Health Systems - EMS

Inspiration

We wanted to help emergency medical services identify ambulance response delays more effectively because every second matters when saving lives.

What it does

Our dashboard monitors ambulance response times across New York City's five boroughs, using statistical analysis and borough-specific thresholds to flag unusually slow responses.

How we built it

We developed a Python-based application using FastAPI, Pandas, NumPy, and Chart.js, applying Six Sigma techniques and statistical process control to simulated EMS data.

Challenges we ran into

Our biggest challenge was establishing meaningful response-time thresholds while accounting for differences between boroughs, traffic conditions, and seasonal patterns.

Accomplishments that we're proud of

We created an interactive dashboard that analyzes EMS response times, identifies unusual delays, and visualizes operational performance across all five NYC boroughs.

What we learned

We learned how statistical process control, percentile-based thresholds, and data visualization can transform complex operational data into actionable insights.

What's next for Health Systems - EMS

We hope to integrate real-time EMS data, improve delay detection, and help emergency response supervisors make faster, more informed operational decisions