventions, they may not always be available or applicable to real-world NHS settings for various reasons, including ethical constraints, technical challenges, or lack of funding. Sometimes, RCTs may also have limitations such as irrelevant comparators, exclusion of important population groups, or poor quality, making them insufficient for NHS decision-making.

NICE often resorts to non-randomised studies, particularly for medical devices, public health interventions, and assessments of medicines. Observational cohort studies and single-arm trials with real-world external control are commonly used non-randomised methods.

NICE aims to use RWD more consistently to fill evidence gaps and expedite patient access to innovations. For RWD to be trusted, it needs to be gathered and analysed transparently, utilising fit-for-purpose data while mitigating biases.

NICE has developed a real-world evidence framework to guide the generation of high-quality evidence. This framework is targeted primarily at those creating evidence to inform NICE guidance but is also relevant for patients, data collectors, and evidence reviewers. Core principles for generating trusted real-world evidence include ensuring data quality, transparency, and the use of analytical methods that minimise bias.

The framework consists of several sections:

  1. Introduction: Outlines the role and potential of RWD.
  2. Study Conduct: Describes expectations for planning and reporting RWD studies.
  3. Assessing Data Suitability: Provides guidelines for assessing the quality and relevance of RWD.
  4. Methods for Real-world Studies: Offers specific recommendations for conducting non-randomised studies.

Companies are encouraged to seek early advice from NICE on integrating RWD into their evidence-generation plans.

In summary, RWD has become an essential tool for improving healthcare outcomes and policy, fulfilling NICE’s strategic ambitions to drive innovation and resolve gaps in knowledge through robust, transparent, and high-quality evidence.

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EU – EMA’s 2025 Vision for RWE

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EU – The European Health Data Space

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UK – NICE – RWE Framework2023-10-14T08:35:21+00:00

UK – MHRA – Randomised Controlled Trials Using Real World Data

RWE 201 – UK – MHRA – Randomised Controlled Trials Using Real World Data

 

MHRA RWD Guidance: https://www.gov.uk/government/publications/mhra-guidance-on-the-use-of-real-world-data-in-clinical-studies-to-support-regulatory-decisions/mhra-guideline-on-randomised-controlled-trials-using-real-world-data-to-support-regulatory-decisions

The MHRA guideline focuses on sponsors planning to conduct randomised, controlled clinical trials (RCTs) primarily using Real-World Data (RWD) for regulatory decisions concerning medicinal products.

Scope

The guideline covers:

  1. Clinical trial authorisation in the UK.
  2. Trial design, including endpoint selection and safety requirements.

It excludes discussions on observational studies, and clinical trials using RWD as a control arm.

Definition and Types of RWD Trials

RWD is health data obtained outside clinical studies and can include electronic healthcare records, disease registries, and patient-reported outcomes. In a simple RWD-based RCT, patients are randomized to receive standard clinical care alone or an added intervention. Data quality and study design need to be as rigorous as traditional RCTs for the results to be valid for regulatory decisions.

Protocol Requirements

The protocol should pre-specify objectives, data collection methods, and primary and secondary endpoints. Consent is required before enrolment, and in most real-world settings, patients are not blinded to treatment allocation.

Regulatory Acceptability

From a regulatory standpoint, the source of the data (RWD, hybrid, or traditional) is irrelevant as long as the trial answers the necessary regulatory questions. The need for blinding should be considered, especially if the primary endpoints are not sufficiently objective.

Examples and Practical Considerations

The guideline suggests that RWD-based trials can be effective when dealing with an established EHR database and objective endpoints like all-cause mortality. Scenarios where the intervention is an existing licensed product with a well-known safety profile are particularly suited for RWD trials. It’s critical not to assume the completeness of potential endpoints in EHRs. Sponsors are advised to conduct a feasibility study to assess the reliability of the data capture methods.

Data Gaps and Hybrid Trials

If the RWD source does not cover all required endpoints, a hybrid trial can supplement RWD with specific additional data. These could be additional clinical assessments, which might even be carried out remotely.

