Comprehensive Analysis: Big Data's Profound Impact on Healthcare

This report provides a comprehensive analysis of the impact of big data on the healthcare sector. It begins with an overview of big data, its characteristics, and the opportunities it presents for organizations. The report then delves into the specific applications of big data within healthcare, highlighting the advantages and challenges associated with its implementation. It explores various tools and techniques, such as regression analysis, used in big data management and analysis. The report also addresses critical issues like data security, privacy, and the ethical considerations surrounding the use of big data in healthcare. The analysis includes real-world examples, emphasizing how big data is transforming healthcare practices, patient care, and organizational performance. Overall, the report offers insights into the benefits and potential drawbacks of big data, providing a balanced perspective on its role in shaping the future of the healthcare industry.

Impact of Big Data on Healthcare Sector
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INTRODUCTION
Without a doubt, in the past years, the majority of business organisations have started
heavily relying on data and information not only to survive in this competitive environment but
also to compete with other business organisations. In fact, these data and information are
believed to be the most critical asset for business organisations for the reason that they make use
of these data to drive usage patterns by which they take effective decisions. Big data is a very
important development in the realm of information technology. In describing Big Data, it is
important to mention that worldwide data storage, management, and transaction rates have
increased several times in the last 20 years (Baker, Fletcher, Garvey, & Sweazy, 2015). Big Data
is a novel data management technology that is capable of including very large data sets. Big Data
can significantly rapidly capture, process, and manage enormous amounts of data having varied
complexities and purposes. Thus, it has profound effect on scientific research, business
intelligence, weather forecasting, etc. Distinguishing characteristics of Big Data are its
capabilities of circumnavigating data utilization and limitation issues. It deploys highly cohesive
methods for data nomenclature and architecture. It also deploys dedicated and powerful
processors just for data storage and retrieval functions. While normal range of handling data sets
varies from megabytes to gigabytes, Big Data can handle data sets varying in the range of
terabytes and pentabytes. However, functioning of Big Data at such an enormous scale of
database management may have some unwanted impact. It often becomes highly difficult to
detect privacy violations or flaws in data capturing methods while operating Big Data. Also,
because of latest tools and technologies such as the Internet, this world has turned into the
information based age ("BID 2017 program schedule," 2017). There is a massive amount of data
available on the Internet. This report presents an analysis of big data. The primary purpose of this

research is to give an overview of big data and how the organisations can use it for the
betterment of their organisational tasks. This report will start with an introduction of big data.
After that, a general discussion will provide big data and its associated aspects. In the start this
report discusses the general concepts associated with big data however after that a detailed
analysis will be provided on the impact of big data on a specific organisation. In this scenario,
this report will present a detail discussion of the effects of big data on the healthcare sector (Big
data and big challenges for law and legal information, In Jayasuriya, & Georgetown University,
2015).
AN OVERVIEW OF BIG DATA
Basically, “the term big data is normally used as a marketing concept refers to data sets
whose size is further than the potential of normally used enterprise tools to gather, manage and
organise, and process within an acceptable elapsed time.”. In fact, the size of these enormous
data sets is believed to be a continually growing target. Additionally, the size of big data is
presently ranging from a few dozen terabytes to some petabytes of data in a single data set.
Given the fact, this era is known as the age of information and communication technology in
which everything appears in digital format, and as a result, everything comes under the domain
of data. For instance, in the medical sector, an electrocardiogram is now used in a digital form
which can be collected and stored as a dataset and information (attained after the processing of
these data). In the same way, MRIs, CT scans and a variety of medical images are at the present
digital, and these unique digital records and files are being stored and processed in the form of
datasets. Hence, thousands and thousands of distinct datasets are adding up to the big data
(Ebeling, 2016).

