The Future of Clinical Trials: RWD and Beyond

As the role of real-world evidence (RWE) expands in pharmaceutical approval, market access, and post-marketing, drug developers face the need to adapt to new challenges and harness their potential.

With regulatory agencies like the FDA embracing RWE to inform decisions and monitor product safety, it becomes essential for developers to efficiently navigate the vast array of data sources, determine the most relevant information, and extract valuable insights to drive effective decision-making. Success in this evolving landscape hinges on overcoming these challenges and leveraging RWE to demonstrate value for market access and commercialization.

Understanding RWD(Real-world data)

Patient health data and healthcare delivery information are routinely gathered from diverse sources. These sources include electronic health records, claims, and billing activities, product and disease registries, as well as patient-generated data collected from medical and health monitoring devices in various settings, including in-home use. The comprehensive nature of data collection provides valuable insights into patient health status and enables a more holistic understanding of healthcare practices and outcomes.

Market Outlook

The healthcare industry, including doctors, biotech, and pharmaceutical companies, recognizes the immense potential of leveraging real-world data (RWD) to enhance health outcomes. In fact, Acorn AI reports that RWD was utilized in approximately 75% of new drug applications (NDAs) and Biologic License Applications (BLAs) in 2023.

However, despite the promising theory behind RWD analysis, there exists a gap between its potential and current practices. The collection, aggregation, and analysis of vast amounts of diverse data pose significant challenges. It is crucial to address issues such as ensuring privacy and data protection, minimizing bias, and deriving meaningful insights that can truly improve outcomes. Overcoming these challenges is essential to fully unlock the transformative power of RWD in healthcare. [Reference]

Supporting Clinical Trials with RWD

Let's explore how data and evidence are being utilized in various applications today.

• Clinical Planning

In the realm of clinical planning, there is a wealth of valuable data pertaining to different disease states, comorbidities, and biomarkers. However, the challenge lies in accessing this data as it is often dispersed across multiple sources or "banks" and structured in diverse formats. Hospital networks maintain their unique nomenclature for electronic and medical health records, while laboratories house imaging and testing data separately.

• Optimization of clinical trial recruitment and protocol design

In many cases, clinical trials are conducted as isolated endeavors, without fully harnessing the wealth of available data sources that could potentially inform and enhance the approach. However, by leveraging real-world data, clinical teams can gain valuable insights prior to initiating a trial, enabling them to refine the protocol effectively. Real-world data can aid in various aspects, including cohort recruitment, questionnaire design, and workflow development, empowering the clinical team to make informed decisions and optimize the trial process.

Example of improved recruitment?

The advantages for both patients and healthcare providers are significant. By incorporating real-world data from patients' medical health records into a study, they gain access to trials that they might not have been aware of otherwise. This integration of real-world data enriches the findings and analysis of the study, enhancing its overall value. Furthermore, the clinical trial data can be reintegrated into the patients' records after the study, providing a comprehensive and longitudinal view of their healthcare journey.?

• Post-marketing surveillance?

This plays a crucial role in utilizing real-world data and evidence. While the primary focus has been on pharmacovigilance and assessing the safety of drugs after their launch, this use case remains highly important today. Tracking the long-term effects of new drugs and devices is essential as certain events may not surface during clinical trials. By monitoring patterns over an extended period, researchers can identify potential adverse events and take proactive measures to minimize their impact.?

Barriers to Adoption in Clinical Trials

One of the significant challenges that have emerged with the increasing interest in RWD and RWE is accessibility. While there is a wealth of data available, accessing and utilizing it effectively can be a complex and cumbersome process.?

• Accessibility

The healthcare system produces an astonishing volume of data, estimated at around a zettabyte (a trillion gigabytes) per year, and this quantity is projected to double every two years. However, despite this wealth of data, it is dispersed across numerous silos, characterized by varying languages, formats, and coding systems. The heterogeneity of the data presents both immense potential and significant challenges.

• Privacy

In certain instances, it can be advantageous to link data with specific individuals, particularly when offering opportunities for clinical trial participation. In such cases, obtaining consent from the individuals, either directly or through their healthcare providers, becomes essential. Collaborations with healthcare providers are fostering this type of patient consent, as patients who perceive their providers as trustworthy sources of guidance are more inclined to grant permission for accessing their medical records.

• Human Error / Bias

The vast amount of data in RWD provides researchers with the opportunity to conduct in-depth analysis, uncovering hidden patterns that would otherwise go unnoticed. To achieve this, training algorithms to perform complex calculations becomes crucial. However, there are inherent risks associated with this process. One major challenge is the identification and integration of all relevant factors and variables, such as treatment patterns, drug availability, disease severity, care settings, and comorbidities.?

Much of the RWD obtained from medical records or claims is often fragmented, offering only a partial view of the overall health landscape. Researchers must be mindful of these gaps and actively seek alternative data sources to fill in the missing information.

The Future of RWD

The effectiveness of RWD is directly linked to the size and quality of the data set under analysis. However, the standardization and interoperability of data sets pose challenges in harnessing the full potential of RWD. To overcome these challenges, there is a growing reliance on technologies such as artificial intelligence (AI), natural language processing (NLP), and robotic process automation (RPA).?

These advanced capabilities enable researchers to unlock valuable insights from RWD by streamlining data processing, enhancing data quality, and facilitating more efficient analysis. The integration of AI, NLP, and RPA holds the promise of revealing powerful insights that can drive advancements in healthcare and inform evidence-based decision-making.