In summary, the MHRA guideline serves as a comprehensive framework for sponsors interested in leveraging RWD for RCTs aimed at supporting regulatory decisions for medicinal products. While RWD-based trials offer advantages in reducing patient and healthcare burdens, they must be designed and executed with rigor comparable to traditional RCTs to be deemed acceptable for regulatory purposes.

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EU – EMA’s 2025 Vision for RWE

November 5, 2023|2023, RWE 201|

RWE 201 - EU – EMA’s 2025 Vision for RWE   EMA 2025 Vision: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299492/ EMA RWE Framework Report: https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf The European Medicines Agency (EMA) isn't simply relying [...]

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EU – The European Health Data Space

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UK – MHRA – Randomised Controlled Trials Using Real World Data2023-10-14T08:31:42+00:00

UK – MHRA – Use of RWD to Support Regulatory Decisions

RWE 201 – UK – MHRA – Use of RWD to Support Regulatory Decisions

 

MHRA RWD Guidance: https://www.gov.uk/government/publications/mhra-guidance-on-the-use-of-real-world-data-in-clinical-studies-to-support-regulatory-decisions/mhra-guidance-on-the-use-of-real-world-data-in-clinical-studies-to-support-regulatory-decisions

The MHRA (Medicines and Healthcare products Regulatory Agency) has produced a comprehensive series of guidelines addressing the use of Real-World Data (RWD) in clinical research, particularly aiming to guide sponsors who intend to use RWD to support regulatory decisions.

Key Highlights:

[1] Definition and Sources: RWD is defined as data relating to patient health status or healthcare delivery, collected outside the controlled environment of a clinical study. EHR, primary and secondary care records, and disease registries are key sources of RWD. The guidelines also recognize newer sources like wearable devices and specialized websites.

[2] Purpose and Scope: The guidelines are intended to inform the design of studies that leverage RWD for evidence suitable for regulatory decisions, irrespective of geographical location. It outlines requirements for gaining approval for such studies to be conducted in the UK.

[3] Benefits: Using RWD can potentially expedite research and reduce costs. RWD can also make certain previously unfeasible studies viable, resulting in a broader understanding of treatments’ real-world effectiveness.

[4] Data Quality: Ensuring the data source’s quality is crucial; otherwise, the benefits of using RWD become moot. Key considerations include the source data’s accuracy, validity, reliability, and provenance. Pre-study protocols must address questions about data quality, population size, data capture details, data availability, interoperability issues, and more.

[5] Methodological Rigor: Traditional principles of evidence strength, such as randomization and blinding, still apply when using RWD. The guidelines indicate that sponsors should clarify how data quality checks, data extraction, and data handling will be performed and validated.

[6] Digital Health Technologies: Digital platforms, including wearables and sensors, are recognized as valid RWD sources. These technologies must be validated for their intended use.

[7] Ethical and Legal Aspects: The guidelines stress the importance of understanding privacy and security policies associated with database use, including data transfer and storage restrictions.

[8] Inspection: MHRA will continue its risk-based Good Clinical Practice (GCP) inspections. The integrity of the reported data will be a focus during these inspections, which may also involve reviewing the systems and processes used for RWD data management.

[9] Sponsor Responsibilities: Sponsors must clearly outline methods for data selection, extraction, transfer, and handling in the study protocol. Validity of the RWD should be formally documented and approved before study initiation.

Future Outlook:

Overall, the guidelines strive to provide a structured approach for incorporating RWD into clinical research, ensuring data integrity, methodological rigor, and compliance with ethical and legal standards. By doing so, they aim to pave the way for more effective and rapid healthcare solutions.

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EU – EMA’s 2025 Vision for RWE

November 5, 2023|2023, RWE 201|

RWE 201 - EU – EMA’s 2025 Vision for RWE   EMA 2025 Vision: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299492/ EMA RWE Framework Report: https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf The European Medicines Agency (EMA) isn't simply relying [...]