OPPORTUNITIES OFFERED BY THE BIG DATA
At present, with the big data, the majority of business organisations and retailers make
use of data more efficiently to produce planned decisions that commence with the customer and
help to develop a more thorough design process. Also, “this analytics-driven design can intensify
major touch points all the way through the customer experience at the same time as improving
sales beneficially” (Eberhardt, n.d.)
The research has shown that the organisations that use big data for their business can be
familiar with their customers and the way they communicate with the company and shop online
much better than many of those customers can be familiar with themselves. In fact, these datasets
are not only the enormous volumes of data but also they provide the organisations with excellent
ways to determine and keep records of their transactions as well as other communications with
suppliers, retailers, banks, utilities and service providers. In addition, at present there have
emerged a number of algorithms which can be applied on these data sets to determine their
customers’ behaviors, shopping patterns, usage of sales coupons and how the business
organization performs transactions and certain tasks are recorded and analyzed with the purpose
of getting a broad and effective depiction of who your customers are and what products you
should take the chance to offer them. In their research article, (Feinleib, 2014) discusses an
example in which Portland Oregon Savory Spice Shop owners Jim Brown and Anne have
decided to put into practice social media-based marketing and advertising with the intention of
getting “the best of big data's” support and capabilities for launching their new boutique store. In
this scenario, by making use of their Facebook ads, they have been capable of routing to catch
the attention of those potential customers and groups of purchasers who almost certainly wish to
purchase their high-end speciality products. It is an admitted fact that in the past few years the

majority of business organisations have started utilising social networking based sites such as
Facebook to advertise their products and services for the reason that these social networks
provide huge amounts of data (Hurwitz, Nugent, Halper, & Kaufman, 2013). Considering these
innovative aspects of social networks, they just had to invest in the ad and then Facebook
algorithms and performing analysis by utilising a ton of consumer data available on Facebook to
identify those people who most directly match their customer profile. “Those potential customers
then get targeted ads and special announcements from the store.” Though big data provides a
large number of advantages, and if it is used effectively, then it can bring some opportunities and
benefits to organisations. On the other hand, significant data can also be turned into a potential
source of annoyance (and even bad) when it encourages unnecessary and unwanted advertising
and marketing movements, emails, phone calls; or get the wrong impression about the central
theme, causing refutation of credit, erroneous charges, or in severe cases, the harmful certainty of
identity theft (In Chan, In Subramanian, In Abdulrahman, M. D.-A, & IGI Global, 2017).
MAJOR IMPLEMENTATIONS
At present, the majority of organisations heavily rely on data to not only run their
business tasks but also for the improvement of their organisational performance. Hence, the
implementation of big data can be seen in every field and industry. However, the healthcare
industry is believed to be the largest that has taken the maximum advantage of this technology
(Jack, 2010).
ISSUES WITH BIG DATA
However, there are various problems associated with big data, for instance, there can be
some data security and privacy-related issues. Given the fact that big data contain large volumes

of raw data and extracting useful information from these mountains of data is a challenging,
costly and time-consuming task. Without a doubt, the emergency "big data" analytics have
transformed the way that information is gathered, stored and processed (Langkafel, 2016). In
fact, a wide variety of techniques are applied to that stored data to change it into business
intelligence and make practical use of this data. Without a doubt, this is a fantastic technology
however with little or no rules concerning its use. Additionally, companies and users dealing
with big data are surrounded by some concerns and issues such as information ethics, data
privacy and data ownership; however, up to now, these issues have not been addressed
adequately. In this scenario, there is no particular research or study that differentiates between
privacy and data analytics. In fact, the emergence of big data analytics has made this line even
more blurred. Also, the advancements and developments in the field of big data analytics have
raised a wide variety of privacy, security, and ownership concerns and issues, not only for
customers, however as well as for company making use of data and analytics to deal with these
customers. In spite of all the developments and improvements, data privacy and security
strategies are up till now serious concerns. Moreover, any questions related to these subjects
have a propensity for obtaining little or even no attention (Liebowitz & LaCugna, 2013).
Undoubtedly, massive amount of data and sophisticated analytical techniques applied to
data not only make it simple for organizations to redevelop and update their services for
customers, however these practices frequently disclose lots of private information related to the
customers, their daily activities, their living styles together with those of their families, relatives
and friends. Additionally, at the present, there exist a number of robust algorithms and
programming tools that can disclose facts that, otherwise cannot be identified regarding
particular person as well as promptly show a relationship among a number of components of the

