Fourteen assistants, one less hour of busywork.
A suite of AI and RPA modules across a major US hospital chain: annotation, medical NER, telemedicine, transcription, and document mining.
Clinical AI suite
What they were up against.
Clinicians spend a startling share of their day on work that is not care: annotating reports, re-keying data between systems, hunting through documents, writing up the same structured notes. For a prominent US hospital chain, that overhead was measured in hours per person per day.
The mandate was broad on purpose, not one tool but a coordinated suite that could take the redundant work out of multiple hospital departments at once.
How the approach works.
The approach treats it as a platform, not a feature. Fourteen modules, each aimed at a specific time sink, sharing common plumbing for documents, transcripts, and clinical text. Report annotator and medical NER handle the reading; a telemedicine module with medical transcription handles the talking; RPA workflows and document mining handle the moving and finding.
Everything is designed to drop into existing hospital systems and to respect the obvious sensitivities of clinical data.
What it changes.
Across 10K users, the suite cut roughly two hours of redundant work per person, per day. That is time handed back to actual care, multiplied across a hospital network.
Fourteen modules sharing one backbone meant each new department came online faster than the last.