Analyzing Business Intelligence Using Big Data in Healthcare

This report provides an in-depth analysis of business intelligence using big data within the healthcare sector. It begins with an executive summary highlighting the growing importance of big data in managing patient care and cost-saving opportunities through predictive analytics and the use of wearables. The introduction defines big data in healthcare, emphasizing its volume, velocity, and variety, and discusses the increasing need for analytics in strategic decision-making. The discussion section explores various opportunities such as predicting patient numbers, utilizing electronic health records, real-time alerting, improving patient engagement, and informed strategic planning. The report then delves into value creation using big data, focusing on the key characteristics of volume, variety, velocity, and veracity. Furthermore, it includes an analysis using Porter's Value Chain and Five Forces models to evaluate the competitive landscape and strategic implications of big data adoption in healthcare. The report concludes by summarizing the key findings and emphasizing the transformative potential of big data in the healthcare industry. The content is contributed by a student and is available on Desklib, a platform providing AI-based study tools.

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Executive Summary
Healthcare domain is growing at a much rapid rate along with importance for managing patient
care by innovative medicines. With an increase in need of these need, new kind of technologies
are being implemented in the industry. One of the biggest changes which is being analyzed in the
upcoming days is the use of big data in healthcare domain. It can be considered to be as one great
way to cost-saving in hospital. Predictive analytics is needed for resolving the issue by analyzing
the admission rates. Insurance industry will easily save money by keeping a proper track of
health trackers and wearables so that patients do not need to spend much time in hospital.

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Table of Contents
Introduction..........................................................................................................................3
Discussion............................................................................................................................4
Big Data Opportunities....................................................................................................4
Value creation Using Big Data........................................................................................5
Porter Value Chain Analysis...........................................................................................7
Porter Five Forces Analysis.............................................................................................9
Conclusion.........................................................................................................................10
References..........................................................................................................................12

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Introduction
Big data in Healthcare is completely related to a huge amount of health data which is
collected from various sources. It is merely inclusive of electronic health record, medical
imaging, pay-records and research in pharmaceutical (Groves et al. 2016). There are mainly
three characteristics which distinguish it from traditional human health data and electronic
medical data required for decision-making. It is generally available in huge volume as it moves
at such high velocity. It generally spans the whole health industry universe as it is derived from
different sources. Due to an increase in healthcare big data, there is a response to complete
digitization of information related to healthcare (Chen et al. 2017). As a result of the rise of
value-based care, there has been increase in the whole industry for making use of analytics for
having strategic decision making. Some of the encountered issues with healthcare data are
volume, variety and veracity. Some of the health systems need to have technology that has the
capability of collection, storing and analysis of information for producing the correct insight
(Viceconti, Hunter and Hose 2015). Use of big data in healthcare domain has brought a huge
amount of positive and lifesaving outcomes. Big data can be referred to as an enormous quantity
of information that has been created with the help of digitization (Zhang et al. 2015). It is
completely analyzed by the help of some particular technology.
In the coming pages, an overview has been given with respect to opportunities in Big data
technology. After that, value creation has been achieved by using big data. The next section of
the report deals with porter value chain and porter five forces analysis.

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Discussion
Big Data Opportunities
Predicting the number of Patients: Shift manager of hospital aims to encounter a certain
number of challenges like several staff required on the floor. If there is an excessive number of
workers, then there are chances of having extra labor cost, which is added up (Wang, Kung and
Byrd 2018). If the number of staff is less, then poor customer service can result in fatal for some
patient in the industry. Big data can be considered to be an effective technology for solving this
particular issue. Big Data technology can be used for daily and hourly prediction of a number of
staff required at each hospital.
Electronic Health Records: This is considered to be as one widespread use of big data
technology in medicine domain. In this, each of the patients come up with own record which is
inclusive of medical history, laboratory test and results. All records are completely shared
through an information system which is available for both public and private domains (Andreu-
Perez et al. 2015). Each and every record comes up with a modifiable file which highlights that
doctor can easily implement change over a period of time. There is no need for paperwork and
danger with respect to data replication. EHR can be also used for giving reminders and warnings
about when a patient needs to get a lab test. In addition, it can be used for tracking prescription
for analyzing where the patient is following the orders of a doctor.
Real-Time Alerting: Another biggest example of big data is the sharing of important
functionality for alerting in real-time. There is certain software which help in clinical decision
support for analyzing medical data at the very spot (Belle et al. 2015). It also provides healthcare
practitioners are generally advised to take up the perspective decision. There are many doctors

