Clinical Trial Basics: Source Data Verification (SDV)

Source Data Verification (SDV) is a method that clinical research organizations (CROs) can use to ensure the data they’re gathering is accurate.

Source Data Verification (SDV) is a method that clinical research organizations (CROs) can use to ensure the data they’re gathering is accurate. It involves comparing a study’s source data against its final report, enabling for the identification of any errors before they become problematic or get published. In this article of MILO, let’s delve into SDV and understand how this method can enhance the experience in clinical trials.

What is SDV and What’s Its Purpose?

  • SDV is a process used to verify source data in clinical trials.
  • Source data is the first element that makes up a study, which can include:
  • Clinical trial documents and protocols
  • Investigator brochures and other promotional material
  • Study forms, questionnaires, and other participant documents

Moreover, SDV helps you verify the accuracy of your clinical trial data. In Sum, SDV is paramount to starting the study, though it also can be performed at any time throughout the clinical. The purpose of SDV is to:

Ensure that all source documents have been collected and are available for review

Verify that each record in a source document has been accurately transcribed onto your electronic case report form (eCRF) or your patient registry system

Determine if there are any discrepancies between individual sources of information (e.g., patient medical records versus pharmacy dispense records), and resolve them as needed

Advantages of SDV

Source data verification is a crucial tool for ensuring the quality of your clinical trial data. By verifying the source documents, you can uncover flaws and mistakes that might otherwise go unnoticed until after your reports are submitted. Some of many benefits of SDV include:

Providing Data Reliability – Verifying the source documents offers a more thorough understanding of how each piece of information was collected and recorded, which provides more confidence in the reliability of those results. This helps to prevent future mistakes from creeping into your study design or analysis plan by revealing potential problems with inputting incorrect patient identifiers or inaccurate timing protocols.

Ensuring Data Accuracy – Because SDV involves examining original documents, it’s possible to catch errors that may have been missed during manual transcription processes (e.g., if there are inconsistent formats between different types of text fields).

Allowing Consistency – With many studies involving multiple sites across several regions, states, or countries, it can be challenging for one person to keep track without some form of checking system in place—which is precisely what SDV offers. It ensures everything is checked to ensure it all matches up correctly before submission, reducing the odds discrepancies slip through undetected. This also helps build trust between researchers.

What are some alternatives to SDV?

If you’re planning on doing SDV, there are a few things to keep in mind. First of all, it is the most reliable way to make sure that your data is accurate and reliable. Second, if your company doesn’t already have an SDV program in place, it can be a good investment. Finally, if you think that this kind of verification isn’t necessary for your trial or study, that isn’t typically the case.

SDV isn´t the only option. In some cases, trial oversight committees may be a functional alternative. Risk-based monitoring is also a potential solution. You may also want to explore hybrid approaches to clinical trials monitoring, as these can potentially enable you to integrate benefits from different methodologies or avoid specific drawbacks associated with various strategies.

What are the limitations of SDV?

In clinical trials, SDV intent is to make sure that the data entered is accurate and valid. It is time-consuming and expensive, but it’s only as good as the data you enter into it. If there are errors in your source documents, then those errors will be replicated when you submit them for SDV.

Fortunately for all parties involved, not all studies require source data verification. For instance, if a study has already been completed successfully or if there are no regulatory requirements related to your clinical trial, then SDV may not be necessary.

Using SDV in your clinical trials can help uncover mistakes and preserve data is reliable

Source Data Verification is a pivotal step in the clinical trial process. SDV is used to guarantee that the source data is accurate and complete as it is collected, stored, shared, and analyzed. It helps ensure that your data will be reliable and can be depended upon for decision-making during your clinical trials.

If You Are Planning a Clinical Trial, Meet MILO Platform

Integrated solutions encompassing EDC, eCOA/ePRO, eCRF, and eConsent for non-interventional studies, Real-World Evidence (RWE), and successful launches. Enhance communication with healthcare professionals by providing real-time clinical data insights into study progression.

Our Milo ePRO integration automatically provides the necessary audit trail traceability for compliance with clinical trial regulations. Mandatory fields, edit checks, input-dependent paths, and automated validations further enhance the quality of data, ensuring accuracy and reliability.

Contact us today and schedule a demo, our team will be thrilled to help you.

Final Insights

SDV can support you to build a superior clinical trial and get the results you need. It’s paramount to know that SDV is only one of many tools in your toolbox. If you’re still searching what kind of data verification strategy will work best for your project, explore all of the available options, allowing you to select the ideal path while preserving regulatory compliance.