Abstract

Healthcare in the past few decades has been greatly shaped by technology and this has greatly affected the way data is produced and used. Health care now is in the period recognized as the ‘big data’ period when the huge amounts of health-related data from various sources are collected and combined. These data include EHR and genomic data, wearable health devices, and SDS which present tremendous opportunities to reimagine patient care and service. They all posed great relevance to change in healthcare provision, and big data analytical technology has become a primary tool in this transformation through this development of predictive model instruments in improving the health care decisions of clients. Forecasting, one of the sectors in the big data analytics field, depends on the translation of big data into usable information. The results show that through the utilization of historical and real-time data, predictive models can predict the outcome of a given patient, and possible at-risk cofactor populations, with view to developed targeted interventions. That is, in chronic disease management, we get outcomes such as intervention at an earlier stage and thus control of the disease and costs. In addition, new machine learning approaches have proven to have an incredible potential feature in such significant clinical indicators as hospital readmissions, the severity of the disease, and even therapeutic outcomes. These are transformative capabilities in health care from the traditional curative mode to preventive mode