Exploring the System Dynamics of Covid-19 in Emergency Medical Services

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dc.contributor.author Ali, Muhammad
dc.date.accessioned 2022-08-11T07:59:48Z
dc.date.available 2022-08-11T07:59:48Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-05-12
dc.identifier.uri http://hdl.handle.net/10725/13922
dc.description.abstract Emergency Medical Services (EMS) are essential to the healthcare system as they maximize the overall expected survival probability of patients. During the COVID-19 pandemic, peoples’ lifestyle changed and their decisions to seek medical assistance were mixed with fear. Similarly, EMS systems needed to take extra precautions in terms of personal protective equipment protocols. These changes created variability in both demand levels and the response times. In this context, this research presents descriptive and predictive analysis to fully explore the Covid-19 impact on EMS in Lebanon. The descriptive analysis focuses on the changes in call volumes and response times during the COVID-19 pandemic compared to both other countries and previous years. Results show that the number of calls and number of missions dropped yet, the emergency response time was higher and more variable than in previous years. The predictive analysis yielded a model of response times for emergency missions through machine learning, specifically using a random forest algorithm. The value in building a predictive model of response time lies in identifying the most influential predictors of response times such as team utilization, case severity, COVID-19 patients, and roadway distance. Furthermore, the model allows for the identification of the variables that influence response time across different segments of the emergency response process: dispatch, wheeling, and roadway times. As a whole, this work supports EMS operations through the identification of managerial levers that have a direct influence on response time. en_US
dc.language.iso en en_US
dc.subject Emergency medical services -- Lebanon en_US
dc.subject Emergency medical services -- Case studies en_US
dc.subject COVID-19 Pandemic, 2020- -- Case studies en_US
dc.subject Machine learning en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Exploring the System Dynamics of Covid-19 in Emergency Medical Services en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MBA en_US
dc.author.school SOB en_US
dc.author.idnumber 202005494 en_US
dc.author.commembers Badr, Nabil
dc.author.commembers Tarhini, Abbas
dc.author.department Information Technology And Operations Management en_US
dc.description.physdesc 1 online resource (ix, 48 leaves): ill. (some col.) en_US
dc.author.advisor Srour, Jordan
dc.keywords Emergency Medical Services en_US
dc.keywords Data Analytics en_US
dc.keywords Operations Management en_US
dc.keywords COVID-19 en_US
dc.keywords Machine Learning en_US
dc.keywords Random Forests en_US
dc.description.bibliographiccitations Bibliography: leaf 44-48. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.409
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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