Application of the emergency medical services trigger tool to measure adverse events in prehospital emergency care: a time series analysis

Abstract Background Emergency Care has previously been identified as an area of significant concern regarding the prevalence of Adverse Events (AEs). However, the majority of this focus has been on the in-hospital setting, with little understanding of the identification and incidence of AEs in the prehospital environment. Method The early development and testing of Emergency Medical Services (EMS) specific triggers for the identification of AEs and Harm has been previously described. To operationalise the Emergency Medical Services Trigger Tool (EMSTT), the processes developed by the Institute for Healthcare Improvement for use with the Global Trigger Tool were adapted to a prehospital emergency care setting. These were then applied using a stepwise approach to the analysis of 36 consecutive samples of patient care records over an 18-month period (n = 710). Inter-rater reliability was measured for each trigger item and level of Harm classification. Total Triggers per 10,000 Patient Encounters, AEs per 10,000 Patient Encounters and Harm per 10,000 Patient Encounters were measured. All measures were plotted on Statistical Process Control Charts. Results There was a high level of inter-rater agreement across all items (range: 85.6–100%). The EMSTT found an average rate of 8.20 Triggers per 10,000 Patient Encounters, 2.48 AEs per 10,000 Patient Encounters and 0.34 Harm events per 10,000 Patient Encounters. Three triggers: Change in Systolic Blood Pressure Greater Than 20%; Temp > 38 °C without subsequent reduction; and SpO 2 < 94% without supplemental Oxygen or SpO 2 < 85% without assisted ventilation accounted for 93% (n = 180) of the triggers found throughout the longitudinal analysis. Discussion With sufficient focus on implementation and data collection, as well as the inclusion of a contextually relevant system for classifying AE/Harm, the EMSTT represents a potentially successful strategy towards identifying the rate of AEs within EMS across a large patient population with limited commitment of time and resources