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who want to keep away their patient form hospital for avoiding any kind of costly in-house
treatment. Wearables will have all the data of the patient health on a continuous basis followed
by sending the required data on cloud. The collected information can be easily made available to
database on state health for the general public (Beam and Kohane 2018). Doctor will allow them
to compare the data of social-economic respect and make the modification with respect to
delivery strategies. Both the institute and care manager will require the right tool for monitoring
the right stream of data.
Improving Engagement of Patient: Most of the consumer inclusive of a patient will
already which have a proper interest for smart devices for record-keeping like heart rate, habit of
sleeping. All the important information is made easily coupled by the help of trackable data for
analyzing health risk (Luo et al. 2016). Chronic insomnia along with increased heart rate will
result in a chance for heart diseases. There are many patients who are completely involved for
tracking the health condition. In addition, health insurance can also push them so they lead a
proper healthy lifestyle.
Informed Strategic Planning: In healthcare domain, big data is mainly needed for
strategic planning which helps in having a better insight for motivating people (Chen et al.
2016). Care manager can also analyze proper check-ups among the various demographic group
of people. Identification is done with respect to factors that discourages people from taking
treatment. Many universities have already making use of technology like Google maps for
creating heat maps targeted at various problem like growth of population and even chronic
diseases (Viceconti, Hunter and Hose 2015). Most of the academics aim to compare data by the
help of the available various medical services in most heated areas. Insight gleaned from this

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particular area will help them to review the complete delivery strategy followed by adding some
of more units.
Value creation Using Big Data
In most of the aspects, big data circles around the four that are volume, variety, velocity
and veracity.
Volume: The biggest feature of big data is all about its volume. It does not make any
sense to focus on the minimum amount of storage. It has mainly come into the picture as the total
number of information is growing at much exponential rate. In the year 2010, there are around
800 Exabyte’s of data which is growing at a much rapid rate (Fang et al. 2016). EMC is a well-
known organization which makes use of data storage devices. It has been analyzed that there
were around 900 extra bytes of data which has grown to a value of 50 percent in last few years. It
is very much tough to analyze the amount of data being generated while the quantity of
information being collected in very large.
Variety: It is the most vital development in technology where more technology is being
digitized. Some of the traditional kind of data types that are structure data are things are inclusive
of account statement from bank that comprises of date, time and amount. These are a large
number of things which can easily fit into the relational databases (Hilbert 2016). Structured data
is completely augmented by using unstructured data that comprises some kind of information
like MRI images. Anything that is being captured and stored do not have any kind of meta-
model. It can be defined as a collection of rules which are needed for framing an idea or concept.
It merely defines the class of information and how the whole thing can be expressed.
Unstructured data is one of the concepts in the domain of big data (Archenaa and Anita 2015).

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The most suitable way of understanding structured data is all about comparing it with structured
data. Structure data can be stated data which has a defined set of rules. The most suitable
example is, the money is always considered like a number that will have two decimal that is
expressed like text and dates in a particular specific format. In case of unstructured data, there is
no kind of rules associated with it. Picture and tweet can be different but they are intended to
express the same idea and thought to depend on human understanding (Manogaran et al. 2018).
The biggest plus point of using big data is to make use of technology so that they can take data
and make complete sense.
Veracity: It generally related to the overall data quality which is being analyzed. High
veracity comes up with many records which are much valuable for analysis and can easily
contribute to most suitable for overall result (Huang et al. 2015). Low veracity comes up with
huge amount of data that is meaningless. Most of the non-valuable data sets are also stated as
noise. The most suitable example of high veracity where data can set from medical experiments
(Lo’ai et al. 2016). Data which comes up with high volume, high veracity and high variety is
completely processed by the help of advanced tools. The tools used are algorithm and analytics
which is a need for revealing the required information.
Velocity: Velocity is defined as the rate at which data is being build up or created. High
velocity of generated data along with pace needs to have a particular distinct processing-based
technique.
Porter Value Chain Analysis
Value Chain is a well-defined concept that has been created by Michael Porter. Value
chain can be stated as an activities collection which are performed by organization in creating

