How GOOD Is That Data?

Measuring EMS system performance is crucial to identifying trends in patient care that affect patient outcomes. But while collecting standardized data sets is important in all facets of healthcare, the field of prehospital care poses inherent difficulties. Defining obtainable data elements across multiple institutions (e.g., 9-1-1 centers, first-response agencies, EMS agencies and hospitals) can prove cumbersome. Such difficulties have hindered research that could have a meaningful impact on patient outcomes. Those involved in EMS research must work to establish sound methodologies, consolidate data from multiple sources and generate integrated feedback reports. These will be important steps in unlocking the gate to evidence-based medicine in prehospital care.

     Standardized research templates and metrics improve the rate and quality of data collection because they permit the creation of composite databases of information that's comparable from one healthcare system to another.¹ EMS is no different--standardizing the data-collection process enables systems to analyze events both internally and across regions. EMS organizations and emergency physicians have begun to respond to the call for evidence-based medicine, and recent research initiatives have led to standardization of some EMS data collection.² However, the difficulties inherent in EMS research can complicate such efforts. In examining one of the most frequently studied prehospital events, cardiac arrest, evaluating these difficulties can help illustrate the considerations that should be made when developing research methodology.

A Scenario
     Prehospital providers are dispatched for a pulseless and apneic 56-year-old male in cardiac arrest. Bystanders have initiated CPR. The first-responding fire agency arrives on scene and confirms the absence of spontaneous respirations and pulses. Firefighters apply an AED and assist ventilations with a bag-valve mask. EMS arrives with advanced life support interventions, and the patient is transported to the closest facility. He has a return of spontaneous circulation while in the care of EMS providers, then is turned over to the hospital staff for further care.

     In this case, neither the EMS record nor the dispatch record will indicate the patient's ultimate outcome. Despite the importance of prehospital care in the outcome of this patient, this metric of successful response and intervention is not measured. Because of disconnects between the agencies involved in such responses--i.e., EMS, hospitals and dispatch centers--the ability to consistently measure response intervals and collect meaningful outcome data is lost, or becomes too cumbersome to continue efficiently. But before it's possible to piece these data elements together, a decision must be made on which data elements to collect.

Keeping It Simple
     Reporting prehospital cardiac arrest data in a standardized format was the goal of the Utstein style of data collection. The first version of Utstein, in 1991, required the measurement of several time intervals,² including the interval from call reception to first defibrillation. However, capturing this data was not feasible because the clocks in 9-1-1 centers and the EMS monitors/defibrillators that delivered first shocks were not synchronized. Because a "universal stopwatch" was absent, this data element was deemed unobtainable, and the next version of Utstein excluded it.³ It was also determined that some time elements, such as time of EMS arrival to the patient, were inherently unclear because multiple caregivers may arrive at different times with different pieces of equipment.³

     In addition to cardiac arrest, there are current measures in trauma research that are difficult to collect. The Golden Hour, for instance, is a commonly used interval for predicting outcomes in trauma patients. It begins at the time of injury and ends at the point of definitive care. It can be quite uncertain, though, as to when an event actually occurred, based on whether it was witnessed and how long it took bystanders to activate EMS. Because of such imprecisions, a true Golden Hour may not be easily identified.