data puzzle and find out from time to time with excellent intelligibility the parameters, such as,
record, and even health conditions of a particular person. The extent of this correlation will
increase with the amount of data. In this scenario, before implementing prominent data analytics
organisations should cautiously think about the possible privacy and security issues that
automatically come with big data and analytics (Rajkumar, Srikanth, & Ramasubramanian,
2017). Also, there is no appropriate rule of law which defines specific measures on the use of big
data. For instance, how much data will be used and for what purpose it will be used and what are
the penalties for the misuse of this data.
Also, the majority of people do not know for what purpose their information is being
collected in fact how much information is being gathered. In some cases, they are entirely
unaware that stores are recording and keeping track of their purchases in due course). Moreover,
the fact goes to the substantial problem to put out of sight its knowledge from its customers.
Though this information is being collected for some positive activity but in many cases, this
information is hacked and misused by hackers, and the customers bear considerable loss
(Saadoun & Human Rights Watch (Organization), 2017).
TOOLS AND TECHNIQUES UTILISED ON BIG DATA
At present, a large number of business organisations make use of regression models to
identify some useful trends in their business data. Without a doubt, regression analysis is a form
of business analytics. In this scenario, regression analysis allows the business organisation to use
the value of some variable(s) which is known or they can control to forecast the benefit of
another variable. Also, it can be acknowledged as a metric for which a firm can optimise (almost
certainly obtain as far above the ground as possible). In simple words, a regression analysis

method can refer to the equation for a line that goes onto a dispersed area. In this scenario, each
value relates to an object of one variable’s response provided the condition of another variable.
However, it requires the business management to learn basic concepts of reasonably
sophisticated linear algebra especially the partial derivatives (Sathe & Hiwale, 2017). There are
many software, applications which provide excellent support for regression analysis. Some of the
well-known software applications can be MS Excel or SAS, or R, or a wide variety of other
statistical analysis applications and tools. In a business, regression analysis can be acknowledged
as the relationship between two variables. For instance, what will be the effect on variable B if
the value of variable A changes to some extent? This scenario can be understood with another
example, in which a business wants to invest in e-commerce but what will be the return on
investment (Simon, 2013). In this example, investment will be variable A and return on
investment will be variable B. In another example, a business can use this regression analysis to
determine if the some employees are increased then what will be the he effect on business
performance (Jeff)
THE IMPACT OF BIG DATA ON HEALTHCARE INDUSTRY
There is a number of considerations that are made in the process of Database
management within the usual healthcare field, which encompasses the collection of data from
numerous patients and the record keeping process that is crucial as each and every detail counts.
The mistake or even slight misinformation could be fatal for patients, and could cost the hospital
clients, as well as nurses who might end up losing their jobs. As such, database construction is
not only crucial but also one of the greatest skill requiring processes in the field. Informatics and
information technology specialists, as well as assistance of nursing technicians are utterly
important in this field (Singh, 2017).

The various databases are created for several purposes besides information management.
One of the core competences resulting from such a system is marketing. A majority of patients
mostly prefers institutions with an accurate and elaborate database. Besides, efficiency is another
main advantage derived from the use of these systems. Majority of the databases is made to
increase the speed and capability of the organization. Database systems are developed through a
number of issues and a number of strategies that have been employed over a long period. These
are mostly known as the dimensions of database construction (Wang, Li, & Perrizo, 2015).
Several dimensions are used in the development of database systems for not only nursing
but virtually all fields. The core field that will have to be assessed is the data transformation
dimension, which is basically the main field in database construction. Database construction
involves the development of a field, or a transformational item that will directly and
automatically convert raw information or data into information that can be stored. For this part of
the system, an already developed system for this purpose, say digital computers, or the usual
computer system, and the development of servers to store data will be used. The server will store
the information whereas the computers, connected to the servers will be used for as the access
interfaces for the users (Wehmeier & Baumann, n.d.).
Besides the transformation of data, developing a dimension or criterion for information
storage is vital and crucial, for instance, deciding who can access the information and who can
alter the content in the information. The basic overall consideration that will be used in the
development of this data management system is the basic and common use of fact tables.
According to (Williamson, 2014) basic use of data management tables and dimensional factual
information is crucial in the development of database dimension systems. These systems
basically involve the entry of information that has been crosschecked by a supervisor for

