Clinical trials have evolved. In the past, they mainly depended on real-world data gathered directly by researchers. Nowadays, external data sources such as electronic health records (EHRs), wearable devices, and patient-reported outcomes play an important role. These sources provide opportunities to improve accuracy, health outcomes, and the pace of drug development.

But here’s the catch: working with external data can be messy, unpredictable, and frustrating. From integrating different data formats to helping compliance with regulations, the hurdles can feel endless.  

Let’s examine five key challenges in managing external data sources in clinical trial design and why it’s essential to address them.

1. Data Integration Issues 

External data doesn’t just show up neatly packaged and ready to use. It comes from different systems with quirks, formats, and standards. One hospital might use a particular EHR system, while another uses something completely different. Then, data from wearable devices, which might record activity levels in one format and heart rate in another, will be thrown in. The lack of uniformity makes combining all these pieces an enormous task. 

And it’s not just about the format. The standards—or lack thereof—can be a nightmare. Imagine merging data when one system records weight in pounds while another uses kilograms. Or worse, when data is missing entirely because one system doesn’t track specific metrics. These inconsistencies create delays, errors, and headaches for everyone involved. 

The ripple effects are significant. When data integration takes too long, it slows the entire trial process. Researchers can’t analyze incomplete or mismatched data. To mitigate against these challenges, streamlining external data management in clinical trials should be done through data transfer specifications (DTS). This helps define the data sets upfront and accelerates submission time.  

2. Data Quality and Reliability 

Now, even if you integrate all that data, there’s another question: can you trust it? External data often doesn’t go through the same rigorous quality controls as the data you collect internally. That means errors, inconsistencies, and even outright inaccuracies can slip through. 

Missing or incomplete data is another major issue. Let’s say you’re using external data from a wearable device. Maybe the patient forgot to wear it for a few days, or the device malfunctioned. Suddenly, there’s a gap in the data. And if those gaps aren’t identified and addressed, they may skew the results of your trial. 

There are ways to improve data quality and reliability. Cleaning the data—such as removing duplicates, filling in gaps, and standardizing formats—can be a helpful starting point. Validation protocols can also help with the data meet specific standards before use. However, managing external data quality remains an ongoing task that requires ongoing attention.

3. Regulatory and Compliance Concerns

If you’ve ever dealt with regulations in clinical study designs, you know they’re no joke. Different countries have regulatory bodies and rules about collecting, storing, and sharing data. For example, the U.S.’s HIPAA compliance focuses on health information and privacy. Keeping up with these regulations is a full-time job in itself. 

And it’s not just about following the rules. It’s about proving you’re following them. Regulatory agencies expect detailed documentation showing how external data was collected, stored, and used. If there’s even a hint of non-compliance, you could face audits, fines, or—worst of all—have your trial results invalidated. 

Given the issue’s importance, many organizations invest in compliance teams or collaborate with external experts. Even with proper support, managing regulatory and compliance concerns can be complex. It requires ongoing attention, communication, and a solid understanding of the relevant rules.

4. Data Privacy and Security Risks 

When working with external data, you often handle sensitive patient information. And with that comes a huge responsibility to protect it. Unfortunately, external data systems aren’t always as secure as hoped. They might lack encryption, have outdated security measures, or be vulnerable to cyberattacks. 

Then there’s the issue of patient confidentiality. Even if the data is anonymized, re-identification is always risky. Consider combining multiple datasets—what if someone can piece together enough information to identify a patient? It’s a scary thought and a serious concern in clinical trials. 

One approach is to begin with secure clinical trial protocols, such as encryption and access controls. Another essential step is anonymizing data to remove any information that could identify a patient. Regular security audits can also help identify and address vulnerabilities. While this may not be a perfect system, it can be a helpful starting point.

5. Logistical and Operational Challenges 

Managing external data often means coordinating with multiple vendors, processes, and systems. This is a significant logistics challenge that may affect the success of a clinical trial. Miscommunication, delays, and inconsistencies are all too common. 

Data transfer is another sticking point. External data doesn’t always arrive when you need it. Maybe a vendor is slow to process the data, or a technical issue causes delays. Whatever the reason, late data can throw off the timeline for your entire trial. 

And let’s not forget about resources. Managing external data requires skilled personnel, advanced technology, and plenty of time. Not every organization has those resources readily available. As a result, they’re forced to stretch their teams thin or outsource the work—neither of which is ideal. 

Conclusion 

Managing external data in clinical trials can be challenging. There are various hurdles, such as integration, quality concerns, and regulatory or logistical issues. Addressing these challenges is essential for making the most of external data. These obstacles could become opportunities for more efficient and reliable clinical trials with the right tools and strategies.

Disclaimer: This article is for informational purposes only and does not constitute medical, legal, or regulatory advice. The challenges outlined in managing external data sources in clinical trials are based on general industry trends and may vary depending on specific trial circumstances. Readers are advised to consult with clinical research experts, regulatory authorities, or legal professionals for guidance related to their specific situations.

Published by Mark V.