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EU – The European Health Data Space

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UK – MHRA – Use of RWD to Support Regulatory Decisions2023-10-14T08:25:36+00:00

Real World Evidence (RWE) 201 – France – Haute Autorité de Santé (HAS) – Real World Study Guide

RWE 201 – France – Haute Autorité de Santé (HAS) – Real World Study Guide

 

HAS Real World Stidy Guide: https://www.has-sante.fr/upload/docs/application/pdf/2021-06/real-world_studies_for_the_assessment_of_medicinal_products_and_medical_devices.pdf

The Haute Autorité de Santé (HAS), France’s National Authority for Health, is responsible for scientifically evaluating medicinal products and medical devices in terms of their medical, economic, and public health value. While clinical trials have traditionally been the foundation for these assessments, HAS is increasingly incorporating “real-world data” (RWE) into their evaluations.

Real-world data comes from sources other than conventional clinical trials and includes observational data and data on the use, efficacy, and safety of health products. This kind of data is particularly useful because clinical trials often have limitations; their controlled conditions can differ significantly from real-life medical practice, and their results might not be applicable in all cases. For example, new therapies involving gene technologies or machine learning bring uncertainties that can only be fully understood in real-world conditions.

Real-world studies offer advantages like understanding a product’s performance in actual clinical settings, identifying the patients most likely to benefit, and foreseeing potential risks. They also allow for the inclusion of patient perspectives, which are becoming increasingly important in the evaluation process.

HAS has begun updating its methodological guide on real-world studies to provide practical guidelines for such evaluations. This is due to increased access to health data and the growing recognition of the importance of real-world data to complement clinical trials. These guidelines aim to aid all stakeholders involved in the health product evaluation process, including manufacturers, research companies, and academic teams, in the design and implementation of real-world studies.

In summary, HAS is expanding its reliance on real-world data to enhance the quality and applicability of its health product assessments. This shift is aimed at capturing a more comprehensive picture of product effectiveness and safety, which ultimately benefits patient care.

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EU – EMA’s 2025 Vision for RWE

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Real World Evidence (RWE) 201 – France – Haute Autorité de Santé (HAS) – Real World Study Guide2023-09-04T08:30:12+00:00

Real World Evidence (RWE) 201 – France – CNIL Regulatory Sandbox: Digital Health

RWE 201 – France – CNIL Regulatory Sandbox: Digital Health

The French Data Protection Agency (CNIL) has been actively supporting digital health technology innovators through its regulatory “sandbox.” These projects range from federated learning across health data warehouses to building diagnostic tools in oncology, statistical indicators for medical research, and a therapeutic game for minors with eating disorders. The CNIL provides crucial guidance on overcoming regulatory challenges, including the nature of data, legal frameworks, and data security measures.

Benefits for RWD, RWE, and Digital Health Innovators:

  1. Navigating Regulatory Challenges: The CNIL’s sandbox provides a safe environment to test solutions and understand regulations. For RWD and RWE developers, this means an easier path to compliance with GDPR and other privacy laws.
  2. Interconnected Data Sources: For projects like Resilience in oncology, CNIL’s guidance enabled the interconnection of various data sources. This has implications for RWD, as it becomes easier to integrate data from disparate sources for more comprehensive Real-World Evidence.
  3. Data Security: With its focus on secure data processing, the sandbox offers a blueprint for ensuring the safety of health data, which is invaluable for digital health innovators dealing with sensitive patient information.
  4. AI and Machine Learning: Projects like the one carried out at Lille University Hospital utilized federated learning protocols, offering a roadmap for implementing machine learning algorithms in healthcare. This aids RWD and RWE applications where machine learning could provide new insights.
  5. Specialized Use-Cases: The Vertexica project focusing on minors with eating disorders shows how data protection can be maintained even in specialized healthcare solutions, thereby ensuring the ethical use of Real-World Data.
  6. Knowledge Sharing: The joint work and multiple exchanges with CNIL have generated lessons that could be useful for the broader health sector, facilitating faster and more secure innovation.
  7. ‘Privacy by Design’: The emphasis on integrating GDPR compliance from the design phase benefits all stakeholders by baking in data protection from the outset, which is a fundamental need in RWD and RWE applications.
  8. Stakeholder Collaboration: The sandbox projects involve multi-disciplinary teams, demonstrating a collaborative approach that could benefit digital health innovators, RWD and RWE developers in addressing complex regulatory and ethical issues.

