Last weekend, I had the opportunity to participate in HackPrinceton alongside my talented teammates Maria Pasaylo, Shuti, and Samuel. Urgent Care Coordinator is a smart tool designed to help users locate the nearest urgent care center with the shortest wait time, aiming to streamline patient distribution and improve healthcare accessibility.

❗ Problem

Urgent care centers often face unpredictable patient inflow, leading to prolonged wait times and uneven distribution of patients. This inefficiency can cause delays in care and place strain on healthcare staff, impacting the quality of service.

🏥 Solution

Our solution uses machine learning to predict patient demand at various urgent care locations, allowing for the redistribution of incoming patients to centers with lower wait times. By analyzing real-time data and predicting patient influx, our tool can direct users to the best urgent care center based on current and anticipated demand, enhancing patient experience and operational efficiency.

💻 Features

Nearest Urgent Care Finder: Finds the nearest urgent care facilities to the user’s location.

Real-Time Wait Time Estimation: Provides estimated wait times based on real-time patient data.

Smart Redistribution: Suggests alternate urgent care locations with shorter wait times to balance patient load.

Demand Prediction: Uses machine learning to forecast patient influx, allowing for dynamic adjustment of recommendations.

Urgent Care Info Cards: Displays urgent care center information on interactive cards, including name, address, and estimated wait time.

Machine Learning Model for Wait Times: Implements a predictive model to estimate wait times.

🎯 Target Market:

Individuals seeking efficient access to urgent care facilities.

🧠 How It Works

Data Collection: The system gathers real-time data on patient count, wait times, and peak hours at nearby urgent care centers.

Machine Learning Prediction Model: A machine learning model analyzes current data and historical trends to predict patient influx and wait times.

Recommendation Engine: The tool recommends urgent care centers with minimal wait times and optimal capacity.

User Notification: Users receive instant recommendations via an intuitive interface with information cards.

💾 Tech Stack

Frontend: React for the user interface.

Backend: Node.js for the server and API calls.

Machine Learning: Python with Scikit-Learn for demand and wait time prediction.

APIs: Google Maps API for location-based services and Maps integration.

🛴 Exploring Princeton

Recently, I watched Oppenheimer, and there’s a scene featuring Einstein so I was on the lookout for the spot. In the process, I was captivated by Princeton's stunning architecture and history like the father of computer science, Alan Turing earned his Ph.D here, which made the experience even more inspiring.

https://github.com/craftingweb/HackPrinceton