I Analyzed 51 Million Emergency Calls to Build an AI That Could Save Lives. Here’s What I Found.
What started as a graduate school assignment became a mission to bridge one of healthcare’s most critical knowledge gaps.
When I first dove into the National Emergency Medical Services Information System (NEMSIS) dataset, I wasn’t prepared for what I’d discover. 51 million emergency calls from all 54 states in 2022 alone. Each record represents someone’s worst day — and the split-second decisions that could determine whether they lived or died.
The Problem Hidden in Plain Sight
Here’s what most people don’t realize about emergency response: the person arriving at your medical emergency might have months of training, or they might have years. An EMT, a paramedic, a firefighter, or a police officer — all equipped with the same basic tools to collect vital signs, but with vastly different abilities to interpret what those numbers mean.
Blood pressure 90/60. Heart rate 110. Oxygen saturation 94%. Glasgow Coma Scale 12.
To an experienced paramedic, this paints a clear picture. To someone with basic life support training, these are just numbers that need to be radioed to the hospital.
What 51 Million Emergencies Taught Me
After processing this massive dataset through machine learning algorithms, two critical patterns emerged that could change everything:
The Glasgow Coma Score and ROX Index are game-changers. These two measurements — one assessing neurological function, the other respiratory efficiency — proved to be the most powerful predictors of patient risk. My Random Forest model showed they had importance scores of 0.30 and 0.27 respectively, far outweighing individual vital signs.
The ROX Index was the surprise star. Calculated as (SpO2/FiO2)/Respiratory Rate, this composite score captures both oxygenation and ventilation efficiency. Values below 4.88 consistently flagged high-risk patients, while values above 22 indicated lower risk. Yet most first responders have never heard of it.
From Academic Paper to Real-World Prototype
The research was compelling, but I couldn’t stop there. Papers don’t save lives — tools do. So I built something that could actually be used in the field: a web application that turns vital signs into instant risk assessments.
You can see the evolution at bryankstites.com/ems-ai/behind-model. What started as data analysis became a working prototype that any first responder could use on a smartphone or tablet.
Here’s how it works in practice:
- Responder enters basic vital signs (equipment they already carry)
- AI processes the data using patterns learned from 51 million calls
- Instant risk classification: Low, Moderate, High, or Critical
- Clear guidance on urgency and potential interventions needed
The Real Game-Changer: Democratizing Clinical Expertise
This isn’t about replacing human judgment — it’s about amplifying it. Think about a police officer arriving first at a cardiac arrest, or a firefighter responding to a motorcycle accident. They have the training to collect vital signs and provide basic life support. What they often lack is the clinical experience to recognize subtle patterns that indicate someone is about to crash. More importantly — what happens when the advanced clinical staff are hours away and the first person on scene is a traffic officer with a BLS bag. This app could be the bridge between calling for an airlift and waiting over an hour for an ambulance to arrive.
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The gap is massive and costly:
- Police officers might have 40 hours of medical training
- Firefighters often have EMT certification (120–150 hours)
- Paramedics have 1,200+ hours of medical education
My AI bridges that gap by giving everyone access to pattern recognition that typically requires years of clinical experience.
The Challenges I Had to Solve
Overfitting was real. My initial model achieved 98.7% accuracy — suspiciously high. The algorithm was memorizing rather than generalizing. I had to rebuild the risk classification system, create more robust validation methods, and accept that perfect accuracy isn’t the goal — reliable, actionable insights are.
Creating ground truth was complex. The NEMSIS dataset doesn’t come with pre-labeled risk categories. I had to develop composite scoring systems for neurological (GCS), respiratory (ROX), and cardiovascular (RPP) functions, then combine them into overall risk assessments.
Real-world applicability mattered most. Academic accuracy means nothing if the tool can’t work on a smartphone in the middle of the street during a thunderstorm.
What This Means for Emergency Response
Try the working prototype at bryankstites.com/ems-ai. Enter some vital signs and watch how machine learning transforms raw numbers into clinical insights.
The potential impact is big:
- Faster recognition of deteriorating patients
- More accurate triage decisions by non-medical first responders
- Earlier activation of critical care resources
When someone’s life hangs in the balance, every second counts. But so does every piece of information we can extract from the data we’re already collecting.
What’s Next
This prototype demonstrates what’s possible when we thoughtfully apply AI to real-world problems. I built this to spark conversations about how technology can bridge critical knowledge gaps in emergency response.
I’m curious to hear from EMS professionals, healthcare innovators, and other researchers about their experiences with these challenges. What patterns do you see in your field? Where do knowledge gaps create the biggest risks? How might tools like this fit into existing workflows?
The goal was never to create another piece of technology that sits on a shelf. It was to start a dialogue about how we can put the power of data-driven insights into the hands of every first responder who shows up when someone needs help most.
Sometimes the best innovations come from unexpected conversations and collaborations.
What patterns are hiding in your industry’s data? What knowledge gaps could AI help bridge?
Bryan Stites is an IT Risk Manager at Capital One and graduate student in Business Analytics who applied his studies to prehospital emergency medicine. Learn more about his work at bryankstites.com. Connect with him to discuss healthcare AI, emergency services innovation, or data science applications.
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