Abstract analysed data immediately. A database is a


process of examining huge and different data sets is known as Big data
analytics. It is used to uncover hidden patterns correlation and other insights
with today’s technology. Big data is mainly used to analyse data and get answer
for the analysed data immediately. A database is
a collection of information that is organized so
that it can be easily accessed, managed and updated. A relational database, more restrictively, is a collection of  schemas,  tables, queries, reports, views, and other elements. The outcome of the research paper is how big
data analytics and database is applicable in medical sector on Promises &
Potential. This paper provides a broad overview of big data analytics for
healthcare researchers and practitioners and health database organization.

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Keyword: Big data analytics, healthcare, Database.


healthcare industry historically has generated large amounts of data, driven by
record keeping, compliance & regulatory requirements, and patient care.
While most data is stored in hard copy form, the current trend is towards rapid
digitization of these large amounts of data.8 Healthcare
organizations are now presented with a flood of data. Everyone in the
organization—from the CEO to the individual clinician—needs a way to turn all
of that raw information into targeted,
actionable knowledge. Making sense of
the raw data on its own, without tools and processes to guide the process, can
be overwhelming. Here, we
are going to compare the “Big data and Data Base” in healthcare.


               This paper includes the
fundamental field of big data analytics in healthcare,  outlines an architectural framework and
methodology, underlying benefits, describes examples reported in the literature
, briefly explains the challenges and offers conclusion. This paper provides
the overview of big data analytics for healthcare research and practitioners.2

Raghupathi and Viju Raghupathi (2014). Has
of healthcare:

There are various
merits of using big data analytics in healthcare. By digitizing, combining and
effectively using big data, healthcare organizations ranging from a
single-physician offices to large hospital networks benefited through big data
analytics. McKinsey believes big data could help reduce waste and inefficiency
in the following three areas:

Clinical operation

Research and

Public Health

Evidence based

Genomic Analytics

Fraud Analysis

Device or Remote

Patient Profile

Raghupathi and Viju Raghupathi (2014).He has also given explanation using 4
“vs” of big data analytics in healthcare.

4 “vs” are

Volume, Velocity and Variety are the primary
characteristics. Veracity is the goal

Ø  Architecture

Raghupathi and Viju Raghupathi (2014).He has also given explanation using        architecture framework of big data
analytics in healthcare.









Architectural Framework

Ø  Outline of big data analytics in
healthcare methodology

Wullianallur Raghupathi and Viju
Raghupathi (2014).He has also given explain the Outline of big data analytics
in healthcare methodology in the table form

1       Concept statement

Establish need for big data analytics
project in healthcare based on the “4Vs”.

2      Proposal

What is the problem being addressed?

Why is it important and interesting?

Why big data analytics approach?

Background material

3        Methodology


Variable selection

Data collection

ETL and data transformation

Platform/tool selection

Conceptual model



exactly is big data? A report delivered to the U.S. Congress in August 2012
defines big data as “large volumes of high velocity, complex, and variable data
that require advanced techniques and technologies to enable the capture,
storage, distribution, management and analysis of the information.6

data analytics in healthcare

. Big data in healthcare is massive not
only because of its capacity but also because of the diversity of data types
and the speed at which it must be managed. By definition, big data in
healthcare refers to electronic health data sets are huge and difficult to
manage with traditional software and or hardware; nor can they be easily
managed with traditional or common data management tools and methods. The
healthcare industry historically has generated large amounts of data, driven by
record keeping, compliance & regulatory requirements, and patient care.
While most data is stored in hard copy form, the current trend is toward rapid
digitization of these large amounts of data. Driven by mandatory requirements
and the potential to improve the quality of healthcare delivery meanwhile
reducing the costs, these massive quantities of data (known as ‘big data’) hold
the promise of supporting a wide range of medical and healthcare functions,
including among others clinical decision support, disease surveillance, and
population health  management . Big data
encompasses such characteristics as variety, velocity and, with respect
specifically to healthcare, veracity.7

to healthcare

                      By digitizing, combining
and effectively using big data, healthcare organizations ranging from
single-physician offices and multi-provider groups to large hospital networks
and accountable care organizations stand to realize significant benefits.
Potential benefits include detecting diseases at earlier stages when they can
be treated more easily and effectively; managing specific individual and
population health and detecting health care fraud more quickly and efficiently.9
Numerous questions can be addressed with big data analytics. Certain
developments or outcomes may be predicted and/or estimated based on vast
amounts of historical data, such as length of stay (LOS); patients who will
choose elective surgery; patients who likely will not benefit from surgery; complications;
patients at risk for medical complications; patients at risk for sepsis, MRSA,
C. difficile, or other hospital-acquired illness; illness/disease progression;
patients at risk for advancement in disease states; causal factors of
illness/disease progression; and possible co morbid conditions (EMC


          At minimum, a big data analytics platform in
healthcare must support the key functions necessary for processing the data.
The criteria for platform evaluation may include availability, continuity, ease
of use, scalability, ability to manipulate at different levels of granularity,
privacy and security enablement, and quality assurance. To succeed, big data
analytics in healthcare needs to be packaged so it is menu driven,
user-friendly and transparent. Real-time big data analytics is a key
requirement in healthcare. The lag between data collection and processing has
to be addressed. The dynamic availability of numerous analytics algorithms,
models and methods in a pull-down type of menu is also necessary for
large-scale adoption. The important managerial issues of ownership, governance
and standards have to be considered. And woven through these issues are those
of continuous data acquisition and data cleansing. Health care data is rarely
standardized, often fragmented, or generated in legacy IT systems with
incompatible formats . This great challenge needs to be addressed as well.


term database embraces
many different concepts: from paper records maintained by a single practitioner
to the vast computerized collections of insurance claims for Medicare
beneficiaries. A database is a large collection of data in a computer,
organized so that it can be expanded, updated, and retrieved rapidly for
various uses.10

Key Attributes of Databases

In reviewing the
considerable variation in databases that might be accessed, controlled, or
acquired by HDOs, the committee sought a simple way to characterize them by key
attributes. It decided on two critical dimensions of databases:   comprehensiveness and inclusiveness.1 


Comprehensiveness refers to the amount of
information one has on an individual both for each patient encounter with the
health care system and for all of a patient’s encounters over time (USDHHS,
1991, refers to this as completeness). A record that is comprehensive contains:
Demographic data, Administrative data, Health risks and Health status, Patient
medical history, Current management of health conditions, and Outcomes data.


