How to Implement Operational Predictions and Why these Insights are Key to Business Success

Elvin Thalund
Director, Industry Strategy

On September 8, 2020, Oracle Health Sciences presented on the impact of machine learning at the Metrics Champion Consortium (MCC) Clinical Trial Risk and Performance Management with regards to operational aspects of starting clinical trials.

The annual summit provides the opportunity to delve into the way metrics – and the careful and critical analysis of those metrics – can help life science organizations navigate clinical trials with thoughtful effort and unprecedented success.

The presentation introduced the implementation of machine learning based on MCC study startup metrics for cycle times, how this is used for milestone prediction and how this can evolve. Clinical operations staff need to have confidence in machine learning predictive models and be able to validate the accuracy of outcomes. Machine learning will give scientific insight into which indicators have the most impact on these models, so organizations can focus on those indicators to refine their models and learn from these insights, which can ultimately drive behavioral changes (i.e., less reliance on subjective decisions) to optimize business processes.

Machine learning allows organizations to continuously improve with direct implications on timelines and associated costs of clinical trials.

What to learn more?

Download the ChromoReport Spring 2020 report on the Importance of Leading Indicators to Machine Learning Predictive Models.

Download the ChromoReport Winter 2019 report on Proactive Planning with Predictive Analytics.

Learn more about optimizing your study startup.

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