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Utilizing AEMS (Assess-EOP-Map-Simulate) Algorithm to Assess and Spatially Link Prehospital Emergency Medical Services Resources in Road-Traffic Mass Casualty Incidents in Kumasi, Ghana

Published online by Cambridge University Press:  13 July 2023

Roxane Richter
Affiliation:
University of Louisville, Louisville, USA Fulbright-Fogarty Postdoctoral Global Health Fellow, Sub-Saharan Africa, Kumasi, Ghana
Thomas Flowers
Affiliation:
Owensboro Regional Health Center Hospital, Greenville, USA
George Oduro
Affiliation:
Komfo Anokye Teaching Hospital, Kumasi, Ghana
Joe Bonney
Affiliation:
Komfo Anokye Teaching Hospital, Kumasi, Ghana
Paa Forson
Affiliation:
Komfo Anokye Teaching Hospital, Kumasi, Ghana
Chris Oppong
Affiliation:
Komfo Anokye Teaching Hospital, Kumasi, Ghana
Sonia Cobbold
Affiliation:
Komfo Anokye Teaching Hospital, Kumasi, Ghana
Rainier Richter
Affiliation:
The Learning Lab, Nashville, USA
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Abstract

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Introduction:

Due to the high number of road traffic accidents with acute injuries and fatalities–particularly in Mass Casualty Incidents (MCI) in low-resource urban sub-Saharan African cities–research was undertaken to create an evidence-based algorithm that could be used to assess and geospatially link EMS needs in Kumasi, Ghana, to trauma resources. Our examination showed that non-MCI fatalities was approximately 2.5%, however, MCI fatalities were found to be 1.8 times higher–at 4.3%, indicating significant opportunities in the planning, preparedness, care, and transport among MCI patient management.

Therefore, several studies (funded through Fulbright-Fogarty and Fulbright Specialist programs), supported the development of the A-E-M-S (Assess-EOP-Map-Simulate) Medical Mass Casualty Algorithm that began networking accident ‘hotspots’ to existing trauma-level capabilities and surge capacity competencies in eight specified Kumasi hospitals. This low-cost response model promises to be an innovative alternative to long-term infrastructure development and high-priced resource distributions. Use of GIS and UAV drones allowed response systems to geospatially locate, classify, shift, and/or augment resources as needed in conjunction with hotspots.

Method:

Sample sizes were averaged at 295 for all patients' ages, with only a sample size of 292 for adults at 95% confidence intervals, and a standard deviation of 0.5. A total of 300 road-traffic accident victims were collected at KATH A&E in February-May, 2017, utilizing handheld devices by four researchers 24/7 daily.

Results:

Our examination showed that non-MCI fatalities were approximately 2.5%, however, MCI fatalities were found to be 1.8 times higher–at 4.3%, indicating significant opportunities in the planning, preparedness, care, and transport among MCI patient management.

Conclusion:

To date–and in partnership with Kwame Nkrumah University of Science and Technology, Komfo Anokye Teaching Hospital, Ghana Medical Council, Health Services, National Disaster Management Organization, and others–over 306 Ghanaian healthcare providers from 80 different facilities have been trained in the AEMS program.

Type
Poster Presentations
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of World Association for Disaster and Emergency Medicine