Big data is impacting the healthcare industry by enhancing efficiency, increasing productivity, and helping anticipate potential issues. The document outlines how big data plays a role in healthcare through benefits like detecting illnesses early, customized treatment, and reducing waste. It also discusses challenges like privacy concerns, fragmented data from different sources, and ensuring data integrity when sharing information.

1. Big
Implications of
Big Data
in Healthcare
This white paper offers a detailed perspective on how big data is impacting the
healthcare industry and its underlying implication on the industry as a whole.
It outlines the role of big data in healthcare, its benefits, core components and
challenges faced by the healthcare sector towards full-fledged adoption &
implementation.
R
We Make It Happen.

2. CONTENT
Introduction 01
Big Data in Healthcare 02
Role of Big Data in Healthcare 03
Four V’s of Big Data in Healthcare 04
Big Benefits of Big Data in
Healthcare 06
Challenges for Big Data in
Healthcare 08

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Introduction
Over the last decade, tremendous progress has been witnessed in the volumes of data that is generated each
day through various activities. The capacity of people in harnessing technology to collect, evaluate and under-
stand such data has also increased substantially. The propensity of these trends to intersect with each other has
come to be termed as‘Big Data’. Needless to say, big data has emerged to be critically important to businesses
across diverse industrial sectors by enabling them to enhance their efficiency and augment their productivity.
The idea that big data plays a vital role in making the world a better place may seem farfetched but, it is true.
Evidence can be found in the way big data is being utilized in the healthcare sector.
Healthcare is one of the sectors that has immensely benefitted from big data. Other than enhancing efficiency
and increasing productivity, big data in healthcare is being effectively utilized to anticipate potential epidemics,
alleviate disease, enhance life expectancy and evade preventable deaths. While population continues to grow
and with increased life spans, changes have been witnessed in treatment methodologies. However, it is worth-
while to note that the changes have been largely data driven. The challenge here is to get a better understand-
ing about patients’health as early as possible.This is necessary to pinpoint any serious illness a patient may have,
early on. Identifying a life threatening illness at an early stage enables healthcare professionals to treat and
manage the ailment with relative ease as compared to the condition being diagnosed at a much later stage.This
is made possible by assessing and analyzing big data.
However, the diverse nature of the data collection community renders the
process of extracting and integrating data thoroughly challenging. Every
individual stakeholder such as healthcare providers, employers, disease
management organizations, payers, wellness institutions, genetic testing
organizations and patients, collects data. This has led to path breaking
efforts that are spurred by strategic partnerships amongst medical and
data professionals. This presents an immense opportunity to look into the
future and pinpoint potential challenges before they actually present
themselves. Big data from diverse sources viz., genetic records, medical
and insurance records, data from social media and wearable sensors are
effectively harnessed to outline a detailed picture of the patient and offer a
customized healthcare solution.
Predictive analytics has been tactically deployed within big data to derive
innovative insights. Apart from the routine clinical and administrative data,
amalgamating new data derived from patients pertaining to their health
records helps to anticipate ailments and offer timely intervention to
patients. Anticipating underlying health issues enables healthcare provid-
ers to offer preventive remedies to counteract the effects of such issues. All
this is possible by collecting, analyzing and understanding big data.

