Hidden frontlines of public health: How EMS data can power a new era of syndrome monitoring
First responders are often the first signs of witnessing an emerging health crisis – an abnormal overdose in a community, a surge in heat-related illnesses or days before the flu season peaks. But until recently, their observations were isolated in the Electronic Patient Care Report (EPCR), disconnected from the wider public health system.
This is changing. The rise of syndrome monitoring tools powered by EMS data is changing how emergency responses, public health departments and preparation teams detect and respond to emerging threats – often before official diagnosis is recorded.
What is Syndrome Surveillance – Why are EMS data so valuable?
Syndrome surveillance refers to the real-time collection, analysis and interpretation of health-related data to identify trends or abnormalities that may mark public health events. Unlike traditional surveillance, which relies on confirmed diagnosis (usually delayed or weeks) reported through clinical channels, the syndrome system detects patterns based on symptoms, major complaints and behavioral indicators.
This is where EMS data plays a key role. Emergency Medical Services submits EPCRs within hours of 911 response (much faster than hospitals or laboratories), and these records contain structured data (such as vital signs and provider impressions) and narrative areas that provide a rich clinical environment.
According to the CDC’s National Syndrome Surveillance Program (NSSP), the use of data from non-traditional sources, including EMS, is key to faster outbreak detection and community-level awareness. Through correct analysis, these data became the main indicator of local health threats.
How it works: Early alerts that trigger real-time operations
Modern syndrome monitoring platforms use machine learning and statistical modeling to analyze historical EMS data by region, time and syndrome type – establishing a benchmark that constitutes “normal” activities.
When new data comes in, the system compares it to these baselines. If there is a large bias (for example, 50% of the peak in opioid-related calls over three days – it triggers an alert. Agents can customize alert thresholds and subscribe to a specific geographic location or symptom type to receive notifications via text, email, or dashboard view.
This model gives EMS leaders, epidemiologists and contingency planners the following functions:
- Clustering of detecting overdoses, gunshot wounds or mental health calls
- Track emerging flu, RSV, or symptoms similar to co-certification
- Monitor heat-related diseases in vulnerable communities in extreme weather
- Active public safety campaign information based on crash or trauma clusters
Even the ability to detect these offsets 24-48 hours ago may be the difference between reactive competition and coordinated response.
A case of wider adoption
Syndrome monitoring has produced measurable results. Since 2024, the CDC has highlighted more than 20 success stories, highlighting the value of integrating EMS and emergency care data into real-time threat detection, especially in areas with limited hospital infrastructure.
Additionally, in the early days of the COVID-19 pandemic, some public health agencies used EMS data to track suspicious cases and then before confirming a positive test – reading faster on community differences. Recently, agencies have deployed these tools to monitor fentanyl spikes, coordinate Narcan distributions and support thermal safety programs.
In addition to direct crisis detection, the data also provides information for strategic planning. Agents are using EMS trend calls to location-provided calls, train responders for new risks, and work with local health departments for targeted education or outreach.
Why is it important now
The rise of extreme weather, behavioral health crises and drug use trends have expanded local emergency systems. Meanwhile, the public health sector is under pressure to have fewer resources.
Syndrome monitoring using EMS data represents a highly leveraged tool in this environment. It turns everyday emergency calls into a series of real-time intelligence that can help the community stay ahead of the curve.
But the adoption is still inconsistent. While some states have fully integrated EMS into their surveillance infrastructure, others are still processing delayed hospital reports and disconnected data systems. As in a report in the Journal of Medical Internet Research (JMIR), “Policies to establish collaborative frameworks will need to support data sharing between federal, state and local partners.”
Looking to the future
The future of public health surveillance will be faster, smarter and more localized – EMS data will be the cornerstone of this transformation. Originally used as a tool for recording care, the current situation is becoming one of the most dynamic signals in community health trends.
Whether the threat is a synthetic opioid, an infectious disease or an environmental hazard, EMS data has the potential to detect dangers faster, mobilize responses faster and ultimately save more lives.
It’s time to bring the frontlines into the center of our public health strategy.
Photo: Flickr User EMS_EMT
Joe Graw is the chief growth officer of Imagetend. Joe’s passion for learning and exploring new ideas in the industry is not just about managing growth on the imaging side—it’s forward-looking thinking. Many aspects of participating in Imagetrend are part of driving Joe. He is committed to our community, our customers and their use of data to drive results, implement changes and drive improvements in their industries.
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