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Today, emergency physicians increasingly operate in AI-assisted environments, often with ambient listening technology that can document the medical record in real time. Yet even in these technologically advanced settings, critical data from EMS are usually not made available prior to patient arrival and sometimes remain unavailable for initial clinical decision-making.
Meanwhile, EMS professionals face their own information challenges as they make split-second treatment and transport decisions for complex patients — like someone who suffered cardiac arrest, a head injury from the associated fall in a parking lot and severe burns from hot summer asphalt while undergoing resuscitative efforts. After EMS providers have transferred that patient to the hospital, however, they lack the information to know if they made the right choices and how their actions influenced the patient’s care.
Emergency clinicians face life or death decisions with limited information every day, relying on their education, personal experience and expertise to guide them. They are judged by patient results, yet they have historically operated in isolated knowledge environments in which those results often aren’t readily available.
The traditionally episodic nature of EMS in particular has limited providers as they consider discrete incidents without longitudinal visibility, preventing them from recognizing patterns across care delivery and prior outcomes and from leveraging aggregated insights from thousands of similar cases. The scale of the resulting knowledge gap is staggering considering that EMS providers transport tens of millions of patients annually.
But a transformation is underway on two critical fronts as emergency care evolves from siloed, individual expertise to real-time enhancement of patient care using collective, data-driven knowledge. First is the enhanced interoperability and bidirectional data flow between EMS and hospitals. Second is the shared knowledge that is augmenting clinical decision-making. Where practitioners once relied on personal experience and memorized protocols or best practices to guide their decisions, they now have systems that offer recommendations based on shared data, freeing them to focus more on their patients and less on the minor details of medicine.
Breaking down the barriers between EMS and hospital
Emergency medicine has long operated with an imaginary line drawn at the sliding glass doors of the emergency department, leaving EMS professionals in the dark about patient outcomes after transfer and, ultimately, limiting their ability to refine their decision-making at the point of care in the field.
Consider a treatment from the 60s and 70s: military anti-shock trousers (MAST), inflatable garments applied from the waist down for trauma patients suffering severe blood loss. When deployed, they would impressively elevate blood pressure in the field. However, the complete clinical picture eventually revealed that artificially increasing blood pressure in patients with internal hemorrhage often led to poorer outcomes despite the initially promising vital signs.
This pattern has repeated itself countless times throughout EMS’s evolution. Treatments that were once thought to be effective in the immediate term often proved detrimental to long-term outcomes because providers lacked proper data-sharing mechanisms, which kept insights fragmented and localized.
In today’s highest-functioning systems, medics transport a patient to the hospital and can later log into their systems to see, among other data, the patient’s first blood gas and blood pressure readings in the emergency department or ICU. The artificial divide between prehospital and hospital care is dissolving through intentional data continuity that, by extension, improves care delivery itself.
The power of advanced analytics in patient care
The benefits of advanced data analytics apply to more than individual procedures or day-to-day tasks. By removing the cognitive load of complex calculations, protocol memorization and high-skill procedures, providers are free to focus more on patient assessment, actual care delivery and data-driven, clinical decision-making that incorporates insights from thousands or millions of relevant patient encounters.
Through automated data collection and sharing, medical professionals can access aggregated knowledge that reveals surprising truths: Simpler, standardized approaches often yield better outcomes than traditional, skill-intensive techniques.
This transformation is particularly evident in airway management procedures, for instance. Historically, emergency clinicians were distinguished by their proficiency in endotracheal intubation — a skill honed in controlled hospital environments with ample staff available but challenging to execute perfectly in relatively austere prehospital field conditions. Moreover, the emphasis on instituting hospital-based therapies in the prehospital environment requires rigorous evaluation of outcomes, as patient presentations and conditions are not the same.
Fortunately, we now have several well-designed trials that demonstrate a wide variety of airway interventions are efficacious in the out-of-hospital environment, empowering EMS physicians, paramedics and EMTs to provide evidence-based interventions. A recent study analyzing longitudinal changes in advanced airway management revealed that simpler techniques requiring less technical skill but offering comparable efficacy are now more common in pediatric cases as well as for adult cardiac arrests. This finding from a 2024 Ohio State study aligns with previous research
Findings like these — consistent with previous research, including systematic reviews and the 2018 AIRWAYS-2 randomized trial — represent the critical role of data in validating or changing perspectives and in evolving best practices in emergency care at scale.
Learning from fire prevention’s success model
Fire departments and agencies have long studied outcomes to identify preventable factors, implementing building codes and inspection requirements that have dramatically reduced fire incidents despite population growth.
This model provides a compelling blueprint for emergency medicine. EMS and hospital integration has been limited because outcomes were previously unknown or disconnected from initial interventions. Now, with aggregated outcome data combined with the ability to exchange health records for an individual patient, we can begin to implement similar approaches based on comprehensive analysis of what actually works — not just what appears effective in the moment.
The future of data-augmented medicine
Advanced data analytics and machine learning technologies are leading emergency medicine into the 21st century by enabling providers to create and share knowledge that facilitates improvements in performance as well as patient outcomes. These technologies feed predictive models that automate decision support tools, providing clinicians with guidance and insights based on patterns across large amounts of data that they otherwise wouldn’t be able to access, let alone use.
As health care evolves, clinical expertise will be augmented — not replaced — by collective knowledge and analytics. Such an approach will enable proactive, data-informed care delivery and performance improvement at scale, allowing practitioners to focus on what truly matters: delivering the best possible care to every patient.
Photo: pablohart, Getty Images
Brent Myers, M.D., MPH, FACEP, FAEMS is the chief medical officer of ESO and an internationally recognized expert in the area of Emergency Medical Services (EMS), particularly as it relates to systems of care, performance improvement and population management.
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