eIQ Review Overview

Transcript

With the average clinical trial now generating over 3 million data points, even the most comprehensive data review plans leave room for issues to be missed, leading to costly and time consuming errors. eIQ Review, part of Data Central in the elluminate Clinical Data Cloud, provides AI-enabled data review capabilities to ensure data integrity in a more efficient, scalable way.

There are several models available within eIQ Review. Central Statistical Monitoring, which detects patient level anomalies from clinical trial data. Anomalies shift in labs to predict data discrepancies that have an atypical shift in lab values. Anomalous adverse event duration to predict if reported AEs have an anomalous duration. Univariate outlier detection and incorrect item type to identify atypical values and incorrect item types across several domains.

Across all models, reviewers get a list of anomalous subjects or atypical values and can drill down to underlying data to visualize, better understand, and ask further questions about the data before taking review actions, all without leaving the eIQ panel. Detection of anomalous data is difficult and time consuming. Therefore, detection of data discrepancies using AI and ML helps clinical data review teams, medical directors and medical monitors identify data quality issues in a more efficient manner.

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