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Big Data in Healthcare
So what constitutes big data? A 2012 report outlines big data as huge quantities of intricate, high velocity and
variable data that warrants innovative techniques and technologies to facilitate the acquisition, storage, distri-
bution, management and analysis of information in a cohesive manner. Big data displays characteristics such as
variety, velocity, volume and a feature that is particularly visible in the healthcare sector - veracity. Together they
are known as the 4V’s of big data in healthcare. Prevailing analytical methods are applicable to the large amount
of unanalyzed data pertaining to health and medical records of patients to arrive at resourceful insights that can
be intrinsically applied to treatment methodologies. Preferably, available medical data can be analyzed to
provide appropriate information to care givers which can then be utilized by them to streamline treatment
procedures for a specific patient.
According to (Raghupathi & Viju, 2014) the volume of data in healthcare is projected to grow sizably in the
coming years. Moreover, a shift has been observed in models of healthcare refund wherein, significant use and
payment for performance occupy crucial importance in the current day healthcare environment. Though the
motivating factor here is not profit, it is vital that healthcare organizations obtain necessary tools, techniques
and infrastructure to effectively leverage big data or they stand to lose millions in revenue and profits.
At present the healthcare sector is facing a digital revolution. Volume of data is now shifting from primary
science to genomics that are clinically based and personalized medicine. Big data is evolving non-stop in the
healthcare sector both at a personal and large scale levels (Issa, Byers, & Dakshanamurthy, 2014). As a matter of
fact, clinical phenotypes are now being biochemically and quantitatively expressed by utilizing proteomics,
metabolomics and transcriptomics. Accumulating and analyzing big data is set to emerge as a core factor that
drives innovation in healthcare. New developments in big data analytics has major implications not just on
healthcare delivery from a patient and care provider’s perspective but it will also prove to be critical in restruc-
turing biomedical discovery. For instance, a single human genome was decoded in a decade initially, however,
with the advent of big data, a human genome can now be decoded within a week by utilizing modern DNA
sequencing and informatics approaches. Big data is now being applied in healthcare in an all inclusive manner
such that it encompasses:-

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Role of Big Data in Healthcare
Clinical Discovery
Systems Medicine & Pharmacology
Toxicity Prediction
Electronic Medical Records
Loosely structured patient related data is being generated by healthcare monitoring systems from various
sensors over a period of time. Such healthcare monitoring systems are intricate and demand the need for effec-
tive algorithms and computational prowess to process and analyze the raw data. Big data in healthcare can be
termed as data that has been derived from various sensors that encompasses medical, traffic and social data
(Augustine, 2014).
Big data analysis infrastructure in healthcare can be developed by putting in place a robust mechanism that is
required to accumulate, organize and process data with an objective to derive useful information. This facet can
be attributed as data acquisition, data organization and data processing. Acquiring the data can prove to be a
key challenge in a big data environment. Since healthcare monitoring systems deal with large quantities of data,
a low latency is needed in data capturing while simple query methods can be implemented to process a huge
quantity of data.
Source: https://hbr.org/resources/pdfs/comm/sap/18826_HBR_SAP_Healthcare_Aug_2014.pdf
Objective of applying Big Data Analytics in Healthcare

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Four V’s of Big Data in Healthcare
Reduce cost
Reduce time
Develop new research
Optimize decision making
As data in healthcare is available in large quantities, it is necessary for the healthcare monitoring system to
assimilate and process existing data from the primary location where it is stored. This is easily facilitated by
‘Apache Hadoop’software that offers a unique technology to process huge quantities of data and also enables
the data to be kept in original data clusters. Moreover, it is essential that the data in big data be processed in a
distributed environment. Analysis of medical information merits the need for deploying a statistical and mining
approach. Delivery of analyzed data within a comparatively faster turnaround time assumes high priority in such
a scenario. The amalgamation of data pertaining to patients, data pertaining to effects of drugs, research and
development data, financial data and medical data by healthcare organizations, can be instrumental in deter-
mining existing patterns that lead to providing proactive and improved healthcare solutions. Further, in the
chance that healthcare organizations integrate patient-centric data in tandem with data derived from social
media within their data management systems, they stand to gain an inclusive insight into the correlation
between such data. The primary idea of infusing big data in healthcare is to enable the industry to acquire data
from any relevant source, support the data thus collected and analyze it to arrive at conclusions that enable
them to:-
One billion smart phones and three billion IP enabled devices are expected to enter service in the coming three
years. Remote health monitoring devices would be used by approximately around 4.9 million patients. In addi-
tion, around three million patients would be using a remote monitoring device by means of a smart phone hub.
Healthcare and medical app downloads are projected at 142 million. In 2012 alone, healthcare had generated
around 500 petabytes of data. This figure in 2020 is projected to grow to 25000 petabytes. Summing it in short,
it can be conclusively said that big data is poised to emerge as the buzzword for the next generation. Data that
supersedes the processing capability of traditional data management systems is termed as big data. Big data
displays tendencies where the data is rather large, fast paced and doesn’t conform to the existing database
architecture. Big data in healthcare has four prominent dimensions which are generally referred as four V’s viz.,
volume, variety, velocity and veracity. Volume in this context refers to data in terabytes and zettabytes, variety
would refer to unstructured, semi-structured and structured data, velocity would refer to batch processing to
real-time streaming of data and lastly veracity would relate to quality and relevance of data. Applicability of each
of these dimensions to healthcare data is discussed below in detail (Sreekanth, RR, & Arvind Kumar, 2014).

