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Data-Centric Approaches to Streamline the Clinical Workload

In the context of clinical trials, reducing the workload of the clinical team without compromising data quality is imperative for efficiency and cost-effectiveness. One key area that demands attention is the approach to Source Data Verification (SDV) and data review. Typically, the industry relies on 100% SDV, a resource-intensive quality assurance method that often leads to higher costs and staff turnover without necessarily improving data quality. A well-known analysis by TransCelerate1 has revealed that only around 3.7% of clinical trial data changes after its initial entry, and even fewer changes occur because of data review and SDV. It begs the question: why is 100% SDV still so prevalent?

We explore two data-centric methodologies, both supported by regulatory guidelines from authoritative bodies like the FDA2 and EMA:3

  1. Targeted Source Data Verification (TSDV)
  2. Centralized Statistical Monitoring (CSM)

Targeted Source Data Verification

Targeted Source Data Verification (TSDV) reduces the clinical team's workload by incorporating effective feedback loops to enhance its effectiveness. Many Electronic Data Capture (EDC) solutions have built-in support for TSDV, facilitating a more targeted and strategic approach to data verification. It allows SDV efforts to concentrate on critical aspects essential for trial integrity, including informed consent, adherence to inclusion/exclusion criteria, study endpoints, and adverse events or serious adverse events.

The system-level support options offered by EDC providers vary, but most offer solutions that reduce the workload of clinical teams. These solutions can modify the level of SDV depending on factors such as the volume of edits generated or the patient population at specific sites. Moreover, EDC platforms equipped with built-in TSDV capabilities provide reports that furnish a feedback loop to understand why a change in the initial TSDV rate is necessary. By leveraging this feedback, clinical teams can continuously refine their data verification processes, achieving a more efficient and targeted approach to quality assurance.


Centralized Statistical Monitoring

To maintain data coherence for individual patients, we should examine the data from different perspectives, including field and form edit checks, cross-page checks, and leveraging Centralized Statistical Monitoring (CSM). Similar to how clinical data is analyzed using p-values to demonstrate efficacy, CSM employs statistical methods to identify data issues. These include:

  1. Integrating all clinical data to detect anomalies.

  2. Comparing data tendencies by assessing between-patient variability, grouping data by sites, comparing them with other sites, examining means, variances, outliers, and missing values, and calculating p-values to highlight potential concerns.

  3. Comparing data trends by examining within-patient variabilities across visits.

  4. Using the results of these comparisons to generate an inconsistency score to identify areas that require a manual review.


Realizing savings and quality Improvements

The adoption of data-centric methodologies such as TSDV and CSM not only alleviates the workload of clinical teams but also translates into substantial savings and quality enhancements throughout the lifecycle of a clinical trial. Let’s delve into how various stakeholders benefit from these approaches:

Data management (DM) teams:

    • Targeted DM review allows for addressing critical issues efficiently.
    • Fewer data discrepancies requiring resolution result in a decrease in "Ok as is" responses.
    • Additional review ensures that the patient data tells a consistent story, thus improving overall data quality.


    • Greater focus on primary endpoints, expanding as necessary based on initial findings.
    • Fewer queries that result in “Ok as is” responses.
    • Less time is required on-site.


    • Fewer queries to review and respond to, streamlining the operational workflow.
    • Less time is required to supply space and documentation to support on-site monitoring visits.


    • Improved ability to detect and rectify issues or fraud and take early action to close sites.
    • Shorter database lock timelines resulting from fewer unresolved issues at the end of the study.


Final takeaways

Data-centric strategies such as Targeted Source Data Verification and Centralized Statistical Monitoring present a transformative opportunity to streamline the workload of clinical teams. These methodologies enhance operational efficiencies, improve data quality, deliver substantial cost savings, and expedite study timelines. Evaluating and quantifying the savings generated by these approaches can facilitate a comprehensive ROI analysis, promoting acceptance of these tools by the study teams and integration into your processes.



William Baker_cropAbout William Baker

William Baker is Global Head of Data Management at Cytel. He has over 25 years of experience in clinical drug development, spanning data collection, data review, SAS programming, and NDA submissions. This has included leading global project teams to select, pilot, and implement a new EDC system, document publishing tool, and data visualization tool. His mission is to deliver high-quality data and insights that support the advancement of innovative therapies and improve patient outcomes. He leverages his expertise in data visualizations, data management practices, standards, and process development to optimize the performance and efficiency of data collection and data review to ensure patient’s data tells a consistent story.



1 Sheetz, N., Wilson, B., Benedict, J., et al. (2014). Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials. Therapeutic Innovation & Regulatory Science, 48(6).

2 U.S. Food and Drug Administration. (2013). Oversight of clinical investigations — A risk-based approach to monitoring.

3 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. (2016). Integrated addendum to ICH E6(R1): Guideline for good clinical practice E6(R2).

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