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Maximizing the Potential of Real-World Data for Evidence Generation

Male doctor holding a stethoscope in front of a patient being pushed in a wheelchair.
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Traditional randomized clinical trials (RCTs) are widely recognized as a reliable framework for generating evidence on the effectiveness and safety of health technologies for regulatory purposes. However, when alternative sources of evidence – such as real-world data (RWD) from diverse settings – are the only option, the rigid processes of RCTs pose a challenge. Health authorities and national health systems worldwide are increasingly accepting the need to move beyond relying solely on RCT data.


RWD, in the form of non-identified patient health information from various sources, complements and expedites the outcomes of RCTs. Yet, despite the wealth of RWD available, effectively identifying complete, up-to-date and relevant data remains a challenge. It can be difficult to secure information for specific research questions. The vast volume of non-identified patient data from different countries, sources (such as registries and hospitals) and disease types often amounts to billions of records. Additionally, researchers have faced difficulty gaining insights into the contents of each dataset without purchasing access. This not only burdens staffing and research budgets but also limits the integration of RWD into traditional clinical research and breakthroughs in treatment.


To fully harness the potential of RWD, researchers need comprehensive knowledge and access to globally available data assets. Researchers must have a greater understanding of the applicable legal and regulatory guidelines, criteria for data quality and validation and the ability to navigate data based on settings, disease types and geography. Without this insight, the inefficient sorting of RWD can result in significant time and value loss. Therefore, improving the identification and filtering processes for datasets is crucial in leveraging fit-for-purpose RWD to generate real-world evidence (RWE).


Understanding the uses of RWD

Generating meaningful evidence from the abundant information generated daily by healthcare professionals (HCPs), health administration bodies (claims, insurances), medical devices and digital apps can be challenging. Primarily, the data is not collected with analytical purposes in mind but rather to facilitate patient management by HCPs, dispensation management by pharmacists and reimbursement processing of claims. Furthermore, this data is not uniformly formatted for quick review. However, this diverse dataset holds immense potential and insights that can be used for conducting a wide range of studies, including safety studies, drug utilization studies, epidemiological evaluations, health economics and outcome research studies. It is important to recognize that not all data possesses the same level of usability, but by utilizing it effectively, valuable insights can be gained.


Once non-identified RWD is curated and harmonized, it is ready for analysis and use for various purposes across the drug development cycle. This can assist with the early planning crucial to establishing robust RWE strategies, including designing optimal clinical trials, selecting appropriate comparators and conducting comprehensive post-marketing evaluations. To support safe and successful product launches, address new development requirements and assess the impact of drugs, pharmaceutical companies and regulatory bodies are presently developing internal knowledge and expertise and are actively implementing RWD-based strategies in real world settings to ensure effectiveness and safety.


Additionally, healthcare stakeholders are widely recognizing the benefits of RWD in generating valuable evidence to support healthcare product development and lifecycle strategies. Regulators worldwide are increasingly incorporating RWE derived from RWD into their decision-making processes. Although RWD may not possess the same level of control and structure as data generated from RCTs, its importance is growing. In certain situations, RWD is the sole option for generating evidence, particularly when evaluating rare diseases.


Implementing a metadata catalog strategy

The transformation from RWD to RWE often faces challenges due to the heterogeneous nature of data sources across different settings, with varying formats and access complexities. It is important to recognize that healthcare professionals primarily focus on patient care rather than collecting data in a standardized manner.


Achieving full interoperability between data sources and settings remains a significant hurdle. In response, regulatory bodies are launching initiatives like the European Health Data Space (EHDS) to promote the principles of making RWD easily findable, accessible, interoperable and reusable (FAIR), as initially outlined by a group of international researchers in 2016. However, there is still considerable progress to be made in fully resolving the concerns.


The first obstacle faced by stakeholders in the healthcare ecosystem is finding appropriate data assets that serve their purpose in generating insights and evidence. To facilitate a rapid and effective identification/exploration process, decision-makers require the ability to search, sort, filter and compare options. This enables them to determine which assets or combinations of assets best meet their needs. Comprehensive descriptions and evaluations of data sources require expertise, efficient technology and engagement from data custodians to ensure accurate and up-to-date information.


In this context, metadata catalogs play a vital role as essential applications that facilitate the search functionality across various country-specific or global data sources. They enable users to easily evaluate assets based on content, accessibility, usage restrictions, key quality metrics and prior usage documented in publications. By leveraging robust data and efficient search and comparison tools, it becomes feasible to identify appropriate health data sources and refine results according to user preferences. The development of such catalogs is of significant interest to national regulatory bodies. It is crucial to foster a unified and harmonized vision and strategy among stakeholders to ensure seamless interoperability and accessibility of multiple data sources for researchers' data analysis needs.


Leveraging RWD to deliver powerful insights faster

RWD can provide deeper comprehension of patient journeys, reveal essential treatment pathways and demonstrate the safety and efficacy of drugs in complex scenarios. However, generating analysis-ready RWD is a complex undertaking involving data selection, collection, assessment and analysis. It also requires a thorough understanding of the standards and processes that work in tandem to effectively analyze usable data.

In today's fast-paced healthcare environment, understanding how RWD is collected and utilized is crucial for the creation of meaningful RWE. Embracing this knowledge opens the door to innovative advancements and informed decision-making, ultimately shaping positive healthcare outcomes.

About the author:

Michèle Arnoe is the head of global real-world data at IQVIA, overseeing the visibility of RWD assets in terms of access, transformation and compliant usage throughout the drug development life cycle. 

With a career encompassing various stages of drug development from pre-clinical to post-approval, Arnoe has held key roles in leading organizations. She initially worked for the French company Cegedim, providing technology and services in the digital healthcare ecosystem to launch their international expansion. She then was vice president for business management and strategy in the European peri-approval division at Parexel. She also served as global head of business development at Cerep SA providing pre-clinical development services.

Arnoe returned to Cegedim to serve as general manager for France strategic data and as VP for medical research. Following the acquisition of Cegedim, she became head of innovation for IQVIA in France and subsequently head of global real-world data assets. Her extensive experience and expertise make her a dynamic leader in the healthcare industry.