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Home / Volume 03, Number 01, April 2017 / BIG DATA IN HEALTHCARE: REVIEW AND OPEN RESEARCH ISSUES

BIG DATA IN HEALTHCARE: REVIEW AND OPEN RESEARCH ISSUES

(Received: 2016-10-18, Revised: 2016-12-29 , Accepted: 2017-01-21)
Mohammad Ashraf Ottom,
#Big data #Healthcare #Hadoop #Google flu trends #Big data challenges #Cycle of big data management.
The globe is generating a high volume of data in all domains, such as social media, industries, stock markets and healthcare systems. Most of data volume has been generated in the past two years. This massive amount of data can bring benefits and draw knowledge to individuals, governments and industries and assist in decision making. In healthcare, an enormous volume of data is generated from healthcare providers and stored in digital systems. Hence, data are more accessible for reference and future use. The ultimate vision for working with health big data is to support the process of improving the quality of service in healthcare providers, reducing medical mistakes and providing a promoting consultation in addition to providing answers when needed. This paper provides a critical review of some applications of big data in healthcare, such as the flu-prediction project by the Institute of Cognitive Sciences, which combines social media data with governmental data. The project aim is to provide swift response about flu-related questions. The project should study human multi-modal representations, such as text, voice and images. Moreover, integrating social media data with governmental health data could create some challenges, because governmental health data are considered as more accurate than subjective opinions on social media. Another attempt to utilize big data in healthcare is Google Flu Trends GFT. GFT collects search queries from users to predict flu activity and outbreak. GFT performed well for the first two to three years; however, it started to perform worse since 2011 due to people behaviour changes. GFT did not update the prediction model based on new data released by the Centre for Disease Control and Prevention-US (CDC). On the other hand, ARGO (Auto Regression with Google) performed better than all previously available influenza models, because it adjusts people behaviour changes and relies on current publicly available data from google-search and CDC. This research also describes, analayzes and reflects the value of big data in healthcare. Big data has been introduced and defined based on the most agreed terms. The paper also explains big data revenue forecast for the year 2017 and historical revenue in three main domains: services, hardware and software. Big data management cycle has been reviewed and the main aspects of big data in healthcare (volume, velocity, variety and veracity) have been discussed. Finally, a discussion has been made of some challenges that face individuals and organizations in the process of utilizing big data in healthcare, such as data ownership, privacy, security, clinical data linkage, storage and processing issues and skills requirements.
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DOI 10.5455/jjcit.71-1476816159
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