Transforming Chronic Disease Data Into Actionable Knowledge: A Comprehensive Systematic Review For Data Science Models In Healthcare Knowledge Management

The increasing prevalence of chronic disease, responsible for high mortality and disability, is of considerable concern to many organizations. The ongoing care and management re-quirements contribute to excessive healthcare costs which affect economies worldwide. Patients with chronic diseases require understanding of the existing care models to address their complex and involved health needs. Chronic Disease Data (CDC) points out that providing data to guide, prioritize, deliver and monitor population health is a vital domain to improve Chronic Disease Management. The twenty-first century led to a new age of Data Science and Analytics. Big data has become a core technology for providing innovative solutions in numerical applications and services in healthcare. Embedded in big data is val-uable information and knowledge. That’s when Data Science becomes an important tool to transform actionable data into knowledge which is a required tread to make better decisions, solve complicated problems and optimize health processes. This aims for a Data Science Model as a Health Knowledge Management System tool, which emerges as an important paradigm for innovating healthcare. KMS depends on developing more adaptive, intelligent and user-centric systems that can effectively manage the increasingly dense healthcare knowledge ecosystem. Under this context, is also a critical need to develop Healthcare Intel-ligent Systems based on Data Science. This article provides a comprehensive overview of KMS and HIS, synthesizing current research and highlighting the critical role of these sys-tems in modern healthcare. It also breaks down the key concepts, advances, challenges and solutions concerning Data Science as an interdisciplinary field.