Leading Healthcare Technology Company

Fresh Gravity was engaged to improve the accuracy of case intake processes. Case intake is the first step in the Pharmacovigilance process that involves reading, analyzing, and aggregating data from multiple sources to collate adverse events reported.

Problem

The client required an automated and intelligent way of extracting relevant data from multiple data sources. Since the existing solutions in the market lacked the ability to understand entity context, the client’s Pharmacovigilance teams spent a significant amount of time tracking, aggregating and analyzing the incoming data.

Solution

Fresh Gravity improved upon the traditional methods for Named Entity Recognition. Machine Learning was used to gather context for tasks such as structured extraction to grasp new words found in medical language and codes.

Impact

Fresh Gravity developed deep learning solutions for the client to help derive context from the sequence of the words to correctly distinguish “Cancer” from “Cancer Institute”. This helped in increasing the speed of the case intake process and reduced the manual effort needed to perform data entry tasks.