In essence, the CNIL’s regulatory sandbox serves as an invaluable resource, not just as a testing ground but as a knowledge base for RWD, RWE, and digital health innovators. It provides practical insights into overcoming regulatory challenges and implementing secure, effective healthcare solutions.

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EU – EMA’s 2025 Vision for RWE

November 5, 2023|2023, RWE 201|

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Real World Evidence (RWE) 201 – France – CNIL Regulatory Sandbox: Digital Health2023-09-03T18:17:48+00:00

Real World Evidence (RWE) 201 – France – CNIL Reference Methodologies: Facilitating Access to Real World Data

RWE 201 – France – CNIL Reference Methodologies: Facilitating Access to Real World Data

 

The CNIL (Commission Nationale de l’Informatique et des Libertés) is the French data protection authority. CNIL has issued various “Reference Methodologies” (Méthodologies de Référence or MRs) which are guidelines/frameworks for compliance with data protection regulations in specific areas e.g., MR-001 (interventional research) and MR-003 (non-interventional research) which cover research involving direct interactions with people (RIPH), or MR-004 for research involving secondary use of existing personal healthcare data i.e., research not involving direct interaction with people (RNIPH).

By declaring conformity to the applicable reference methodology to the CNIL, research sponsors do not need to seek individual authorisation for each research project that involves non-anonymous data, making this an efficient and effective form of self-regulation.

Key features of MR-004 conformity include:

  1. Data Minimisation: Only collect the data that is strictly necessary for the research or healthcare activity.
  2. Purpose Limitation: Use the data only for the specified, explicit, and legitimate purposes for which it was collected.
  3. Consent: Access to and use (re-use) of existing patient health data is subject to informing the affected patients (patient information).
  4. Security: Guidelines for data storage, encryption, and access control, in line with GDPR requirements.
  5. Data Subject Rights: Details about how to facilitate data subjects’ rights like access, rectification, deletion, and data portability.
  6. Data Retention: Sets time limits on how long the data can be stored and provides guidance on secure deletion practices.
  7. Accountability and Governance: Stresses the importance of record-keeping, conducting impact assessments, and potentially appointing a Data Protection Officer (DPO).
  8. Data Sharing: Provides guidelines for sharing data with third parties, including cross-border data transfers.
  9. Legal Compliance: Ensures that the data processing activities are compliant with other relevant laws and ethical considerations.

By adhering to MR-004 or similar CNIL Reference Methodologies (as applicable), healthcare organizations and researchers can use real-world data while fulfilling their legal obligations and ethical responsibilities for data protection (GDPR). Note that these guidelines are subject to change, so it’s crucial to consult the most current version and seek legal advice for complex scenarios.

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Real World Evidence (RWE) 201 – France – Health Data Hub: Facilitating Access to Real World Data

RWE 201 – France – Health Data Hub: Facilitating Access to Real World Data

 

The purpose of France’s Health Data Hub (HDH) is to facilitate the sharing of health data to support research and innovation. Launched in December 2019, the HDH is a health data platform established by the French government to combine existing health databases and make them more accessible for research and development purposes [1].

The platform allows public interest project leaders to access large volumes of data and utilize the platform’s computing power to run complex research algorithms [2].

