Building the next generation Clinical-AI Engine to optimise the Diagnose -> Treat -> Fund pathway for hospitals

Problem:

We have identified two intricately connected problems: how to improve hospital revenues and how to build an AI diagnostics engine that helps clinicians. Hospital earnings worldwide depend on a manually intensive back-office process (clinical coding) requiring specially trained staff to identify patient diagnosis & treatments and compute payments. Despite being crucial for hospital funding this process is error prone. Mistakes currently cause significant revenue losses for hospitals worldwide - in Australia alone, hospitals lose over 2 billion dollars annually.  


Solution:

We improve hospital revenues by automating clinical coding. A streamlined workflow enables clinical coders to work with our AI diagnostics engine to code faster and more importantly improve hospital revenue. In the background we build a rich dataset of clinical records with specialist annotations that help improve our AI diagnostics engine. Our vision is to integrate our Clinical-AI engine into the clinical treatment pathway to help clinicians diagnose better and treat patients effectively - thereby improving healthcare. 


Founders:
Squad:
Chris Quirk
Investment Manager @ Rampersand
Matthew Tracey
Palantir Technologies: Director, Global Markets
Gill Goldsmith
Chief of Staff, 5B
Phil Lee
Director, Prosperlee
Luke Howes
Partner at Tanooki Ventures