Abstract

Currently, Personalized Precision medicine and Personalize Healthcare required simultaneously accomplished the following four conditions, to be: predictive, preventive, personalize and participatory. Personalized Precision medicine (PPM) is an emerging approach for disease treatment and prevention that include individual variability in genes, environment and lifestyle for each person. It is also pointed out that PPM gives clinicians tools to better understand the complex mechanism underlying a patient´s health, disease or condition and to better predict which treatment will be most effective. PPM includes several benefits such as: a) ensure that people get the correct treatment every time; b) enable biomarkers guided therapy; c) the emphasis is in prevention not reaction; d) improved patient outcomes; e) may or may not result in drug discovery and f) offer less cost effective and more efficient health care. In parallel, present ongoing challenges from regulation, reimbursement, clinical adoption to the economic value of the data. PPM has the ability to link large scale genomics, other omics, biomedical imaging, data with large scale electronic patient health care records, e-record and big data. This integrative approach is being made in targeted therapies for respiratory, cardiovascular /metabolic and neurological disease. Personalize healthcare (PH) historically has been used in the pharmaceutical industry focus on treatment and the association between molecular markers, companion or complementary diagnostics and targeted therapies. Big data analytics are supposed to be the cure-all for the healthcare industry, closing the gap between what is expected of clinicians and how well they can actually perform. Despite the proliferation of digital documentation, predictive analytics and risk scoring technologies, healthcare organizations are still struggling with the basics of health information exchange and EHR interoperability where new protocols like FHIR or day-to-day information sharing is still in its infancy. Healthcare is the largest sector in the largest economy in the world and we are at the stage of beginning to integrate the digital record of what has happened to our patients, together to get an end-to-end look across the service line. The demand of Data Science (Ds) in industry, academia, and government is rapidly growing. We already mentioned above how transform PPM and PH. Ds is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. Also, Ds is a set of fundamental principles that guide the extraction of knowledge from data. Ds involved principles, process and techniques for understanding the phenomena via (automated) the analysis of data and its goal is to improve the decision making. Data driven decision making (DDD) refers to the practice of basing decision on the analysis of data and is the focal point for PPM and PH in the present. The adoption of big data analytics, PPM and digital health innovations will change healthcare delivery and increase quality care. In this Tutorial we will: A) analyze the development of PPM and PH along the world; B) analyze the PPM benefits with specific examples for each of them; C) analyze the challenges from regulation to the economic value of the data in PPM; D) analyze the PPM strategy for products, and services across diagnostics, life science, companies and medical institutions; E) example of integrative PMM approach considering the link between omics, imaging, data with e-record and big data in targeted therapies; F) analyze data driven treatment and predictive medicine incorporating DDD; G) analyze the current situation with health information exchange and EHR interoperability like FHIR; H) analyze the clinical guidelines and standardized processes considering patient engagement, big data, remote care including telehealth or virtual care; I) analyze Ds evolution and the influences on PPM and PH from the Ds point of view; J) defined data mining together with Ds to improve business decision, specifically in PPM and PH; K) analyze the role of What is Machine Learning and Data Munging in PPM and L) analyze how the adoption of big data analytics, PPM and digital health innovations will change healthcare delivery and increase quality care.