Investigating the Adoption of Big Data Management in Healthcare in Jordan

Abstract

Software developers and data scientists use and deal with big data to easily discover useful knowledge and find better solutions to improve healthcare services and patient safety. Big data analytics (BDA) is getting attention due to its role in decision-making across the healthcare field. Therefore, this article examines the adoption mechanism of big data analytics and management in healthcare organizations in Jordan. Additionally, it discusses health big data’s characteristics and the challenges, and limitations for health big data analytics and management in Jordan. This article proposes a conceptual framework that allows utilizing health big data. The proposed conceptual framework suggests a way to merge the existing health information system with the National Health Information Exchange (HIE), which might play a role in extracting insights from our massive datasets, increases the data availability and reduces waste in resources. When applying the framework, the collected data are processed to develop knowledge and support decision-making, which helps improve the health care quality for both the community and individuals by improving diagnosis, treatment, and other services.

The challenges and opportunities of adopting artificial intelligence (AI) in Jordan’s healthcare transformation

“…The 'Hakeem' health program (e-health solution) is adopted by public health sectors where the health data from over five million patients have been collected and stored in an electronic format. The multi-systems are used by the private sectors and computerized (Bani-Salameh, Al-Qawaqneh and Taamneh, 2021) . There are different types of data such as structured (e.g., laboratory tests), semi-structured (e.g.…”

“…Health framework in Jordan develops quick, and it includes huge and varying sensitive data. But still it needs to be stored, arranged, managed, and analyse efficiently and with productivity which AI can only do that (Bani-Salameh, Al-Qawaqneh and Taamneh, 2021) . AI gathers valuable health data and get insights from the raw data.…”

A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity Machines

“…The MapReduce parallel programming model has recently gained attention for running the k-means algorithm on a cluster of machines. Apache Hadoop is an open-source framework for running applications on clusters of commodity machines, using the MapReduce programming model [19,20]. Hadoop was the most widely used MapReduce-based framework for clustering large data sets in a distributed fashion [21][22][23][24][25][26][27].…”