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| {{Infobox journal article
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| |caption = | | | text = This is sublevel13 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p> |
| |title_full = DataCare: Big data analytics solution for intelligent healthcare management | |
| |journal = ''International Journal of Interactive Multimedia and Artificial Intelligence'' | |
| |authors = Baldominos, Alejandro; de Rada, Fernando; Saez, Yago
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| |affiliations = Universidad Carlos III de Madrid, Camilo José Cela University
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| |contact = Email: abaldomi at inf dot uc3m dot es
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| |pub_year = 2018
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| |vol_iss = '''4'''(7)
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| |pages = 13–20
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| |doi = [http://10.9781/ijimai.2017.03.002 10.9781/ijimai.2017.03.002]
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| |issn = 1989-1660 | |
| |license = [https://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported]
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| |website = [http://www.ijimai.org/journal/node/1621 http://www.ijimai.org/journal/node/1621]
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| |download = [http://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_2_pdf_16566.pdf http://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_2_pdf_16566.pdf] (PDF)
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| ==Abstract==
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| 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 rapid speeds 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.
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| '''Keywords''': Architecture, artificial intelligence, big data, healthcare, management
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| ==Introduction==
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| When 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 vital to achieving high user satisfaction.
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| 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 obtain relevant information, which is displayed in a user-friendly dashboard.
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| 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 is expected to have a future value that falls outside the expected boundaries, and it can provide recommendations for improving the behavior and the metrics, or prevent future problems with attending events.
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| Second, the core system module is built on top of 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 large volumes of data coming in at very high speeds.
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| This article will discuss many aspects of DataCare. The next section will present context for this research by analyzing the state of the art and related work. After that an overview of DataCare’s architecture will be presented, including the three main modules responsible for retrieving data, processing and analyzing it, and displaying the resulting valuable information.
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| After the architecture has been explained, the subsequent three sections will describe the preprocessing, processing, and analytics engines in further detail. The design of these systems is crucial to providing a scalable solution with an intelligent behavior. After discussing those engines in detail, the article will then describe the visual analytics engine and the different dashboards that are presented to users.
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| Finally, the penultimate section will describe how the solution has been validated, and the last section will provide some conclusive remarks, along with potential future work.
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| ==State of the art==
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| ==References==
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| {{Reflist|colwidth=30em}}
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| ==Notes==
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| This presentation is faithful to the original, with only a few minor changes to presentation. Grammar has been updated for clarity. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.
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| [[Category:LIMSwiki journal articles (added in 2018)]]
| | <div class="nonumtoc">__TOC__</div> |
| [[Category:LIMSwiki journal articles (all)]]
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| [[Category:LIMSwiki journal articles on big data]]
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| [[Category:LIMSwiki journal articles on health informatics]]
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