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Data volumes globally, are witnessing an exponential growth. However, growth of data in healthcare is aug-
mented by digitizing existing data and through new data generation. Existing healthcare data that is over-
whelming to say the least encompasses individual medical records, clinical trial data, radiology images, popula-
tion data, human genetics, genomic sequences and FDA submissions. The growth of data in healthcare is also
being spurred by new forms of big byte data that relates to 3D imaging, biometric sensor readings and genom-
ics.
Source: http://research.ijcaonline.org/volume89/number16/pxc3894622.pdf
Volume
Velocity
Variety
Veracity
Data at Rest
TB & ZB of data to process
Data in Motion
Streaming Data
Data in many forms
Structured, Unstructured
Data in Doubt
Uncertainty due to data
ambiguity
Volume
An aspect that renders healthcare data as interesting and challenging is the existence of a large variety of data,
either in unstructured, semi-structured or structured formats. Since ages, point of care has a tendency to gener-
ate data that is largely unstructured. Data in this case would refer to medical records, handwritten notes by
doctors and nurses, doctor’s prescriptions on paper, admission and discharge records in hospitals, radiograph
films, CT scan, MRI and other relevant images etc. Structured data is comparatively easy to store, query, recall,
analyze and can be manipulated by machines. Structured and semi-structured data encapsulates data pertain-
ing to electronic health records, e-accounting and billing, actuarial and clinical data to some extent and readings
of laboratory instruments.
Variety

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Big Benefits of Big Data in
Healthcare
The rate at which new data is being generated presents a unique challenge to healthcare organizations. While
volume and variety of data that has been accumulated and stored has changed considerably, likewise, the veloc-
ity and speed necessary to recover, compare, compute and analyze healthcare data to make strategic decisions
has also changed. The situation has witnessed a remarkable transition from sluggish batch processed data han-
dling to real-time data processing. Nowadays, data velocity can be crucial in some medical scenarios where real-
time data can be instrumental in life and death situations.
Velocity
Issues in data quality are of critical concern in the healthcare sector owing to two causes; 1. Decisions in life and
death situations hinge largely on access to accurate data. 2. Data in healthcare, especially related to unstruc-
tured data is largely variable and presents a scope for error, for instance, handwritten prescriptions that are
unreadable. Data veracity in healthcare is confronted with the same challenges as financial data, more so when
it concerns payers; veracity in terms of whether it is the right patient, payer, hospital, reimbursement code etc.
Veracity issues could further relate to questions such as; whether prescriptions, diagnoses, procedures and
outcomes are accurately captured.
Veracity
Diverse healthcare organizations be it small single doctor clinics, large hospital networks, multi provider groups,
and organizations offering advanced and inclusive care, stand to derive maximum benefits by digitizing, com-
bining and adopting big data analytics within their operational systems. Some of the benefits to healthcare
organizations would relate to an enhanced ability to detect complex diseases during their initial stages which is
imperative for effective and successful treatment. It also facilitates healthcare organizations to provide custom-
ized treatment plans to specific individuals based on their preferences. Offering treatment for lifestyle related
diseases by intrinsically analyzing data pertaining to lifestyle patterns of patients. In addition, big data also facili-
tates healthcare organizations to detect frauds and malpractices with relative ease and efficiency (Raghupathi &
Viju, 2014).

