Potential and Adoption of Data Science in the Healthcare Analytics

The creation and validation of clinical practice predictive models is just the initial step in the path towards mainstream adoption of predictions for real-time point-of-care. Adoption of healthcare analytics can occur at diverse levels, including medical error tracking and avoidance, data integration, predictive analysis and personalized modelling. Although substantial advancement and progress has been made from the perspective of data science and study, challenges and opportunities remain. Current main fields of study can be categorized according to the organisation, introduction, and assessment of health information systems, patient information representation, and analysis and interpretation of underlying signals and data. We should anticipate many shifts in future medical informatics science in view of the fluid existence of many of the driving factors behind advancement in knowledge management methods and their technology, developments in medicine and health care, and the constantly shifting demands, requirements and aspirations of human populations. This chapter will explain the relevance of the application of predictive analytics strategies focused on data science in healthcare. By way of intelligent process analysis and medical data mining, the device would be able to derive real time valuable information that aids in decision making and medical tracking.