This major global software vendor needed help annotating patient and clinician conversations to improve the performance of its digital scribe.

It’s tough to find medical conversations we can actually use. iMerit proposed a very unique solution to our problem.

– Head of AI Solutions 



This major software company needed to improve its model’s ability to establish boundaries between speakers, summarize conversations, and improve clinical data gathering.


iMerit experts generated large-scale semantic taxonomies using Ango Hub. Parameters were tuned using the Unified Medical Language System, with phrases such as “I am feeling well” resulting in “I” mapped to “patient”, “feeling” mapped to “emotions”, and “fine” mapped to “qualitative concept”, for example.


After implementing the training datasets into the model, quality checks comparing performance before and after fine-tuning indicated a significant +37% improvement to speaker identification, which resulted in a substantial interpretive accuracy metric of 98%.





 Interpretive Accuracy


Speaker Identification


Patient-Facing Time