AN ML model assists global medical affairs international services team
The global medical affairs International services team (GMAIST) of an F100 global pharma uses an ML model to improve the quality and consistency of the promotional material review process
MQuest
The client’s global medical affairs international services team (GMAIST) used a using a global content management and approval (GCMA) system to manage the review and approval process of promotional materials based on the various parameters.
- The earlier process was a manual one that used human judgment to compare promotional claims to references in medical literature for consistency
- This consisted of 600-3800 transactions annually and was time-consuming.
The new solution would need to automate parts of the process for greater efficiency and quality.
MSolve
MResult developed an ML model to assist the GMAIS team with the review process.
- The model examined the promotional claims against the reference material and suggested if the claim-reference pair corresponded or not
- These suggestions were then provided to the reviewers to make their work easier, faster, and of better quality.
MPact
The suggestions provided by the ML model helped the GMAIS team:
- Save up to 8K-32K hours over 3 years, depending upon demand
- Improved consistency and quality
- Reduce time spent on manual tasks, enabling them to engage in higher value-add activities and more meaningful work.