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Source: https://hbr.org/resources/pdfs/comm/sap/18826_HBR_SAP_Healthcare_Aug_2014.pdf
Graph depicting benefits of Big Data in Healthcare
Big data in healthcare is particularly beneficial in answering several critical questions. It can also be effectively
utilized to forecast certain developments or outcomes or a future scenario can be anticipated on the basis of
existing historical data. For instance; length of stay of patients, propensity of a patient to opt for elective surgery,
patients who are unlikely to gain from surgery, risk to patients arising from medical complications, sepsis or
other relevant illness acquired during hospitalization etc. A report by leading research firm McKinsey estimates
that big data in healthcare would facilitate savings to the tune of more than $300 billion per year in the United
States alone. Two core areas of healthcare where huge savings can be affected through big data are in clinical
operations and research and development. The McKinsey report also emphasizes that big data could be effec-
tively utilized to reduce waste and inefficiency in three key areas of healthcare viz., clinical operations, research
and development and public health.

10. Challenges for Big Data in
Healthcare
Conclusion
Big data in healthcare is confronted with several challenges that complicate the usage of healthcare data to its
maximum potential. To start with, data available with several healthcare organizations (hospitals in general) are
more often than not fragmented and exists in silos. As a matter of fact, data related to administrative activities
that involves reimbursement, information related to cost and claims process are amassed to be utilized largely
by the financial department and by the operations management personnel. While financial data is utilized to
execute the business aspects of healthcare, such data is not utilized to notify treatment protocols or patient care.
At the same time, clinical data that includes medical history of patients, vital statistics, rate of progress and diag-
nostic outcomes are stored within electronic health records. This data is exclusively maintained and periodically
accessed by care givers, nurses and other core clinical personnel. Also such data is utilized by care givers to
monitor patient care and convey appropriate treatment plans to patients. Other data pertaining to quality and
outcomes that encompasses infection on surgical sites, rate at which patients return to surgery etc., are handled
and accessed by the quality or risk management departments. Results of a clinical informatics survey revealed
that 43 per cent of respondents were of the opinion that data maintained in silos within an organization
emerged as a major challenge when it came to analyzing clinical data (White, 2014).
Another challenge faced by the healthcare sector while leveraging big data pertains to privacy of patients’data.
Data sharing amongst healthcare organizations is critical and the formation of regional health information com-
panies hinged largely on assimilating data from various stakeholders like payers, providers and public health
organizations. As per the Health Insurance Portability and Accountability Act [HIPAA] organizations are required
to protect data pertaining to patients. In this scenario, data sharing can only be facilitated after de-identification
however, the challenge lies in the organization’s ability to protect patients’ identity, either directly or indirectly
while sustaining the usefulness of existing data. Thus, the major challenges for big data in healthcare relates to
creating an appropriate balance amongst patient privacy and maintaining data integrity.
Consumer markets have successfully adopted and implemented big data within their business processes how-
ever, implementing big data analytics within healthcare presents unique challenges which need to be over-
come. One amongst the several challenges that needs to be overcome relates to fragmented data and data
integrity. Big data in healthcare holds tremendous promise and potential to substantially alter the way health-
care is delivered. It can also make an impact on the way care providers adopt and utilize cutting edge technolo-
gies to derive strategic perceptions from clinical and other data and arrive at informed decisions (Raghupathi &
Viju, 2014).
The coming days will bear testimony to a rampant and enhanced use and implementation of big data analytics
within the healthcare sector. However, to completely embrace big data and adopting it in total will most
certainly depend on tactically eliminating the challenges posed by big data in healthcare. Since the importance
of big data is gradually dawning on stakeholders, concerns related to maintaining privacy, ensuring security,
setting standards, appropriate governance and continuous enhancement of the tools and technology will gain
prominence.Though big data in the healthcare sector is still in the early phase of development, brisk progress in
existing tools and platforms can aggravate the rate at which it matures.

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