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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