AN AGILE DATA ANALYTICS FRAMEWORK TO IMPROVE HEALTHCARE PROCESS PERFORMANCE IN INFECTIOUS DISEASE PROPAGATION

Abstract

The 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.