Data Analytics to Support Fraud Detection
Dec 04
10:00 A.M. -
10:45 A.M.
The success of any clinical trial depends on the accuracy and integrity of the study process and data produced from the trial. Detecting inadvertent errors and fraudulent data is paramount at every step.

Omission of critical data, reporting of only positive outcomes and none of the adverse outcomes, etc. are some instances which could have dire consequences on the overall study. Data analytics techniques have a significant role to play in the early warning, detection and monitoring of fraud.

Key Learnings from the Session

  • Understanding the impact of fraud in the data life cycle
  • Detecting fraudulent data – how clinical data visualization matters
  • Identifying data patterns to analyse fabrication, employing various datamining techniques to uncover potential data anomalies and challenges faced
  • Building quality into clinical trials – a regulatory perspective

Speaker Profile

Sara Vaidya

Clinical Data Management

A biotechnology post-graduate by education with diploma in business management, Sara has about 6 years of industry experience in clinical data management. She has worked in several therapeutic areas like Hematology, Neurology, Immunology, Cardiovascular, Oncology, etc. and has an experience of working on multiple studies, on various databases such as Oracle Clinical and EDC (Electronic Data Capture) such as Rave and Inform. She has managed projects in different capacities for global as well as local clients.

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