Improved Data Quality & Integrity for Faster Regulatory Approvals

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June 1, 2016

Research shows that data errors—missed adverse events, data anomalies and procedural deviations— can lead to costly study delays. Data errors are harder to spot as trials become more complex, which means standard practices for evaluating protocols and clinical data need to improve.

Data analytics have driven radical improvements in industries from shipping to finance, and these powerful tools can now be  tailored to address the complexity of clinical research.

Life science companies have begun using statistical algorithms and predictive analytics to mine and evaluate the quality of clinical and lab data alike. These tools automatically identify anomalies, outliers, potential fraud or misconduct and procedural issues.

But what does the process look like and how does it work?

Download this white paper and learn how data analytics:

  • Improves data quality in clinical trials in contrast to standard practices
  • Surfaces actionable insight on site performance and data quality
  • Protects a blockbuster from avoidable failure

Leveraging big data analytics in clinical trials

Medidata CSA

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