How Sqilline Health support the EU data quality framework?
The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to make informed decisions about treatment effectiveness and safety. The Data Quality Framework (DQF) for EU Medicines regulation, endorsed by the European Medicines Agency (EMA) is an important step towards addressing this issue. However, the journey toward leveraging the full potential of RWD and RWE can only be enhanced by overcoming several challenges, ranging from data discoverability, transparency in data curation and data quality assurance, the linkage of data across various platforms, and the protection of sensitive data.
Observational data have long been utilized by regulators and the pharmaceutical industry to identify significant adverse events associated with treatments; however, both in the US and Europe, regulators are exploring how to appropriately use RWE derived from observational studies to inform their decisions about treatment effectiveness. In addition, good-quality observational RWD enables the spectrum of healthcare stakeholders to obtain a more comprehensive view of patient health and treatment outcomes, facilitating the identification of trends, patterns, and insights that may have otherwise gone unnoticed.
At Sqilline Health, our Danny Platform is built on a commitment to transparency in how we curate real-world data, from acquiring raw coded data, to transforming it into analyzable formats, ensuring its quality and relevance for answering specific scientific and clinical questions.
While a minority of the data in EHRs are recorded in structured fields, most of the data are not, which may only be accessible as free-text, reports in portable document formats (PDFs) or other formats. The breadth and depth of this data are tremendous; however, the curation of this data into analyzable data sets requires significant effort. Free-text doctor notes can be converted into structured, coded data manually or through natural language processing and machine learning which is the role of modern technology.
The findability, accessibility, interoperability, and reusability (FAIR) principles outline the dimensions of RWD that are fundamental considerations in assessing their usefulness. Such data must also be of sufficient quality and fit-for-purpose. Collecting RWD specifically for research has advantages because it ensures important data is gathered and regularly checked for quality.
Data discoverability and linkage remain significant hurdles, with RWD often scattered across disparate systems and sources. Addressing these challenges requires concerted efforts from stakeholders across the healthcare ecosystem. Initiatives aimed at improving data standardization, interoperability, and privacy protection are essential. Sqilline Health supports the collaboration between healthcare providers, technology companies, regulators, and pharmaceutical industry as it is key to building a robust infrastructure for RWD and RWE utilization.
The EU perspective on RWD quality
The EU has supported the development of systems to facilitate data sharing while ensuring compliance with privacy regulations. The European Health Data Space (EHDS) aims to foster cross-border data exchanges and transfers to support healthcare delivery, research, and innovation. The EHDS seeks to strike a balance between enabling data sharing for legitimate purposes and safeguarding individual privacy rights. Recently, the EMA has written a reflection paper. Ultimately, regulators want to be convinced that the RWD used to generate RWE is reliable, relevant, and fit-for-purpose. Regulators also want to be assured that non-interventional RWD studies are designed and conducted rigorously. By implementing robust data governance frameworks and fostering collaboration between stakeholders, healthcare organizations can harness the power of real-world data while safeguarding patient privacy and complying with regulatory requirements.
The data quality framework in the EU is more detailed and encompasses various dimensions as compared to the US. For EMA, the major considerations are transparency, reliability, extensiveness, coherence, and timeliness included within twelve data quality dimensions.
System interoperability and data privacy
It has become clear that the value of RWD increases as various data sources are linked together. Accurate linkage of different data sources requires overcoming the challenges of interoperability. Other challenges persist due to varying levels of digitalization across member states, as well as differences in data governance frameworks and privacy regulations. Sqilline Health standardizes data curation across these disparate data types and sources supporting consensus among stakeholders and alignment with industry best practices.
Safeguarding patient privacy and ensuring compliance with regulatory requirements are critical considerations in the collection, use, and sharing of RWD. Stringent privacy protections, such as de-identification techniques, encryption protocols, and access controls, are essential for mitigating the risks of unauthorized access, data breaches, and privacy violations. By prioritizing patient privacy and data security, stakeholders can foster trust and confidence in RWD/RWE initiatives, enabling data-driven decision-making while respecting individual privacy rights.
Illustrative example for detailed fitness-for-use assessment
An example is provided here for a Chronic Lymphocytic Leukaemia (CLL) External Comparator study based on multi-site EHR. Note that this is purely an illustrative use case to demonstrate how to use the framework (See table below).
Table: Detailed fitness-for-use assessment for a Chronic Lymphocytic Leukaemia study
CLL: Chronic Lymphocytic Leukaemia; RCT: Randomized controlled trial; BTKi: Bruton tyrosine kinase inhibitor; AE: adverse event.
Conclusion
EMA provides a data quality framework for medicines regulation which is an important and necessary step in the field. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). Sqilline Health with Danny Platform is part of the HMA-EMA Catalogue of RWD Studies and provides a practical approach towards the utilization of RWD and RWE in healthcare decision-making and scientific research.
