Exploring the System Dynamics of Covid-19 in Emergency Medical Services
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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | masterThesis |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/13922 https://doi.org/10.26756/th.2022.409 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513468396208128 |
|---|---|
| author | Ali, Muhammad |
| author_facet | Ali, Muhammad |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali, Muhammad |
| dc.date.none.fl_str_mv | 2022-08-11T07:59:48Z 2022-08-11T07:59:48Z 2022 2022-05-12 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/13922 https://doi.org/10.26756/th.2022.409 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Emergency medical services -- Lebanon Emergency medical services -- Case studies COVID-19 Pandemic, 2020- -- Case studies Machine learning Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_244c4d25b8fcaec0daf5c3a2e646093a |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/13922 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Exploring the System Dynamics of Covid-19 in Emergency Medical ServicesAli, MuhammadEmergency medical services -- LebanonEmergency medical services -- Case studiesCOVID-19 Pandemic, 2020- -- Case studiesMachine learningLebanese American University -- DissertationsDissertations, AcademicEmergency 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.1 online resource (ix, 48 leaves): ill. (some col.)Bibliography: leaf 44-48.Lebanese American University2022-08-11T07:59:48Z2022-08-11T07:59:48Z20222022-05-12Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/13922https://doi.org/10.26756/th.2022.409http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/139222022-08-23T06:23:47Z |
| spellingShingle | Exploring the System Dynamics of Covid-19 in Emergency Medical Services Ali, Muhammad Emergency medical services -- Lebanon Emergency medical services -- Case studies COVID-19 Pandemic, 2020- -- Case studies Machine learning Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| title_full | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| title_fullStr | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| title_full_unstemmed | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| title_short | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| title_sort | Exploring the System Dynamics of Covid-19 in Emergency Medical Services |
| topic | Emergency medical services -- Lebanon Emergency medical services -- Case studies COVID-19 Pandemic, 2020- -- Case studies Machine learning Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/13922 https://doi.org/10.26756/th.2022.409 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |