Big Data Assignment | Healthcare Assignment

Running head: GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Guide to Big Data Applications in Healthcare
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1GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Abstract
Big data is generated everyday through communications with millions of individuals
employing GPS devices, cell phones, medical devices, sensors and data intensive aspects like
healthcare and atmospheric science. The paper focused in investigating the likely effect of big
data on healthcare information systems. It explored that other than improving profits and
decreasing wasted overheads, in healthcare Big Data is employed for anticipating epidemics,
cure disease, avoid preventable deaths and enhance life quality. It is also gathered that the
healthcare organizations on the efficiency forefront continues to gather benefits from big data
that helps them in developing value-conscious medicines and personalized treatment.
Introduction
Due to information explosion, big data is deemed to be among the most discussed topics
in healthcare information systems recently. The government of US also announced initiative for
big data research and development of around 200 million in 2012. 1 Such initiate ensures the
possibility of employing big data within large scale database for dealing with considerable
concerns faced by the government. Big data is deemed identical to business analytical and
intelligence but the data scale is larger. In such scenario, dimensions of three “v” are employed
in describing big data that includes variability, velocity and volume.
1 Belle, Ashwin, et al. "Big data analytics in healthcare." BioMed research
international 2015 (2015).

2GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
There is a traditional relationship between business intelligences and healthcare
information systems. There are several reasons that results in big data evolution and one among
them is increase of in-memory computing. 2 Traditional computers encompass secondary storage
devices such as hard drives and entrap processing unit (CPU).
Figure 1: Traditional Relationship between Healthcare Information Systems and
Business Intelligence
(Source: 3)
Transfer of data takes place among the secondary storage and the CPU during data
processing. This serves as an economical manner to employ computers, processing speed of the
2 Bello-Orgaz, Gema, Jason J. Jung, and David Camacho. "Social big data: Recent
achievements and new challenges." Information Fusion 28 (2016): 45-59.
3 Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and
big data." IEEE Systems Journal 11.1 (2017): 88-95.

3GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
computers is likely to be decreased. Oracle and SAP Hana develops new computer systems that
utilizes in-memory systems. 4 For this reason, today’s computer systems are able to deal with
large-scale data than before. Another reason for rise in big data is social networking. Within
business intelligence and traditional data mining, the data is relied on internal data developed
from internal enterprise resource planning systems (ERP) or healthcare information systems.
Such data employed in these systems are defined as structured data that is limited. Moreover,
popularity of social networking is increasing for over six years that has already presented a huge
amount of big data that is helpful in data analysis. Such external data or certain unused internal
data is deemed as unstructured information. Big data includes structured and unstructured data
derived from interval unused data, external data resources and social media. 5
Reasons for Increased Complexity of Healthcare Data
Several big data challenges are faced in healthcare information systems that include
inferring knowledge from difficult heterogeneous patient sources along with leveraging patient
data correlations in longitudinal records. Big data challenges are also present in understanding
unstructured clinical notes in correct context.6 Issues are also present in efficiently dealing with
4 Chen, Min, et al. "Smart clothing: Connecting human with clouds and big data for
sustainable health monitoring." Mobile Networks and Applications 21.5 (2016): 825-845.
5 Gandomi, Amir, and Murtaza Haider. "Beyond the hype: Big data concepts, methods,
and analytics." International Journal of Information Management 35.2 (2015): 137-144.
6 George, Gerard, Martine R. Haas, and Alex Pentland. "Big data and
management." Academy of Management Journal 57.2 (2014): 321-326.

4GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
large volumes of medical imaging data along with gathering likely biomarkers and helpful
information. Evaluating genomic data serves as a computationally intensive task along with
getting associated with standard clinical data adds several complexity layers. Another change
associated with big data in healthcare includes gathering patients’ behavioral data through
numerous sensors along with several communications and social interactions.
Big data can be understood as collection of complex and large data sets that are difficult
to process through employing common management techniques or traditional data processing
applications.7 Big data can also be referred as processes, procedures and tools that facilitate
healthcare organizations in manipulating, generating and managing large data sets along with
storage facilities. Big data in healthcare is focused on recognizing insights from longitudinal,
complex, voluminous and heterogeneous data that intends to answer questions those were
unanswered previously. In such case, the challenges encompass storing, gathering, sharing,
searching and evaluating.
7 Groves, Peter, et al. "The'big data'revolution in healthcare: Accelerating value and
innovation." (2016).

5GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Figure 2: Four V’s of Big Data
(Source: 8)
The reasons for which abundance or complexity of healthcare data is increasing are
explained below:
 Increased incentives to professionals and hospitals to using HER technology
 Standard medical practices changing from being ad-hic to subjective decision making
considering evidence based healthcare
 New technologies development too place such as sensors, capturing devices and mobile
applications.9
 Gathering of genomic information turned out to be cheaper
8 Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and
big data." IEEE Systems Journal 11.1 (2017): 88-95.

6GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
 Patient social communications in digital forms are observed to increase
 High medical knowledge or discoveries are being gathered
Relevance of Big Data Analytics in Healthcare
Big data analytics facilitates in attaining advantage from huge amounts of data long with
offering appropriate intervention to the right patient at a correct time. It also facilitates healthcare
systems in offering personalized care to patients along with offering potential benefits to all
aspects of healthcare system that includes payer, provider, management and patient. Objectives
of big data analytics in healthcare focuses on introducing data mining researchers to the available
sources along with likely challenges and techniques related with employing big data in
healthcare system.10 Big data analytics also considers introducing healthcare practitioners and
analysts to the developments within computing field in efficiently dealing and making
involvements from heterogeneous and voluminous healthcare data. Healthcare facilities consider
offering highly proactive care to patients through regularly monitoring patients’ vital signals.
Data gathered from several monitors might get evaluated in real time along with sending alerts
for caring providers so that they realize instantly regarding alterations in condition of patients.
Processing real-time events with support of machine learned algorithms might offer physicians
with viewpoints that can help them to take life saving decisions along with facilitating efficient
9 Hu, Han, et al. "Toward scalable systems for big data analytics: A technology
tutorial." IEEE access 2 (2014): 652-687.
10 Kambatla, Karthik, et al. "Trends in big data analytics." Journal of Parallel and
Distributed Computing 74.7 (2014): 2561-2573.

7GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
interventions. Real-time monitoring alters nature of relationship as face-to-face care is not that
important.
Figure 3: Big Data Analytics in Healthcare
(Source: 11)
Major objective of big data analytics in healthcare is to cover data mining and medical
informatics communities for fostering interdisciplinary works among two communities. The
healthcare industry focuses on employing big data technologies. Big data analytics has increase
relevance in healthcare industry for the reason that it offers value based and patient focused care.
An objective of modern healthcare systems is to offer optimal healthcare by efficient application
of health information technology.12 This will be for decreasing the healthcare expenses and
avoidable overuse along with offering support for re-developed payment structures. Moreover,
11 Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and
big data." IEEE Systems Journal 11.1 (2017): 88-95.
12 Kayyali, Basel, David Knott, and Steve Van Kuiken. "The big-data revolution in US
health care: Accelerating value and innovation." Mc Kinsey & Company 2.8 (2013): 1-13.

8GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
the expenses of waste, fraud and abuse within healthcare industry are a major factor resulting in
increased healthcare costs in US. In such scenario, big data analytics might turn out to be a game
changer in healthcare fraud. The centres for Medicaid and Medicare services prevented around
$210.7 million within healthcare fraud within a year through employing predictive analytics.
United healthcare changed to predictive modeling surrounding that is relied on Hadoop platform
of big data for recognizing inefficient claims in repeatable and systematic way along with
generating high return on advanced technology or big data.13
Use of Big Data in Healthcare Information Systems
An efficient instance of real-world case in data analysis was provided when it was
revealed that the retail giant target can recognize whether a person is sick through tracking his
behavior. With the help of such data target will be capable to anticipate her future consumption
of certain emergency disease specific medicines. It examined the ways in which Target was able
to analyze sickness of a person through employing data mining tools. Moreover, as Target
employed a statistician in employing data mining techniques for anticipating consumer behavior,
the revenue increased drastically.
13 Luo, Jake, et al. "Big data application in biomedical research and health care: A
literature review." Biomedical informatics insights 8 (2016): 1.

9GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Figure 4: Big Data Use in Healthcare Information Systems
(Source: 14)
It has been observed that there is an increased potential for big data within healthcare
information systems. For such mainframe based business intelligence or programs of data mining
like SAP BW or SAS must have the capability to get upgraded in dealing with big data
evaluation.15 Numerous ERP or healthcare system vendors log with key IT companies such as
Microsoft, IBM, SAP, SAS and Oracle have already worked on several big data projects.
14 Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and
big data." IEEE Systems Journal 11.1 (2017): 88-95.
15 Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare:
promise and potential." Health information science and systems 2.1 (2014): 3.

10GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Research within particular big data applications remains within early stage and under
development but numerous general applications is taking place. Within biology aspects, big data
has turned out to be innovative technological tool for genomics. It is also confirmed that
biologists employ big data for analyzing all the aspects from genes regulation along with
genomes evolution in consideration to the reasons for which coastal algae bloom, the microbes
that are present in human body cavities and the ways in which genetic makeup of distinct cancers
effects the ways in which cancer patients fare. It took almost twelve years for Human Genome
Project in evaluating, gathering along with interpreting a great data amount required to develop a
map regarding genes.16 However, this might take a lot of years for Human Genome Project in
evaluating, gathering and interpreting a great data amount required to develop a map of
numerous genes but it might take just a day for employing new big data technologies in attaining
similar results.
16 Roski, Joachim, George W. Bo-Linn, and Timothy A. Andrews. "Creating value in
health care through big data: opportunities and policy implications." Health affairs 33.7
(2014): 1115-1122.

11GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Figure 5: Complex Big Data Analytics in Healthcare
(Source: 17)
Big data can be employed in pharmaceutical development cycle within areas such as
clinical monitoring, genomics and pharmacovigilliance. A novel system such as Collaborative
assessment and Recommendation Engine (CARE) in order to predict risk of personalized
disease. Big data also supports storage along with processing of medical imaging data. Big data
healthcare serves as a drive for capitalizing on increasing patient and health system data
accessibility in order to develop healthcare innovation.18 Through making smart application of
17 Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and
big data." IEEE Systems Journal 11.1 (2017): 88-95.
18 Wang, Yichuan, LeeAnn Kung, and Terry Anthony Byrd. "Big data analytics:
Understanding its capabilities and potential benefits for healthcare

12GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
increasing amount of available data, new insights can be found through re-examining data or
combining with other important data. In healthcare this involves mining patient records,
biobanks, medical images and test results for diagnosis, insights and decision support advice.
This also involves regular evaluation of data streams developed for all the patients, doctors’
office, at home and on move through mobile devices.
Conclusion
The paper focused in investigating the likely effect of big data on healthcare information
systems. From the findings it was gathered that big data challenges is present in understanding
unstructured clinical notes in correct context. Issues are also present in efficiently dealing with
large volumes of medical imaging data along with gathering likely biomarkers and helpful
information. There are several reasons that results in big data evolution and one among them is
increase of in-memory computing. Big data analytics facilitates in attaining advantage from huge
amounts of data long with offering appropriate intervention to the right patient at a correct time.
It also facilitates healthcare systems in offering personalized care to patients. It was also revealed
that gig data in healthcare remains focused on recognizing insights from longitudinal, complex,
voluminous and heterogeneous data that intends to answer questions those were unanswered
previously.
References
Belle, Ashwin, et al. "Big data analytics in healthcare." BioMed research international 2015
(2015).
organizations." Technological Forecasting and Social Change (2016).

13GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Bello-Orgaz, Gema, Jason J. Jung, and David Camacho. "Social big data: Recent achievements
and new challenges." Information Fusion 28 (2016): 45-59.
Chen, Min, et al. "Smart clothing: Connecting human with clouds and big data for sustainable
health monitoring." Mobile Networks and Applications 21.5 (2016): 825-845.
Gandomi, Amir, and Murtaza Haider. "Beyond the hype: Big data concepts, methods, and
analytics." International Journal of Information Management 35.2 (2015): 137-144.
George, Gerard, Martine R. Haas, and Alex Pentland. "Big data and management." Academy of
Management Journal 57.2 (2014): 321-326.
Groves, Peter, et al. "The'big data'revolution in healthcare: Accelerating value and innovation."
(2016).
Hu, Han, et al. "Toward scalable systems for big data analytics: A technology tutorial." IEEE
access 2 (2014): 652-687.
Kambatla, Karthik, et al. "Trends in big data analytics." Journal of Parallel and Distributed
Computing 74.7 (2014): 2561-2573.
Kayyali, Basel, David Knott, and Steve Van Kuiken. "The big-data revolution in US health care:
Accelerating value and innovation." Mc Kinsey & Company 2.8 (2013): 1-13.
Luo, Jake, et al. "Big data application in biomedical research and health care: A literature
review." Biomedical informatics insights 8 (2016): 1.
Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and
potential." Health information science and systems 2.1 (2014): 3.

14GUIDE TO BIG DATA APPLICATIONS IN HEALTHCARE
Roski, Joachim, George W. Bo-Linn, and Timothy A. Andrews. "Creating value in health care
through big data: opportunities and policy implications." Health affairs 33.7 (2014):
1115-1122.
Wang, Yichuan, LeeAnn Kung, and Terry Anthony Byrd. "Big data analytics: Understanding its
capabilities and potential benefits for healthcare organizations." Technological
Forecasting and Social Change (2016).
Zhang, Yin, et al. "Health-CPS: Healthcare cyber-physical system assisted by cloud and big
data." IEEE Systems Journal 11.1 (2017): 88-95.