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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
Published on: March 14, 2017
A Pilot Study Validating Video-Based Training on Pre-Hospital Stroke Recognition
Aliza Brown1,2,3, Sanjeeva Onteddu1, Rohan Sharma1,2
1Department of Neurology, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.
Introduction:
Delays in recognizing stroke during pre-hospital emergency medical system (EMS) care may affect triage and transport time to an appropriate stroke ready hospital and may preclude patients from receiving time dependent treatment. All EMS transports in a large urban area in the stroke belt were evaluated for transport destinations, triage and transport time and stroke recognition following distribution ofan educational training video to local EMS services.
Hypothesis:
Following video training, local paramedics will improve stroke recognition and shorten triage and transport time to appropriate stroke centers of care.
Methods:
A training module (<10 min) containing a stroke triage scenario, instruction on the Cincinnati Prehospital Stroke Score (CPSS) and the Los Angeles Prehospital Stroke Score (LAPSS) and 'where to transport' stroke patients was distributed and viewed by 96 paramedics. Data was collected from February to October 2016. Stroke recognition was determined from one primary stroke center (PSC) hospital's confirmation of EMS delivered patients (Site A). Yearly stroke recognition percentages of 44% from Site A in 2014 were used as baseline.
Results:
A total of 34,833 emergency 911 response transports were made with a total of 502 (1.4%) suspected strokes identified by paramedics. Median [IQR] triage and transport time for stroke transports was 33 [27-41] min. The PSC hospitals received a 5% increase in stroke transports and non-specific care facilities decreased by 7%. From 8,554 transports to site A (PSC) confirmed strokes totalled 107 transports with 139 suspected strokes by paramedics. Of these transports, 60 were correctly identified by paramedics (positive predictive value of 43%, sensitivity of 56%). By the second month following training, recognition percentages increased from baseline to 64%. At five months, percentages of correct stroke identification had dropped to 36%.
Conclusion:
Video based training improved stroke recognition by an additional 19%, but continual monthly or quarterly training is recommended for maintenance of increased stroke recognition.
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