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L O A D I N G

Delivering
Faster, Smarter, Safer
Emergency Room Care

AI-powered platform for personalized patient and clinician support
Our Features

Experience Our Platform
EmerGenAI

Virtual Chat Assistant
Further contextualize each patient's condition to maximize nurse triage time
01
Data Collection
Collect patient data on social determinants of health, mental health, low acuity risk, and other screening factors during wait time
02
Medication Extraction
Read in patient medication information using image recognition
03
Social Services Matching
Match patients to food, housing, and transportation services based each patient's specific needs and location
04
EHR Integration
Seamless electronic health record and workflow integration, minimizing risk to hospitals
05
About us

Our Solution to a Growing Health Crisis in U.S. Emergency Departments

An innovative, companion AI-powered platform that streamlines patient intake and provides personalized decision support in crowded hospital waiting room settings. The product addresses numerous inefficiencies across the triage process, including the information gaps in triage that lead to mistriage and misalignment of hospital resources, nursing shortages, time and cost inefficiencies in waiting rooms.

Our solution aims to increase clinical capacity through mitigation of nursing burden as well as improved clinical outcomes for patients through reductions in mistriage rates.

End-to-End

EmerGenAI Across the ER Care Journey

  • Onboarding
  • Triage
  • Wait Times
  • Discharge Planning
How it works

EmerGenAI Leverages a
Mixture of Agents Approach
Specific to Emergency
Care Needs



step 01
PATIENT CARE AI
Utilizes large-language model agents directed at triage, discharge, and follow-up to collect key data through questioning and EHR retrieval



step 02
CLINICAL DECISION SUPPORT AI
Generates output to streamline clinical practice, in the form of patient summaries, ESI score evaluations, and hospital specific protocol guidance



step 03
SUPERVISOR AI
Proprietary safeguards and standardization methods for LLMs to ensure bias and discrimination control and content moderation



Affiliations