A major challenge for emergency rooms is managing patient flow and reducing wait times.
Long wait times can lead to patient dissatisfaction and even adverse health outcomes. Current queuing systems often fail to account for the complexity of patient needs and the dynamic nature of patient symptoms.
Staff are often overwhelmed with the volume of patients and struggle to prioritize care effectively.
This project helps users understand and guide them along the process of optimizing emergency room operations.
The system analyzes patient flow, wait times, and staff workload to provide actionable insights.
Our system collects data persistently and synthesizes information in an interactive and easy-to-grok format for users.
The structured and specific data can greatly benefit hospital administrators and staff.
Demo
Key Features
Real-Time Analytics: Provides real-time insights into patient flow and wait times
Multiple Screens: Supports multiple screens for different user roles
Algorithmic Prioritization: Combines voice diagnosis, patient data, and patient context to determine an accurate priority in queue
Full Stack Application: Built from scratch during a hackathon timeline
Technologies Used
Next.js: For server-side rendering
React: For building UI components
Tailwind CSS: For utility-first styling
WavLM: Train dataset on specific respiratory illnesses using WavLM
Random Forest: Uses a random forest algorithm to predict patient outcome based on dataset
Bridge2AI Dataset: Uses a voice dataset labeled with different biomarkers and patient symptoms to predict health outcomes