Real-World Data (RWD) has been a cornerstone of the Life Sciences industry for decades, driving research, guiding clinical decisions, and informing policy. With recent FDA updates, the role of RWD continues to expand. However, traditional data extraction methods often result in “frozen” datasets, which pose significant challenges in a world that increasingly demands agility, updatability, and precision.
This article explores the three main shortcomings of frozen datasets and how adopting a more dynamic approach to data extraction can be a game-changer for Life Sciences.
1. Reduced Longterm Relevance
One of the major challenges with traditional RWD extraction methods is the cutoff time that creates frozen or static datasets. Once data is extracted, it no longer reflects the continuous stream of patient information, leading to a decline in relevance over time. Although efforts in the U.S. to tokenize data and provide a longitudinal view have made progress, they remain insufficient and are almost non-existent outside the U.S.
A 2023 study published in JAMA highlighted the limitations of frozen datasets in the context of cardiovascular research. The study found that datasets extracted more than six months before analysis often missed critical updates in patient treatment regimens, which led to significant discrepancies in research findings. “The relevance of real-time data in ensuring accurate and timely clinical insights cannot be overstated,” said Dr. Robert Califf, former FDA Commissioner and current Head of Clinical Policy at Verily.
2. The Lack of Context in Static Data
Another significant drawback of frozen datasets is their inability to provide access to updated or additional information, particularly in long-term research that spans several months or evolves in response to new hypotheses. Data extracted years ago cannot easily be linked to newer data, as anonymization processes often hinder accurate data matching. This challenge underscores the need for advanced technologies capable of securely connecting historical and current datasets without compromising privacy.
Contextualizing data—understanding the “why” behind the numbers, as well as identifying trends and developments—is essential for generating actionable insights. Without the ability to enrich and update data, researchers are often left with gaps that impede understanding and compromise the sustainability of their research efforts.
In a 2018 study conducted by the University of Oxford and the UK Biobank, researchers encountered significant challenges when using static datasets to investigate genetic predispositions to diabetes. The inability to update and contextualize existing data with ongoing patient information led to incomplete insights, requiring rounds of additional data collection to address gaps. This process increased overhead costs and financial strain. With strict budget constraints and limited resources, these unnecessary expenses often come at the cost of other critical efforts, such as clinical trials and long-term follow-ups.
3. Lagging Behind Trends and Discoveries
Frozen datasets, by their static nature, fail to keep pace with the rapidly evolving healthcare landscape. In recent years, the medical field has experienced an explosion of new discoveries. For instance, the number of new drug approvals by the FDA has grown significantly, with 55 novel drugs approved in 2023 alone. Similarly, new diseases, like COVID-19, have introduced unprecedented complexities to patient care, making it critical for datasets to stay current.
Beyond drugs, the discovery of new biomarkers is revolutionizing personalized medicine. A report by the National Institutes of Health (NIH) in 2022 emphasized that over 150 new biomarkers have been identified since 2010, with significant implications for the diagnosis and treatment of diseases like cancer and Alzheimer’s. Other factors, such as advancements in medical devices, shifts in regulatory requirements, and new standards for patient privacy, add further dynamism to the industry. Frozen datasets are unable to incorporate these rapidly emerging trends and discoveries over time, limiting their value in contemporary research.
Having live data that can be updated, enriched, and contextualized is crucial as healthcare continues to evolve at a rapid pace.
The Impact of Live Data on Healthcare Innovation
Shifting to live data is a game-changer for the pharmaceutical industry. It not only transforms medical research, long-term follow-ups, and drug development but also greatly enhances global research collaboration. For example, the Global Alliance for Genomics and Health (GA4GH) has been a leader in promoting real-time data sharing among researchers worldwide. By enabling live connections, GA4GH has streamlined collaborative efforts, particularly in the field of genomics, fostering more efficient and impactful research.
Briya’s Dynamic Datasets are also at the forefront of this transformative shift, designed to evolve seamlessly alongside ongoing research and complete or even replace traditional registries that are limited to structured, observational data. By providing fast and easy access to real-world, live data—whether through expertly pre-curated datasets or fully customizable solutions—Briya empowers researchers and organizations with unmatched data adaptability and quality. Sourced from a global network of healthcare providers, Briya’s dynamic datasets drive innovation, fuel breakthroughs, and accelerate progress in medical research and development.
In addition to driving healthcare innovation on a global scale, live data offers significant benefits for an organization’s bottom line and operational efficiency. It reduces costs associated with outdated or incomplete datasets, which often necessitate additional rounds of data collection or analysis to stay relevant. By leveraging real-time information, organizations can make faster, more accurate decisions, minimizing inefficiencies in research and clinical trials. In drug development, live data accelerates time to market by providing up-to-the-minute insights into patient responses and treatment outcomes, enabling companies to optimize resources and avoid unnecessary spending on ineffective trials.
The Future of RWD is Dynamic and Live
The healthcare industry is moving towards a future where static, frozen datasets are no longer sufficient. The demand for real-time, updatable, and comprehensive data is driving a fundamental transformation in how we approach research, clinical decision-making, and global collaboration.
As Dr. Amy Abernethy, former Principal Deputy Commissioner of the FDA, has noted, “The future of healthcare data lies in its ability to evolve and adapt in real-time, providing insights that are as dynamic as the world we live in.” The shift towards live data connections and augmented datasets is not just an incremental improvement; it represents a paradigm shift that will define the next generation of healthcare innovation.
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