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value for most of the customer (Ahmed et al. 2017). Value creation aims to create value that
ultimately results to competitive advantage. The created value also results in much high profit for
any given organization.
In the domain of business management, a value chain is mainly needed for decision
support tool which is needed for model of activities. An organization need to perform so that it
can deliver a required product or service in market (Bello-Orgaz, Jung and Camacho 2016).
Value chain aims to categorize the generic value by adding certain number of activities for
organization that allow them to be easily understandable and optimized. Value chain comes up
with a certain number of activities for an organization that allow them to easily understood and
optimized. Value chain is generally built with series of sub-system which comes up with inputs,
transformation process and outputs (Li et al. 2015). It comes up with series of the subsystem
which has input, transformation process and even outputs. Considering this like analytical tool,
value chain that can be given to information flow so that they can have idea with respect to the
overall value creation for data technology.
Value Chain: Porter Value chain model comprises of different kind of activities which
are known as primary activity and support activity (Gligorijević, Malod‐Dognin and Pržulj
2016). Primary activities can easily effect on production, sales, production, maintenance and
support of product. The following activities are needed to be considered like
Inbound logistic: It can be defined as the processes which are involved for storage,
distribution of raw material (Groves et al. 2016). It comprises of basic ingredients for the given
product or services. The relationship with suppliers is needed for creating value in this particular
situation.

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Production: It comprises of activities that is production floor or production line which
can change input product or services for semi-finished product.
Output logistics: It can be defined as the collection of activities that are related to product
delivery and service for customer.
Market and Sales: It is merely inclusive of processes like putting the required product
and services in the market. This is inclusive of managing and generation of customer
relationship.
Support activities: These are like human resource management is considered to be vital
with primary activity (Chen et al. 2017).
Firm infrastructure: It aims to provide support activities in the organization that helps in
maintaining its daily operation (Zhang et al. 2015). In healthcare, both administrative handling
and financial management are some of the examples for creating a value for different firm.
Technology development: This particular kind of service is related to overall developing
products and related to services in the organization at both the level that is internal and external.
Procurement: It is mainly needed for supporting activities to procurement activities for
different customer.
Porter Five Forces Analysis
Big data in healthcare is considered to be future and game-changer in the healthcare
sector that has the highest adaptation among the medical professional, patient and government
bodies. The global market is trending to focus on innovative headways, a high incentive for
medication and big data in healthcare (Andreu-Perez et al. 2015). Big data in healthcare sector

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aims to provide a reliable digital IT platform which is needed for diagnosis and treatment of
different diseases. Apart from efficient diseases management, big data is used in three important
areas of the healthcare industry that is operational, financial and clinical data.
The threat of New Entrants: In healthcare industry, new entry will bring new methods of
completing the things and putting pressure on others (Belle et al. 2015). This particular thing can
be achieved by the help of cost reduction, value proposition for customers.
Bargaining Power of Suppliers: In the healthcare sector, powerful suppliers can make
use of their negotiation power for extracting much higher prices (Beam and Kohane 2018). The
overall impact of higher suppliers is its bargaining power which aims to lower the overall
profitability.
Bargaining Powers of Buyers: Buyers are very much demanding in nature. They require
to buy the best kind of offering which is available for paying off the minimum prices (Chen et al.
2016). If there is much small and strong customer base, then there is higher bargaining power for
customer.
Threat of Substitute: If a new product or services meet a service, then it aims to meet the
need of the customer in various ways (Luo et al. 2016). For example, some of the services like
Dropbox are found to be substitute for storage hardware devices.
Rivalry in the current competitors: Rivalry among the current players in the industry
will aim to drive down the prices and along with decrease in overall profitability for the industry.

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Conclusion
The report is all about big data in Healthcare. Healthcare domain is increasing at a rapid
rate and there is necessity of managing the patient care data. There is need of innovative machine
that has been increased at a rapid rate. With development of needs, new kind of technologies
have been ultimately increased. Due to increase in these kind of need, new technologies are
being implemented in industry. One of the biggest changes which is taking place in the near
future is the usage of big data in healthcare domain. This particular technology is all about
collecting, leveraging and analyzing physical, clinical data. The collected data can be understood
by some traditional means that is data processing. Big data Exabyte’s by the help of machine
learning based information which gives rise to value-based care. Big data is known to be best
technology which can be used for solving the overall cost for hospital which can be over or
under book staff members. Predictive analytics is also helpful in resolving this particular issue by
analyzing admission rates by using staff allocation. It will generally minimize the overall rate of
investment (ROI) which has been incurred by the hospital. On the contrary, it will help in
utilizing their investment to maximum level.

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