From silos to integration: How the information renaissance is transforming emergency medicine

Today, emergency physicians increasingly operate in artificial intelligence-assisted environments, often using ambient listening technology that can record medical records in real time. However, even in these technologically advanced environments, critical data from EMS are often not available prior to patient arrival and are sometimes not even available for initial clinical decision-making.

Meanwhile, first responders face their own information challenges as they make split-second treatment and transport decisions for complex patients such as cardiac arrest, head injuries from falls in parking lots, and patients severely burned by hot summer asphalt while receiving resuscitation care. However, after EMS providers transfer patients to the hospital, they lack information to understand whether they made the right choice and how their actions impacted the patient's care.

Emergency clinicians face life-or-death decisions every day with limited information, relying on their education, personal experience, and expertise to guide them. They are judged on patient outcomes, but they have historically operated in siled knowledge environments where these outcomes are often not readily available.

The traditionally episodic nature of EMS particularly limits providers because they consider discrete events without longitudinal visibility, preventing them from identifying patterns in care delivery and previous outcomes and leveraging aggregated insights from thousands of similar cases. Considering that EMS providers transport tens of millions of patients each year, the scale of the resulting knowledge gap is staggering.

But as urgent care evolves from siled individual expertise to using collective, data-driven knowledge to enhance patient care in real time, two key aspects are transforming. The first is enhanced interoperability and bidirectional data flow between EMS and hospitals. The second is shared knowledge that enhances clinical decision-making. Where doctors once relied on personal experience and memorized protocols or best practices to guide their decisions, they now have systems that provide recommendations based on shared data, allowing them to focus more on the patient and less on the minor details of medicine.

Breaking down barriers between EMS and hospitals

Emergency medicine has long drawn an imaginary line on the sliding glass door of the emergency room, leaving EMS professionals in the dark about patient outcomes after transfer and ultimately limiting their ability to refine decision-making at the point of care.

Consider a treatment from the 1960s and 1970s: the Military Anti-Shock Trousers (MAST), an inflatable garment worn from the waist down on trauma patients suffering severe blood loss. When deployed, they impressively raise blood pressure in the field. However, the full clinical picture conclusively demonstrates that artificially raising blood pressure in patients with internal bleeding often leads to poor outcomes despite promising initial vital signs.

This pattern has been repeated countless times during the development of EMS. Treatments once thought to be effective in the short term often prove detrimental to long-term outcomes because providers lack appropriate data-sharing mechanisms, which leaves insights fragmented and localized.

In today's most capable systems, medical staff transport a patient to the hospital and can then log into the system to view data such as the patient's first blood gas and blood pressure readings in the emergency room or ICU. Through intentional data continuity, the artificial divide between pre-hospital and inpatient care is disappearing, improving care itself.

The power of advanced analytics in patient care

The benefits of advanced data analysis don't just apply to a single program or daily task. By removing the cognitive load of complex calculations, protocol memorization, and highly skilled procedures, providers can focus more on patient assessment, actual care delivery, and data-driven clinical decisions that incorporate insights from thousands or millions of relevant patient experiences.

By automating data collection and sharing, medical professionals can gain aggregated knowledge, revealing surprising truths: simpler, standardized methods often produce better results than traditional, skill-intensive techniques.

This shift is particularly evident in airway management procedures, for example. Historically, emergency clinicians have been known for their proficiency in endotracheal intubation, a skill honed in a controlled hospital environment with adequate staff but challenging to perform perfectly in relatively austere prehospital field conditions. Furthermore, it is emphasized that implementation of hospital-based treatments in the prehospital setting requires rigorous evaluation of outcomes because patients are not homogeneous in presentation and condition.

Fortunately, we now have several well-designed trials demonstrating the effectiveness of various airway interventions in the out-of-hospital setting, allowing EMS physicians, paramedics, and first responders to provide evidence-based interventions. A recent study analyzing longitudinal changes in advanced airway management showed that simpler techniques that require less technical skill but have comparable efficacy are now more common in pediatric cases as well as in adult cardiac arrest cases. A 2024 Ohio State University study found findings consistent with previous research

Such findings are consistent with previous research, including systematic reviews and the 2018 AIRWAYS-2 randomized trial, and represent the critical role of data in validating or changing perspectives and developing best practices in emergency care at scale.

Learn how to succeed in firefighting

Fire departments and agencies have long studied the results to identify preventable factors and implement building codes and inspection requirements that have significantly reduced fire incidents despite population growth.

This model provides a compelling blueprint for emergency medicine. Integration of EMS and hospitals has been limited because outcomes were previously unknown or disconnected from the original intervention. Now, with aggregated outcomes data and the ability to exchange individual patient health records, we can begin to implement similar approaches, based on a thorough analysis of what actually works (not just what currently seems to work).

The future of data-augmented medicine

Advanced data analytics and machine learning technologies are leading emergency medicine into the 21st century by enabling healthcare providers to create and share knowledge that helps improve performance and patient outcomes. These technologies provide predictive models for automated decision support tools, providing clinicians with guidance and insights based on patterns in large volumes of data that they would otherwise not have access to (let alone use).

As healthcare evolves, clinical expertise will be enhanced, not replaced, by collective knowledge and analysis. This approach will enable proactive, data-based care delivery and performance improvements at scale, allowing practitioners to focus on what really matters: providing the best possible care to each patient.

Photo: pablohart, Getty Images

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