Modern data processing has brought data streaming architecture to become a vital element that processes data in real-time, which the traditional batch processing approach cannot. With the amount of data that businesses, especially businesses in the healthcare sector, produce, real time processing and analysis of it is necessary. In this work, we investigate the advantages and disadvantages of data streaming architecture under consideration of healthcare applications. This study focuses on the implementation and effectiveness of data streaming architecture in healthcare settings as the major goal of the work. More specifically, its objective is to understand how the use of data processing could contribute to patient care to save costs and improve operational efficiency. It then hypothesizes that real-time insight, cost of use, and scalability make data streaming architecture so superior to traditional batch processing. Using case studies in two areas of healthcare, real time monitoring of patient vital signs and predictive maintenance of medical equipment and real time mode of wearable physiologic data from chronic disease cases, this study investigates the application of a data streaming architecture. The study design is a qualitative analysis of the challenges the planners faced during implementations, lessons learned, and outcomes realized. Quantitative methods are applied to the quantification of operational efficiency cost savings improvements, and patient outcomes. The study shows that data streaming architecture provides a significant advantage in the case of healthcare applications. Take as an example, it could enable nearly real-time monitoring of patient vital signs, enabling prompt medical intervention and resulting in a great improvement in patient outcomes. Medical equipment was predicted to be maintained, which led to cost savings and reduced the downtimes of the equipment. Wearable device data analysis in real time is conducted to come up with personalized patient recommendations that improve the management of the chronic disease. Statistical data presented the support of these findings in the improvements in operational efficiency and cost reductions. The study concludes that the data streaming architecture technology has the potential to transform healthcare in a real time, cost-effective and scalable manner. The results presented here have relevance to both enhanced patient care, operational cost savings, and increased efficiency in the case studies. Finally, future research should aim at addressing these unresolved challenges in privacy, accuracy, and scalability so that the power of the data streaming architecture could be fully utilized in other data-intensive industries including healthcare.
