Our world is drowning in data. 

Humanity generates a staggering 402.74 million terabytes of information every day. From the countless videos we stream to the endless social media scrolling, our global digital footprint is growing exponentially. This data deluge is reshaping industries, influencing our lives, and posing unprecedented challenges. 

Within clinical trials, rapidly evolving technologies are enabling researchers to capture new data points as trials grow more complex. However, this poses new challenges in managing clinical trial data. 

In 2020, the Tufts Centers for Drug Development (CSDD) released a report highlighting the time spent in clinical trials processing disparate data.  

Diverse data sources are on the rise in clinical trials

The clinical trial landscape is experiencing a data revolution. 

According to the Tufts 2020 report, over two-thirds of sponsors are now using or piloting at least four different data sources in their clinical trials, with almost 14% using six. This uptick comes from a diverse pool of data sources, such as eConsent (electronic informed consent) data, eCOA data, mHealth data, smartphone data, eSource data, etc., and reflects the industry's move towards more comprehensive and nuanced data collection.

Leading the charge is non-CRF (case report form) data, with 87.3% of sponsors utilizing this source. This category includes data from specialty labs, biomarkers, and other sources not traditionally captured in standard case report forms. However, the popularity of non-CRF data comes with a caveat: it's also the most common cause of database lock delays, highlighting the complexities of managing diverse data types.

Other data sources gaining traction include:

  • Direct data capture (76.5%)
  • Devices and apps (71.4%)
  • Medical images (70.2%)
  • EHR/EMR (Electronic Health Records/Electronic Medical Records) (41.4%)
  • -Omics data (38.7%)

Interestingly, adoption rates vary by company size. Medium and large sponsors generally show higher adoption rates across these diverse data sources than smaller ones.

The challenge in managing a deluge of data

While this data expansion promises faster, more accurate decision-making during trials, it's not without its challenges. In a 2017 Tufts CSDD report, 98% of respondents reported issues with their current clinical data management systems. As Ken Getz, research associate professor and director at the Tufts Center for the Study of Drug Development, put it:

"Clinical teams are being forced to step out of their comfort zone to manage, integrate, and analyze data from more diverse and less compatible sources, including smartphone and wearable devices, real-world evidence, and social media."

Managing clinical trial data involves various tasks, each with its own challenges. The Tufts CSDD report reveals that while the most time-consuming task is finding, selecting, and implementing a new data vendor, sponsors and CROs spend large amounts of time: 

  • Managing the load/publish process,
  • transforming and mapping data,
  • performing data review and cleaning, and
  • analyzing data.

Notably, small sponsors are more than twice as likely as large sponsors to report transforming and mapping data as extremely time-consuming, indicating that larger organizations may have more robust systems or resources in place for these tasks.

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Charting a path forward requires plans and a platform

Simply put, companies are already preparing or need to quickly prepare plans and technologies that enable them to collect, integrate, and analyze real-world patient data from various sources to stay competitive in the coming decade.

Accordingly, the study found that CROs and companies with higher trial volumes (more than 15 per year) are leading the charge in adopting new data sources. As we move forward, the success of clinical trials will increasingly depend on our ability to harness the power of diverse data sources. 

Within the last decade, clinical trial platforms like Medable’s Evidence Generation platform have emerged as powerful tools for handling the complex data landscape of modern clinical research. Their ability to process, display, and analyze large amounts of data from multiple sources makes them indispensable in today's data-driven clinical trials. 

Centralized data collection and integration

Clinical trial platforms serve as centralized hubs for data collection and management. This centralization is crucial for several reasons:

  • Single source of truth: These platforms create a single, authoritative source of information for the entire trial by integrating data from various sources (EDC, eConsent, eCOA, IRTs, real-world data, laboratories).
  • Elimination of data silos: Traditionally, different systems might store data separately, leading to inefficiencies and potential inconsistencies. Clinical trial platforms break down these silos, ensuring all stakeholders have access to the same, up-to-date information.
  • Streamlined data flow: These platforms can automate collecting and integrating data from multiple sources, reducing manual effort and the potential for human error.

Standardization and normalization

The ability to standardize and normalize data is a crucial strength of clinical trial platforms:

  • Data harmonization: They can convert data from various formats and structures into a consistent, standardized form. This is crucial when dealing with data from different systems that may use different terminologies or coding systems.
  • Data mapping: Most platforms can automatically map data from various sources into a standardized format, like JSON, reducing manual effort and potential for human error.
  • Data quality control: These platforms can automatically flag inconsistencies or errors in the data through standardization processes, improving overall data quality.

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Advanced analytics and visualization

Modern clinical trial platforms come equipped with sophisticated tools for data analysis and visualization:

  • Real-time analytics: They can process incoming data in real-time, allowing for immediate insights and rapid decision-making.
  • Machine learning integration: Many platforms now incorporate machine learning algorithms for predictive analytics, pattern recognition, and anomaly detection.
  • Interactive dashboards: These platforms often feature customizable dashboards that allow users to visualize complex data sets in intuitive, graphical formats.
  • Statistical analysis tools: Built-in statistical tools enable researchers to perform complex analyses without exporting data to external software.

Scalability and performance

Clinical trial platforms are designed to handle large volumes of data efficiently:

  • Cloud-based infrastructure: Many modern platforms utilize cloud computing, allowing for easy scaling of resources as data volumes grow.
  • Distributed processing: Some platforms use distributed computing techniques to quickly and efficiently process large datasets.
  • Data compression and archiving: Advanced data management techniques ensure that performance remains high even as data volumes grow.

Regulatory compliance and data security

These platforms are built with regulatory requirements in mind:

  • Audit trails: They maintain detailed audit trails of all data changes and user actions, crucial for regulatory compliance.
  • Data privacy features: Built-in features for data anonymization and access control help ensure compliance with data protection regulations like GDPR and HIPAA.
  • Electronic signatures: Support for 21 CFR Part 11 compliant electronic signatures is often included.

Collaboration and workflow management

Clinical trial platforms facilitate collaboration among diverse stakeholders:

  • Role-based access control: They can provide appropriate access to different user roles (e.g., investigators, monitors, data managers), ensuring data security while promoting collaboration.
  • Workflow automation: Many platforms include tools for automating clinical trial workflows, from patient recruitment to data review and report generation.
  • Communication tools: Built-in messaging and notification systems inform all stakeholders of important updates or required actions.

Interoperability

Modern clinical trial platforms emphasize interoperability:

  • API integration: They often provide APIs, allowing seamless integration with other systems and tools used in clinical research. Medable’s clinical trial platform is one such platform that can accommodate existing clinical trial data from any other system or tool.
  • Support for data standards: Many platforms support clinical data standards like CDISC, facilitating data exchange and submission to regulatory authorities.

As trials evolve, data management will be key

As the clinical trial landscape continues to evolve, embracing diverse data sources and advanced technologies is no longer optional—it's imperative. 

As we look to the future, clinical trial success will increasingly depend on our ability to harness the power of diverse data sources effectively. Companies that invest in comprehensive data management strategies and leverage advanced clinical trial platforms will be well-positioned to navigate the complexities of modern clinical research, ultimately accelerating the development of life-changing therapies.

The challenges posed by disparate data systems are significant, but they're not insurmountable. Clinical trial platforms like Medable's offer an easy enterprise-wide solution, providing centralized data integration, standardization, advanced analytics, and robust security features.

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