Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted AN AGILE DATA ANALYTICS FRAMEWORK TO IMPROVE HEALTHCARE PROCESS PERFORMANCE IN INFECTIOUS DISEASE PROPAGATION(Binghamton University, State University of New York, 2024-05-17) Asiri, Mohammed Ali A; Lu, SusanThe recent COVID-19 pandemic has highlighted the importance of responding quickly and efficiently in the healthcare industry, especially when dealing with rapidly spreading infectious diseases. This dissertation presents an Agile Data Analytics Framework that aims to address the critical challenges observed during the COVID-19 crisis. These challenges include the need for early detection of the disease progression, which was difficult due to the initial surge in cases that outpaced the system's responsiveness. There were also limitations in the support system, which made it challenging for healthcare professionals to make triage decisions and classify the severity of cases, which is essential for operational decision-making. Finally, there were issues with process workflow monitoring, where bottlenecks of the patient treatment journey unknowingly led to delays in patient care and process inefficiencies. This framework aims to tackle those challenges to enhance the healthcare process performance and improve patient outcomes. The research has three primary objectives. Firstly, it aims to develop a conceptual agile framework that will use healthcare big data for rapid disease pattern detection, facilitate cross-departmental cooperation, and minimize manual data processing. Secondly, the research implements analytical tools, including machine learning algorithms and time series forecasting, to improve clinical decisions and risk classification. Thirdly, process mining techniques are integrated as performance indicators for healthcare processes, enabling more effective and timely healthcare delivery. The research presented within this dissertation has yielded two substantial contributions to the healthcare industry. Firstly, it has formulated an agile framework marked by its adaptability, expeditious response capabilities, and potential to enhance the responsiveness of healthcare processes in the context of infectious diseases. Secondly, it emphasizes the strategic advantages of integrating big data technology, significantly improving healthcare performance through more informed decision-making processes, and facilitating superior care quality. This dissertation comprehensively explores the Agile Data Analytics Framework's development, implementation, and potential impact on healthcare processes. It presents a transformative approach to healthcare outcomes during health crises, suggesting a novel path for leveraging agility and data analytics in combating infectious diseases' challenges.20 0Item Restricted A VALUE-BASED MODELING FRAMEWORK FOR SOLAR ENERGY UTILIZATION AND MONITORING(Saudi Digital Library, 2023-12-08) Alanizi, Muslat Abdulrahman; Jololian, LeonWe have developed and presented a value-based modeling (VBM) framework for optimal solar energy utilization and monitoring. Our model adopts a universal approach that prioritizes values to ensuring a comprehensive analysis of solar energy systems by recognizing the complexities and intricacies of the renewable energy landscape. To determine the robustness and applicability of our VBM framework, we subjected it to a real-world test through a detailed case study focusing on Net-Metering Monitoring System. This validation reinforced the model's efficacy and showcased its potential as a dynamic tool for decision-making in solar energy. Using Shannon's entropy method, we recorded the optimal efficiency in solar power usage of the case study. These results, in terms of entropy values, highlight the stable and efficient use of solar energy after the implication of our value-based modeling framework. Additionally, our model has proven highly predictive, delivering accurate forecasts for net-metering values. Such predictive accuracy emphasizes the model's potential to assist utility providers, policymakers, and consumers make informed decisions about solar energy utilization. Hence, we introduce a pioneering Value-based Modeling framework for solar energy and highlight its practical significance and potential impact in optimizing and monitoring solar energy systems. Ultimately, we encouraged a sustainable and value-driven energy future. Keywords: Value-based Modeling, Solar Energy, Monitoring, Enterprise Systems, Net-metering, Process Improvement31 0