Modeling of a System for Managing and Analyzing Heart Failure Data
Cardiovascular diseases pose a significant threat to global health, contributing to approximately 15 million deaths worldwide. These complex illnesses are influenced by various factors, and their effective management is of utmost importance. In the pursuit of a comprehensive understanding of these conditions, the medical community has identified triggering factors that play a crucial role in monitoring patients, particularly those with Heart Failure. These factors encompass unalterable elements such as gender, age, and genetic predisposition, as well as modifiable factors like smoking, hypertension, diabetes, and alcohol consumption. Several studies have emphasized that Heart Failure patients often face substantial challenges in self-care, underscoring the need for innovative solutions. The integration of technology has become a common practice for both healthcare professionals and patients in managing various medical conditions. When applied to Heart Failure, this approach offers valuable opportunities to address the challenges associated with managing this complex condition. In this context, regular patient monitoring assumes a pivotal role in maintaining disease stability and ensuring an improved quality of life. In this study, we conducted a comprehensive analysis of the available literature with the aim of establishing essential criteria for the construction of an effective data model. This set of criteria includes critical prognostic variables for the management of Heart Failure patients. Based on these well-defined requirements, this study describes the implementation of a relational database designed to manage information from a system dedicated to monitoring individuals affected by this complex medical condition.