certainty. The use of factual tables is the simplest data entry method used in most database
systems, which allows even the least technologically aware employee to use the system after the
use of basic training.
The final dimension that will have to be considered in this case is the basic consideration
of context. What information will be contained in the information system and what will not?
Automatically, the information used will be of a digital and alphanumerical nature. This is
judged through the consideration of patient information that has to be put in words and not only
digits or amounts the client in question has consumed. Besides patients’ information, employee
information, such as their experience and their applicability to the patients’ conditions can be
used in assigning different nurses to different patients depending on their familiarity with the
conditions in question. According to (Zimmermann-Rittereiser & Schaper, n.d.), general nursing
informatics encompasses the inclusion and use of various dimensions from nurses’ information,
to patients’ conditions and information, their respective usage of hospital facilities, such as beds,
electronics and other similar facilities. In addition to that, practically any nurse can use
consideration the software developed for other purposes such as medical records, issuing of
medicine, and for considering which of the patients require more and thorough attention.
One of the easiest ways to develop a sound information system is the basic cloud
computing processes. One of the greatest advantages attributable to cloud computing is
scalability. A hospital can store and manage the quantity of information it needs to store in the
cloud without having to purchase additional software and hardware. In the case where a hospital
needs to store massive data, cloud computing facilitates this through its unlimited database. The
network load dictates the quantity of information that the hospital can hold. This also implies

manageable service cost for the organization. With cloud computing, organizations need to pay
only for what they use (Simon, 2013).
The fact that cloud computing eliminates the need to purchase new hardware and
software frequently also saves on the cost of infrastructure. The old system, whereby
organizations need to purchase software and hardware relevant for storing information
necessitates frequent renewal of licenses and maintenance. Much time and skill is also used in
training new personnel on using these tools. However, cloud computing saves organizations of
all this stress, thus facilitating delivery by saving on time and resources. Financial resources
which would otherwise be used in maintaining hardware can now be used for other business-
critical functions, improving the hospital’s functionality.
Cloud computing frees up the organizations of the stresses associated with data storage;
including security issues and hiring of teams to cater for data management. With fewer tasks to
handle within the management of an organization, it is possible for the team to cater for tasks
critical to the operations of the hospital, with the knowledge that another, larger hospital, such as
Google, is accountable for the storage of its information. This also saves organizations of the
concerns of network outages. Organizations offering cloud storage services are known to have
reliable network systems, which lower the chances of network outages – a factor that has
threatened the reliability of data providers in the past (Rajkumar, Srikanth, & Ramasubramanian,
2017).
Through cloud technology, the security of the information stored is guaranteed. This is
due to the fact that organizations have the option of encrypting data before storing it in the cloud.
To secure information even further, cloud service providers have strict policies to mitigate

security risks. Privacy policies are also formulated to the guarantee the safety of information
stored by individuals. These factors, coupled by sophisticated authenticated techniques make
cloud storage more reliable than traditional storage techniques, which were vulnerable to
phishing attacks.
Ease of integration is the other advantage associable with cloud computing. Through this
facility, users have assorted access mechanisms; hence, different individuals can access
information simultaneously. Cloud computing also offers flexibility in regard to configuration.
This is beneficial for organizations with several branches reliant on a central hub, as data can be
manipulated appropriately without the need for the persons involved to interact physically.
Organizations that require personnel to travel frequently will also benefit these individuals with
access to all sorts of data upon demand. The final cloud computing advantage relevant to
organizations is disaster recovery. The fact that cloud computing does not rely on hardware
implies that in the occasion of disaster, data is not lost (Langkafel, 2016).
Resulting from the controversy in the issue of hacking and personal privacy, various
privacy rules and laws have been set, prescribed and analyzed, which dictate the extents, levels
and punishments given to people who violate other people’s privacy. Privacy issues and
restrictions are meant to cover the information of a variety of people. In Information Security
Protocols, these restrictions are mainly addressed to employers, clients and other institutions that
prescribe the extent to which these parties can expose information pertaining to the individual to
other people, or organizations. For instance, banks have been prohibited from exposing financial
information of an individual at whatever costs (Jack, 2010).