Have you ever wondered how data verification has changed in clinical trials? If you work in clinical research, you’ve probably noticed that we’ve moved from verifying every single piece of data (100% SDV) to smarter and more efficient methods. In this article, we’ll look at how Targeted SDV (Source Data Verification) has evolved as part of risk-based monitoring.

The Beginning: Why Did We Need a Change?

Not so long ago, it was normal in clinical trials to verify 100% of the data in the CRF (Case Report Form) against the source documents. This meant that clinical monitors spent many hours at research sites, checking every single value, date, and number—everything!

This way of working had several problems:

  • It used a lot of resources (time and money)
  • It caused big delays in the trials
  • It didn’t always improve the quality of critical data

The Turning Point: When Targeted SDV Started to Shine

The real change started around 2011–2013, when regulatory agencies began promoting more efficient approaches. The FDA released its guide "Oversight of Clinical Investigations—A Risk-Based Approach" in 2013, and the EMA published its “Reflection paper on risk-based quality management in clinical trials” that same year.

At the same time, TransCelerate BioPharma published a key document in 2013: "Risk-Based Monitoring Methodology". This suggested focusing monitoring efforts where they matter most—based on risk and data importance.

That’s when Targeted SDV became not just an idea, but a real need for the industry.

Targeted SDV Strategies: From Simple to Advanced

Today, there are different ways to apply Targeted SDV, each with its own benefits. Let’s look at them:

Strategy 1: Selecting Specific Fields

The simplest strategy is to define which fields will be verified. For example:

  • Data important for primary study goals: Primary endpoints, inclusion/exclusion criteria, serious adverse events
  • Patient safety data: Critical vital signs, key lab results
  • Drug dosing info: Dose amounts, administration dates

This strategy is easy to apply and easy for everyone to understand. For example, in ShareCRF, you can mark these fields for verification while leaving out less important ones.

Strategy 2: Adapting by Patient

A more advanced strategy is to change the SDV level depending on each patient’s risk. Not all patients need the same level of checking.

For example:

  • High-risk patients: May need 100% SDV
  • Medium-risk patients: May need SDV only for critical data
  • Low-risk patients: May only need sample checks

What can define a patient’s risk level?

  • Protocol deviation history
  • Experience of the research site
  • Complexity of the clinical case
  • Out-of-range or unusual results

This strategy helps focus resources where they’re really needed. ShareCRF lets you create different strategies and assign them to each patient.

Strategy 3: Hybrid and Dynamic Approach

The most advanced strategy mixes the others and adds a dynamic part that changes during the study:

  • Start with a basic SDV level for all patients
  • Adjust it based on early findings
  • Add extra checks when problems are found
  • Lower the level where data quality is consistently high

This needs more advanced systems, like those in ShareCRF, where the monitoring strategy can change in real time based on new data.

Benefits of Well-Implemented Targeted SDV

When Targeted SDV is done right, there are many benefits:

  • Lower costs: Up to 30% savings in monitoring expenses
  • Faster progress: Less time spent on unnecessary checks
  • Better focus on important data: More attention to what really matters
  • Earlier problem detection: Because efforts are focused on risky areas
  • Happier teams: Monitors can do more valuable tasks

Bringing Targeted SDV into Your Organization

If you're planning to start or improve Targeted SDV in your studies, here are some practical tips:

  1. Review your protocol: Identify critical vs. non-critical data
  2. Choose your approach: Decide which of the strategies fits your study best
  3. Train your team: Make sure everyone understands the method and the reasons
  4. Use the right tools: Platforms like ShareCRF can make implementation easier
  5. Monitor and improve: Check if your strategy works, and adjust if needed

Conclusion

Targeted SDV as part of risk-based monitoring is one of the biggest improvements in clinical trial management in recent decades. We've moved from "check everything" to "check what matters".

At ShareCRF, we understand how important this shift is. That’s why we’ve built tools to help you apply Targeted SDV strategies—from the simplest to the most advanced.

The key is finding the right balance: enough verification to protect critical data, but not so much that it wastes resources.

What about you? What Targeted SDV strategy are you using in your studies? We’d love to hear your experience.

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