Use of Radio Frequency Identification to Establish Emergency Medical Service Offload Times.

Emergency medical services (EMS) crews often wait for emergency department (ED) beds to become available to offload their patients. Presently there is no national benchmark for EMS turnaround or offload times, or method for objectively and reliably measuring this. This study introduces a novel method for monitoring offload times and identifying variance. We performed a descriptive, observational study in a large urban community teaching hospital. We affixed radio frequency identification (RFID) tags (Confidex Survivor™, Confidex, Inc., Glen Ellyn, IL) to 65 cots from 19 different EMS agencies and placed a reader (CaptureTech Weatherproof RFID Interpreter, Barcoding Inc., Baltimore, Maryland) in the ED ambulance entrance, allowing for passive recording of traffic. We recorded data for 16 weeks starting December 2009. Offload times were calculated for each visit and analyzed using STATA to show variations in individual and cumulative offload times based on the time of day and day of the week. Results are presented as median times, confidence intervals (CIs), and interquartile ranges (IQRs). We collected data on 2,512 visits. Five hundred and ninety-two were excluded because of incomplete data, leaving 1,920 (76%) complete visits. Average offload time was 13.2 minutes. Median time was 10.7 minutes (IQR 8.1 minutes to 15.4 minutes). A total of 43% of the patients (833/1,920, 95% CI 0.41-0.46) were offloaded in less than 10 minutes, while 27% (513/1,920, 95% CI 0.25-0.29) took greater than 15 minutes. Median times were longest on Mondays (11.5 minutes) and shortest on Wednesdays (10.3 minutes). Longest daily median offload time occurred between 1600 and 1700 (13.5 minutes), whereas the shortest median time was between 0800 and 0900 (9.3 minutes). Cumulative time spent waiting beyond 15 minutes totaled 72.5 hours over the study period. RFID monitoring is a simple and effective means of monitoring EMS traffic and wait times. At our institution, most squads are able to offload their patients within 15 minutes, with many in less than 10 minutes. Variations in wait times are seen and are a topic for future study.

Paramedic ability to recognize ST-segment elevation myocardial infarction on prehospital electrocardiograms.

BACKGROUND: Identifying ST-segment elevation myocardial infarctions (STEMIs) by paramedics can decrease door-to-balloon times. While many paramedics are trained to obtain and interpret electrocardiograms (ECGs), it is unknown how accurately they can identify STEMIs. OBJECTIVE: This study evaluated paramedics' accuracy in recognizing STEMI on ECGs when faced with potential STEMI mimics. METHODS: This was a descriptive cohort study using a survey administered to paramedics. The survey contained questions about training, experience, and confidence, along with 10 ECGs: three demonstrating STEMIs (inferior, anterior, and lateral), two with normal results, and five STEMI mimics (left ventricular hypertrophy [LVH], ventricular pacing, left and right bundle branch blocks [LBBB, RBBB], and supraventricular tachycardia [SVT]). We calculated the overall sensitivity and specificity and the proportion correct with 95% confidence intervals (CIs). RESULTS: We obtained 472 surveys from 30 municipal emergency medical services (EMS) agencies in five counties with 15 medical directors from seven hospitals. The majority (69%) reported ECG training within the preceding year, 31% within six months; and 74% were confident in recognizing STEMIs. The overall sensitivity and specificity for STEMI detection were 75% and 53% (95% CI 73%-77%, 51%-55%), respectively. Ninety-six percent (453/472, 95% CI 94%-98%) correctly identified the inferior myocardial infarction (MI), but only 78% (368/472, 94% CI 74%-82%) identified the anterior MI and 51% (241/472, 46%-56%) the lateral MI. Thirty-seven percent (173/472, 95% CI 32%-41%) of the paramedics correctly recognized LVH, 39% (184/472, 95% CI 35%-44%) LBBB, and 53% (249/472, 95% CI 48%-57%) ventricular pacing as not a STEMI. Thirty-nine percent (185/472, 95% CI 35%-44%) correctly identified all three STEMIs; however, only 3% of the paramedics were correct in all interpretations. The two normal ECGs were recognized as not a STEMI by 97% (459/472, 95% CI 95%-99%) and 100% (472/472, 95% CI 99%-100%). There was no correlation between training, experience, or confidence and accuracy in recognizing STEMIs. CONCLUSIONS: Despite training and a high level of confidence, the paramedics in our study were only able to identify an inferior STEMI and two normal ECGs. Given the paramedics' low sensitivity and specificity, we cannot rely solely on their ECG interpretation to activate the cardiac catheterization laboratory. Future research should involve the evaluation of training programs that include assessment, initial training, testing, feedback, and repeat training.