The problem
When patient documentation is unstructured and stored inconsistently, medical professionals spend significant time on documentation itself, and even more time piecing together patient history before follow-up visits. There's no easy way to surface what was prescribed, ordered, or recommended in a prior visit without manually reading through records.
Solution
- Audio and text ingestion with a multi-agent extraction layer identifying medications, orders, treatment plans, symptoms, and clinical observations.
- Timestamped structured data stored in a secured relational database with vectorized note content for semantic search
- Hybrid retrieval combining keyword and semantic search across the full clinical record
- Conversational chatbot interface allowing providers to query patient history and auto-generate visit summaries at follow-up
How it works
A multi-agent AI platform that ingests provider notes, whether typed or dictated via audio with patient consent, and automatically extracts structured clinical information including medications, lab and imaging orders, treatment plans, symptoms, and additional observations, each tagged with a timestamp. Extracted data is stored in a secured relational database while note content is vectorized for semantic search. Providers interact with the system through a conversational chatbot interface, allowing them to ask questions about a patient's history, retrieve details from a specific visit, or generate a concise summary of prior visits automatically surfaced at the start of each follow-up appointment.
The result
Providers arrived at every follow-up visit with a clear, AI-generated summary of the patient's history, outstanding orders, and prior treatment plans, reducing documentation burden, eliminating manual chart review, and allowing more time and attention to be spent on the patient.