Inclusiveness refers to which populations in a geographic area are
included in a database. Databases that aim to provide information on the health
of the community ought to include an enumeration of all residents of the
community (e.g., metropolitan area, state) 5

The Concept of HDOs

The committee chose the
phrase Health Database Organization (HDO) to refer to entities
that have access to databases and that have as their chief mission the public
release of data and of results of analyses done on the databases under their
control. For purposes of this report, prototypical HDOs have the characteristics outlined
in these properties

They operate under a single, common authority.

They acquire and maintain information from a wide
variety of sources in the health sector

Files accessible to HDOs will
include person-identified or
person-identifiable data.

HDOs will serve a specific geographic area that
is defined chiefly by geographic or political boundaries (e.g., metropolitan
area, county, state) and will include those who reside in or receive services
in that area, or both.

HDOs will process, store,
analyze, and otherwise manipulate
data electronically.

The Benefits of Health Database

The gains expected from imaginative
but responsible uses of the information held by HDOs accrue not only to various
interest groups but also to populations generally, whether in a metropolitan or
sub state region, a given state, or the nation as a whole. The size of the
potential benefits, whether to the community at large or to specific users, is
likely to be a function of the comprehensiveness
and  inclusiveness of the databases the more  comprehensive or inclusive the more powerful the
information will be at every level and for every potential user and use.

Broad-based Benefits

The intent of many database and HDO
efforts today is to give regions a way to monitor and improve the value of
their health care services and the well being of their residents. HDOs might
achieve this by making available information on access to care, costs,
appropriateness, effectiveness, and quality of health care services and



Quality of care

of health services 
incidence and public health 



Ø  Bigdata analytics in healthcare

article “Big data analytics in healthcare: promise and potential written by
Wullianallur Raghupathi and Viju Raghupathi” uses the following methodology in
their research. The below paragraphs displays the main stages of the

In Step
1, the interdisciplinary big
data analytics in healthcare team develops a ‘concept statement’. The concept
statement is followed by a description of the project’s significance. The
healthcare organization will note that there are trade-offs in terms of
alternative options, cost, scalability, etc.

2, the proposal development stage. Here, more details are filled in.
Based on the concept statement, several questions are addressed: What problem
is being addressed? Why is it important and interesting to the healthcare
provider? What is the case for a ‘big data’ analytics approach? (Because the
complexity and cost of big data analytics are significantly higher compared to
traditional analytics approaches, it is important to justify their use). The
project team also should provide background information on the problem domain
as well as prior projects and research done in this domain.

In Step 3, the steps in the methodology are fleshed out and
implemented. The concept statement is broken down into a series of
propositions. Simultaneously, the independent and dependent variables or
indicators are identified. The data sources, as outlined in, are also
identified; the data is collected, described, and transformed in preparation
for  analytics. A very important step at
this point is platform/tool evaluation and selection. There are several options
available, as indicated previously, including AWS Hadoop, Cloudera, and IBM Big
Insights. The next step is to apply the various big data analytics techniques
to the data. This process differs from routine analytics only in that the
techniques are scaled up to large data sets. Through a series of iterations and
what-if analyses, insight is gained from the big data analytics. From the
insight, informed decisions can be made.

In Step 4, the models and their findings are tested and
validated and presented to stakeholders for action. Implementation is a staged
approach with feedback loops built  in at
each stage to minimize risk of failure.

Ø  Database
in healthcare

Euro stat
Statistics explained what methodology is used in collecting data using database
in healthcare. The following are the methodologies3:

budget surveys- The Household budget
survey, abbreviated as HBS, is a national survey focusing on household’s
expenditure on goods and services

sources-The  administrative source is
the register of units and  data 
associated with an administrative regulation (or group of regulations), viewed
as a source of statistical data.

Data collected for the purpose of national accounts-national accounts means
focusing on the structure and evolution of

Data information
systems available in health (and other) ministries / departments as well as
other agencies involved in health care.


Big data analytics in healthcare provides
sophisticated technologies to gain knowledge about the clinical information.
The healthcare industries are facing many challenges such as privacy,
safeguarding security, standard of governance in providing secured data
analytics in the area of all-over healthcare. Healthcare organizations and
healthcare industries are making steady progress in providing technological
based information in more transparent and as well as in an organized way.
Health database organizations diagnose the health of the public; improve the
quality of care in hospitals, clinics and various other care settings. Database
in clinical care should be maintained in a complete and accurate manner. Any
inaccurate, missing data or out-of-date data are found it may cause a big harm
to the overall sector. HDO’s can also contribute to improvements in quality of
care by making information available to institutions and group of practitioners
for their use in quality assurance and quality improvement(QA/QI)programs for
regional health planning. Hence we conclude by saying that big data analytics
has more storage than health database and it also reduces healthcare costs and
it is more effective than the database in healthcare.


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