Our team understood the client’s requirements and divided the project into three phases, solving one problem in each phase.

Phase 1

• The team tackled the manual work challenge by setting up an automated email notification system. This will automatically shoot emails to the SMEs about an SQE event. The email also had a summary table containing site information such as what happened, what needs to be done, and the event’s effect on the study- all the information populated from the raw clinical data.
• To help the SMEs skip the tedious process of going through the huge chunk of data, the team loaded a list of keywords on SharePoint and highlighted them if present in the event summary within the email.

Phase 2

• Our team set up a system to analyze and find trends and patterns within the data to understand the reason behind the occurrence of various SQEs’.
• The team also built a predictive analytics dashboard which helped the client to identify the sites and protocols where SQEs occurred previously. The information helped in streamlining the training sessions for the clinical site team. This not only reduced the recurrence of SQEs, but also reduced the number of retraining sessions taken per month.

Phase 3

• The team built an ML algorithm to predict the probability of SQEs occurring in a new or existing protocol design. If the probability is yes, then the algorithm is also trained to search historical data and present previously occurred SQEs for similar protocol. This- helped the clinical trials team to increase the success rate of the protocol designs.