Special Issue on Big Data and e-Health - 13 - I. Introduction W hen managing a healthcare center, there are many key performance indicators (KPIs) that can be measured, such as the number of events, the waiting time, the number of planned tours, etc. Often, keeping these KPIs within the expected limits is key to achieve high users’ satisfaction. In this paper we present DataCare, a solution for intelligent healthcare management. DataCare provides a complete architecture to retrieve data from sensors installed in the healthcare center, process and analyze it, and finally obtaining relevant information which is displayed in a user-friendly dashboard. The advantages of DataCare are twofold: first, it is intelligent. Besides retrieving and aggregating data, the system is able to predict future behavior based on past events. This means that the system can fire early alerts when a KPI in the future is expected to have a value that falls outside the expected boundaries, and to provide recommendations for improving the behavior and the metrics, or in order to prevent future problems attending events. Second, the core system module is built over a Big Data Platform. Processing and analysis are run over Apache Spark, and data are stored in MongoDB, thus enabling a highly scalable system that can process very big volumes of data coming at very high speeds. This article is structured as follows: section II will present a context of this research by analyzing the state of the art and related work. Section III will present an overview of DataCare’s architecture, including the three main modules responsible for retrieving data, processing and analyzing it, and displaying the resulting valuable information. Sections IV, V and VI will describe the preprocessing, processing and analytics engines in further detail. The design of these systems is crucial to provide a scalable solution with an intelligent behavior. Section VII describes the visual analytics engine, and the different dashboards that are presented to users. Finally, section VIII describes how the solution has been validated, and section IX provides some conclusive remarks along with potential future work. II. State of the Art Because healthcare services are very complex and life-critical, many works have tackled the design of healthcare management systems, aimed at monitoring metrics in order to detect undesirable behaviors that decrease their satisfaction or even threaten their safety. The design and implementation of healthcare management system is not new. Already in the 2000s, Curtright et al. [4] describe a system to monitor KPIs summarizing them in a dashboard report, with a real- world application in the Mayo Clinic. Also, Griffith and King [7] proposed to establish a “championship” where those healthcare systems with consistently good metrics will help improve decision processes. Some of these works explore the sensing technology that enable proposals. For instance, Ngai et al. [11] focus on how RFID technology can be applied for building a healthcare management system, yet it is only implemented in a quasi-real world setting. Ting et al. [13] also focus on the application of RFID technology to such a project, from the perspective of its preparation, implementation and maintenance. Some previous works have also tackled the design of intelligent healthcare management systems. Recently Jalal et al. [8] have proposed an intelligent depth video-based human activity recognition system to track elderly patients that could be used as a part of a healthcare management and monitoring system. However, the paper does not DataCare: Big Data Analytics Solution for Intelligent Healthcare Management Alejandro Baldominos 1 *, Fernando De Rada 2 , Yago Saez 1 1 Computer Science Department, Universidad Carlos III de Madrid, Leganés (Spain) 2 Camilo José Cela University, Madrid (Spain) Received 15 July 2016 | Accepted 3 February 2017 | Published 27 March 2017 Keywords Architecture, Artificial Intelligence, Big Data, Healthcare, Management. Abstract This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster- computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction. DOI: 10.9781/ijimai.2017.03.002 * Corresponding author. E-mail address: abaldomi@inf.uc3m.es