Mitigate Risks with AI Capability

One of the major challenges of managing clinical trials is collecting and applying relevant and important performance metrics. These Key Performance Indicators (KPIs) are used to monitor quality and efficiency of the processes.

There are many operational parameters that need monitoring, a few of them being:

  • First and last site activated
  • First and last patient recruited
  • Study planned budget
  • Study forecasted spend
  • Study actual expense

In order to monitor such KPIs effectively, it is necessary to synchronize data across all the different systems that are used to record data from various sites. A typical clinical trial has multiple sites across the globe and many different types of users access different systems to record, edit, and update data at all times. To get an accurate view of how a particular metric is performing, it is critical to have real-time access to all data sets. An AI-enabled platform like LSAC can bring in data from all disparate sources, standardize it, and make it available in a harmonized data lake.

A typical clinical trial monitoring/management system compares the plan with the actual status of the trial and indicates any discrepancies. It does not have the sophistication of anticipating issues that may come up while the trial is progressing. Nor does it leverage any learnings from similar trials in the past to help with forecasting of probabilities of achieving KPIs in the future. LSAC leverages AI with pre-trained algorithms that can predict the status of operational and financial KPIs allowing steps for mitigation to be implemented if necessary.

In the absence of AI-powered solutions, a team of analysts would be required to manually do the analysis and apply different algorithms to come up with the answer. These resources are, therefore, saved and can be utilized elsewhere for more significant tasks. The predictions make it simpler to identify the underlying operational or financial process that may be at-risk or lagging behind. Based on the analysis, the right course of action can be determined to ensure that the clinical trial is a success and that it stays within budget and timelines.

Find out more about this LSAC functionality.

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About Amit Gulwadi

mmAmit executes Saama’s clinical analytics innovation roadmap by identifying and integrating emerging concepts across the clinical research value chain, fostering and strengthening clinical ecosystem relationships, and communicating Saama’s strategy to key stakeholders.

Charged with helping Saama positively impact the clinical research value chain by improving the pharmaceutical industry’s existing clinical analytics ecosystem, Amit collaborates closely with Saama’s Board of Advisors, product management and marketing teams, customers and partners, including CROs, healthcare-technology startups and data providers. Amit is leading the development of a clinical innovation ROI model to differentiate Saama’s competitive value to customers.


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