Big Data and Machine Learning: A Way to Improve Outcomes in Population Health Management

Big Data and Machine Learning: A Way to Improve Outcomes in Population Health Management

Fernando Enrique Lopez Martinez (University of Oviedo, Spain) and Edward Rolando Núñez-Valdez (University of Oviedo, Spain)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/978-1-5225-3805-9.ch008

Abstract

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.
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Background

Population health management is the aggregation of patient data across multiple health information technology resources, the analysis of that data and the actions that care providers can use to improve clinical and financial outcomes of a group of individuals (Kindig, 2003). A population health management objective is to give real-time insights to health providers based on clinical, operational and financial data from a population and provides analytics to improve efficiency and patient care.

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