Case Study: DeepScribe achieves up to 80% reduction in data preparation time and streamlined medical NLP labeling with Datasaur

DeepScribe Leverages NLP to Automate Patient Documentation

DeepScribe, a San Francisco HealthTech startup that automates patient documentation, needed extremely granular NLP labeling for thousands of patient–provider encounters (e.g., chief complaints with who/what/why/when subcategories) but found existing tools clunky, lacking hotkeys, search, audio-text integration and automation. In 2020 DeepScribe engaged Datasaur to manage complex text and audio labeling and to support their large taxonomy and workflow needs.

Datasaur delivered a streamlined labeling platform with searchable labels, hotkeys, split-project workflows, API-driven project automation and a proof-of-concept for Datasaur Assist to auto-label new files, backed by dedicated customer support. As a result, DeepScribe achieved faster, more accurate labeling, improved tracking and review, expedited project creation and export, and reported up to an 80% reduction in data preparation time—accelerating model training and scaling of their NLP pipeline.

DeepScribe