Layer Well being, an organization spun out of MIT, says its first product, Distill, makes use of synthetic intelligence to rapidly carry out any scientific, administrative, or analysis process that requires chart overview from unstructured information. Use circumstances embrace registry submissions, high quality measurement, curation of real-world proof, scientific doc enchancment, and income cycle administration. 

The startup is backed by $4 million in funding from GV (Google Ventures), Basic Catalyst, and Inception Well being, and amongst its beta clients is Froedtert & the Medical Faculty of Wisconsin well being community, which is utilizing Distill to assist high quality enchancment efforts. The platform is being utilized by the group’s nurse abstraction staff throughout chart overview, serving to them extra successfully discover and submit information to scientific registries. 

Layer Well being says that Distill integrates into current merchandise and workflows, ingesting scientific notes and analyzing them at scale. “Behind the scenes, Layer Well being’s machine studying (ML) algorithms leverage the ability of huge language fashions (LLMs) to ship correct outcomes with out the necessity for labeled information, decreasing improvement time from months to as little as a day. Distill additionally learns and adapts from buyer interactions, creating extremely environment friendly customer-specific fashions fine-tuned for particular use circumstances,” the corporate says. 

One other person the corporate cites is xCures, a well being expertise firm that’s utilizing Distill to arrange and construction well being information for extra exact most cancers therapy suggestions and environment friendly scientific trial matching. Utilizing Layer’s language fashions skilled and validated on xCures’ distinctive information, the corporate can extra precisely extract intricate and nuanced particulars from affected person medical information.

Layer Well being’s CEO is David Sontag, Ph.D., an MIT professor with greater than 100 revealed papers in AI and machine studying.