1,049 words, 6 minutes read time.
Kelly H. Zou
Kelly H. Zou, Ph.D., PStat® is Founder and CEO, AI4Purpose Inc. and Head of Global Medical Analytics, Real World Evidence, and Health Economics and Outcomes Research, Viatris Inc. She is an elected Fellow of the American Statistical Association and an Accredited Professional Statistician. Previously at Pfizer Inc, she was Vice President and Head of Medical Analytics & Insights; Senior Director of Real World Evidence, Group Lead of Methods & Algorithms and Analytic Science Lead; Senior Director of Statistics.
We are pleased to share that the article “IMPACCT RWE Summit Allows Exploration of Future of Real-World Evidence” by Kelly Zou, originally published in Amstat News on October 3, 2024, has been reposted with permission from the American Statistical Association (ASA). You can read the full article here.
How has the landscape for real-world evidence progressed in the last 12 months?
The field of real-world evidence generated from real-world data has progressed significantly since the 21st Century Cures Act. First, efforts to enhance data interoperability have facilitated smoother data exchange across different platforms and systems, making it easier to aggregate or integrate data from diverse sources. Enhanced data analytics and artificial intelligence tools have improved the ability to process and interpret large data sets. These advances have enabled more precise and actionable insights from real-world data.
Second, regulatory bodies such as the US Food and Drug Administration and European Medicines Agency have issued new guidelines and frameworks to support the use of real-world evidence in decision-making processes. Their emphasis on big data has increased the acceptance and integration of real-world evidence in randomized controlled clinical trials for drug approvals.
Next, there has been increasing collaboration between health care providers, biopharmaceutical companies, and health technology firms. One example is Gravitate Health, with a large set of industry and academic partners in both Europe and the United States. Moreover, an increased focus on target-trial emulations, external control arms, and patient-reported outcomes may provide a more holistic patient outcome assessment.
Finally, data privacy regulations and ethical guidelines are paramount to ensure the responsible use of real-world data, addressing concerns about patient confidentiality and privacy.
In summary, there have been recent advances in technology, regulatory support, collaboration, patient-centric approaches, ethical standards, and interoperability in terms of real-world evidence generation.
What are the biggest challenges the field is facing in real-world evidence, and how can the industry overcome them?
The industry faces several hurdles in fully maximizing the value of real-world data to gain timely and relevant real-world evidence. High-quality, standardized, and fit-for-purpose data is crucial. Integrating data from various sources remains challenging. For example, data sources are particularly siloed in the United States. Seamless integration is essential for comprehensive analysis.
Next, navigating regulatory requirements and ethical concerns is necessary to maintain compliance and protect patient privacy. In terms of data science, advanced analytics and relying on AI and machine learning are useful in processing and interpreting vast amounts of data accurately. Also, engaging stakeholders—including patients, health care providers, and policymakers—is vital for successful real-world evidence generation.
Finally, actionable insights through use cases are crucial for demonstrating the values of real-world data. To address these challenges, industry must consider ways for end-to-end evidence generation to complement randomized controlled clinical trials.
What is the most exciting opportunity to maximize the utility of real-world evidence?
The most exciting opportunity to maximize the utility of real-world data and real-world evidence lies in leveraging advanced data science and AI. The FDA has issued two discussion papers, while the European Medicines Agency has issued a reflection paper—all on AI.
AI/ML may be used to process vast amounts of real-world data quickly and accurately, uncovering patterns and insights that were previously unattainable. This capability enhances predictive analytics, enabling more precise and personalized treatment plans.
Additionally, AI-driven digital health and innovative tools may help identify and correct inconsistencies in big data, thus ensuring more reliable outcomes. And integrating AI with real-world evidence not only accelerates drug development and approval processes but also enhances post-market surveillance, leading to safer and more effective health care solutions.
This is an era of potential transformative shifts in how health care data can be accessed and harnessed in diagnoses, treatments, and prognoses to optimize patient care.
What are the challenges of advancing real-world evidence and ensuring data quality is at a high level in the process?
Several challenges in advancing real-world evidence and maintaining data quality lie in the complexities of “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources,” according to the FDA. Overall, challenges in advancing real-world evidence and maintaining data quality include the following:
- Standardizing and integrating diverse data types
- Ensuring accuracy, completeness, and consistency
- Adhering to privacy standards and ethical use
- Creating clear guidelines and robust methodologies
- Accessing advanced tools and expertise
- Integrating and sharing data across platforms seamlessly
Specifically, standardizing data and integrating diverse data types require extensive data engineering and computer science capabilities. Ensuring accuracy, completeness, and consistency require one to understand data generation and data capture modalities. Adhering to multinational privacy standards and ethical use can be daunting. Establishing clear guidelines, robust processes, and cutting-edge tools are critical for understanding how real-world data can be used in regulatory settings and for safety purposes, as well as for health technology assessments.
What are you looking forward to about the 12th IMPACCT Real-World Evidence Summit?
I have participated in IMPACCT RWE summits over the years and chaired two of them. In this series of summits, industry leaders and experts discuss latest trends and future directions in real-world evidence. Panels share their thoughts about regulatory perspectives, data integration, and the impact of real-world evidence on clinical practice.
In terms of use case examples, it is particularly relevant for biopharma experts, data providers, and analytic solution providers to showcase both the successes and pitfalls of real-world evidence for health care decision-making.
The workshops have been excellent. Last year, my co-instructor and I ran an interactive workshop on external control arms. All sessions are designed for attendees to network with peers, industry leaders, and potential collaborators.
The exhibit hall provides the opportunity to talk with company representatives about the latest tools, technologies, and services.
Finally, Boston in the fall is wonderful for professional networking, especially from the perspective of the former Bostonian in me.