The reuse of health data has significant potential for enhancing healthcare quality and patient support through research. This includes [3]:

  • Evaluating the effectiveness of treatments for rare pathologies like sarcoma by combining patient and insurance data.
  • Enhancing prescription alert systems to help healthcare professionals make better decisions in complex clinical scenarios.
  • Developing early warning tools for conditions like heart failure by correlating pacemaker and hospitalization data.
  • Predicting disease progression, as in Parkinson’s disease, and improving preventive measures using patient cohort data.
  • Streamlining the reporting and analysis of adverse drug reactions to improve healthcare system transparency.
  • Utilizing AI to improve early cancer screening, thus reducing false positives and negatives, and saving medical time.
  • Gathering data on long-term effects of medications, particularly in cases of organ transplantation, to optimize therapeutic strategies.

Researchers interested in accessing real-world data (RWD) and generating real-world evidence (RWE) can benefit from the HDH in several ways:

  1. Data Accessibility: The HDH provides a single gateway for health data in France, making it easier for researchers to access various data sources [4].
  2. Data Catalogue: The platform includes a data catalogue with one of the world’s largest healthcare claims databases, offering researchers a rich source of information for their studies [4].
  3. Collaboration: The HDH brings together 56 stakeholders, including the largest French public organization for scientific research, CNRS, fostering collaboration between government, producers of health data, and users of public and private health data [1].
  4. Support for Projects: The HDH provides researchers with the necessary resources and infrastructure to carry out their studies [4].
  5. Data Security: The platform ensures the protection of health data through a data access approval process, maintaining the privacy and security of sensitive information [4].

By providing researchers with easier access to health data and the necessary resources to analyse and process this data, the HDH can help generate valuable insights and contribute to advancements in the health sector [4].

 

References:

[1] Everything you need to know about Health Data Hub – Implicity

Link: https://www.implicity.com/everything-you-need-to-know-about-health-data-hub/

[2] The Health Data Hub publishes its roadmap for 2022 – French Healthcare (30 May 2022)

Link: https://frenchhealthcare.fr/the-health-data-hub-publishes-its-roadmap-for-2022/

[3] Health Data Hub – FAQ in English

Link: https://www.health-data-hub.fr/page/faq-english

[4] Health Data Hub – Multi-stakeholder workshop RWD quality and RWE use (26 June 2023)

Link: https://www.ema.europa.eu/en/documents/presentation/presentation-rwd-data-quality-experience-france-e-bacry-health-data-hub_en.pdf

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Real World Evidence (RWE) 201 – Canada – Health Canada’s position on the CADTH Guidance for Reporting RWE to Support Decision-making

RWE 201 – Canada – Health Canada’s position on the CADTH Guidance for Reporting RWE to Support Decision-making

 

Health Canada released its position (May 2023) on the Canadian Agency for Drugs and Technologies in Health (CADTH) Guidance for Reporting Real-World Evidence (RWE) to Support Decision-making.

The CADTH RWE guidance, developed in collaboration with Health Canada and other stakeholders, aims to promote the use of high-quality real-world data (RWD) and RWE in drug development and regulatory decision-making. It also emphasizes the importance of transparent and comprehensive reporting of RWE studies to assess reliability and reproducibility.

While traditional clinical trials remain the primary source of evidence for regulatory decision-making, Health Canada acknowledges the value of RWD and RWE.

The agency is open to receiving submissions that rely on high-quality RWD and RWE in certain situations, such as:

  1. Expanding evidence-based indications for populations often excluded from clinical trials, including children, older adults, and expectant mothers
  2. Addressing diseases with limited treatment options or rare conditions
  3. Supporting post-market safety and effectiveness evaluations
  4. Informing the design of future clinical trials

The CADTH guidance provides a framework (and checklist) for reporting RWE studies, which can help researchers and healthcare professionals better understand the potential benefits and limitations of RWE in regulatory decision-making.

By promoting the use of high-quality RWD and RWE, the guidance aims to improve the overall quality of evidence available for drug development and regulatory decisions, ultimately benefiting patients and healthcare systems.

As a real-world evidence researcher or regulatory compliance specialist, you may find this guidance useful in your work, as it can help you provide actionable and engaging insights to researchers and healthcare professionals. By understanding and implementing the CADTH guidance, you can contribute to the development of more effective and efficient regulatory compliance solutions in the field of real-world evidence.

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