Graduation Semester and Year
2022
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Erick C Dr
Abstract
The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation (NSF Award # 2028612) funded this research to help federal agencies with strategies. This research is based on a NSF funded grant to help federal agencies with strategies by investigating supply chain strategies that would minimize the impact on underserved populations during pandemic and by integrating artificial intelligence and social determinants of health to make optimized supply chain models more robust and updated real-time. This project leverages Artificial Intelligence (AI) integrated with an Infrared Facial Recognition, Thermal Imaging and Telemedicine tools to improve patient outcomes for those most at-risk (URM Community) for SARS-CoV-2 (COVID-19) and other severe respiratory illnesses, how this information can be used to design supply chain model that ensures that vaccines can be delivered to this community to prevent and minimize the impacts of COVID-19. The specific objectives of this study were; 1) Use convergent innovation ecosystems and platforms [2] to identify Automated Data Capture (ADC) and Artificial Intelligence (AI) needed to automate the healthcare supply chain. 2) Model the COVID-19 Supply Chain from manufacture to vaccine delivery that optimizes the most efficient manner to impact the most at risk populations and communities; and 3) Identify the readiness and the societal cost benefit of this model for use when as vaccines become ready for use. The outputs of optimized supply chain model using different scenarios showed the prioritized distribution of COVID-19 vaccines to at-risk communities with much higher service levels as compared to non-prioritized communities and overall service levels. This study also identified the phenomena of last mile importance, which is missing in existing healthcare supply chain models. The last mile transportation concept was critical in saving lives during the pandemic for underserved populations. The supply chain model then maximizes social goods by sending drugs or vaccines to the communities that need it the most regardless of ability to pay. The outcome of this study helped us prioritize the communities that need the vaccines the most. This informs our supply chain model to shift resources to these areas showing the value in real time prioritization of the COVID-19 supply chain. This research provides information can be used in our healthcare supply chain model to ensure timely delivery of vaccines and supplies to COVID-19 patients that are the most vulnerable and hence the overall impact of COVID-19 can be minimized.
Keywords
Supply chain optimization, Artificial intelligence, Underrepresented minority communities, Pandemics and disastrous events, Automated data capture
Disciplines
Engineering | Operations Research, Systems Engineering and Industrial Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Azeem, Gohar, "LEVERAGING AI AND SUPPLY CHAIN TECHNOLOGIES WITH THERMAL IMAGING AND TELEMEDICINE FOR EARLY DETECTION AND PREVENTION OF COVID-19 AND RESPIRATORY INFECTIONS IN URM COMMUNITIES" (2022). Industrial, Manufacturing, and Systems Engineering Dissertations. 171.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/171
Comments
Degree granted by The University of Texas at Arlington