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COMPUTER DRIVEN DECISION MAKING IN EMS Mohamud Daya MD, MS Professor of Emergency Medicine Oregon Health & Science University
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Challenge Knowledge growing exponentially Ability to keep up limited by human processing power Aging population
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Example: Calcium Channel Antagonists 1984 Nifedipine Diltiazem Verapamil 2016 Amlodipine Diltiazem Felodipine Isradipine Nicardipine Nifedipine Nisoldipine Verapamil Nimodipine
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Emergency Medical Services Practice of medicine in the out-of-hospital setting Need to make accurate decisions in a time constrained environment in an increasingly complex world Aging population increasing call volume Hospitals expensive Community based health care model (Patient Centered_ Growth of mobile integrated health care in EMS (beyond transport) Technology advances (EMR, Cloud, WiFi)
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Potential Solutions Enhance used of computerized algorithms to drive clinical decisions Harness computer’s ability to store information, rapidly retrieve and process to guide care Cloud for rapid information retrieval
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Initial Project – HP Shock Advisory Algorithm Development
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Shock Advisory Algorithms Part of all AED’s Public Access and EMT Defibrillation 3 second looks x 2-3 Analysis time continued evolution
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Cardiac Arrest – CPR Feedback
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VENTILATIONS (impedance) PADS ACCELERATION (“gravity”) DIRECT IMPEDANCE FORCE (“gravity”)
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Impedance Signal Algorithms Rate ROSC
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12 lead ECG Algorithms
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2006
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2015
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CLINICAL DECISION RULES
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CDR Examples HEART Score Ottawa Ankle Rules NEXUS Canadian C-Spine Rule PERC score Cincinnati Stroke Scale LAPSS LVO scores RACE LAG LAMSS C-STAT ED – Management/TestingEMS – Triage
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GLOBAL INFECTIOUS DISEASE AND EPIDEMIOLOGY NETWORK (GIDEON)
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https://youtu.be/SVRnzILyBOI
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EMR, THE CLOUD AND WIRELESS CONNECTIVITY
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QUESTIONS?
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