Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

Abstract

The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools.

Development and Usability Testing of an Emergency Alert Device for Elderly People and People with Disabilities

“…e points of analysis from the literature review and analysis of recent alert devices on the ai market [7][8][9] were general appearance of the device, functions, and usability by elderly people and people with disabilities in the community. In terms of general appearance, this study found that most alert devices had at least two parts: a signal sending part and signal receiving part.…”

“…erefore, various types of emergency alert devices have been developed recently for dependent people, including children, people with disabilities, sick patients, and the elderly. e purpose of these devices is to locate the persons involved and monitor their safety and security and increase their quality of life [9][10][11]. Most of them send out an alarm only when help is requested, but some are developed to monitor the health of sick patients such as their blood pressure, heart rate, and so on.…”

“…Most of them send out an alarm only when help is requested, but some are developed to monitor the health of sick patients such as their blood pressure, heart rate, and so on. ese health records and daily activity data are collected usually in digital format and analyzed for detecting the risk of serious health problems and sharing between health professionals [9,11]. In addition, current technologies are able to use sensors, real-time cameras, and smart phones as supplementary alert devices for a surveillance system [9,12,13].…”

Big Data Analytics and Processing Platform in Czech Republic Healthcare

“…Other available big data scalable technologies and frameworks [69][70][71][72][73] include Hadoop, an open-source ecosystem (with proprietary file system HDFS); and the Java-based MapReduce framework for storing and batch processing of large amounts of data. Apache Spark is also designed to fit well within big data ecosystems.…”

Fuzzy-Based Symmetrical Multi-Criteria Decision-Making Procedure for Evaluating the Impact of Harmful Factors of Healthcare Information Security

“…It includes various data repositories and cloud storage that are used inside the healthcare organization or outside the healthcare organization. Securing this layer needs extra efforts and sensitivity because storage and transfer layer has direct data access, i.e., if an attacker gets access to the database 1, then the possibilities are very high that he can breach the security of other databases of the organization [31]. That is the main reason why authors recommend extra security and authentication approaches in this layer.…”