Analyzing Big Data in the Healthcare Sector: A Comprehensive Report

This report delves into the transformative impact of big data within the healthcare industry. It explores the essential role of big data in managing vast and diverse datasets, including electronic patient records, online feedback, and machine-generated information. The report highlights key use cases such as electronic data records, real-time alerts, hospital readmissions analysis, and fraud detection. A critical analysis of the 5Vs of big data (volume, variety, velocity, veracity, and value) in healthcare is provided, along with the benefits of big data for patients, hospitals, insurance companies, pharmaceutical companies, and governments. The report also addresses the challenges associated with unstructured data, data origin, and data processing. The report also covers big data architecture, including big data stack and processing architectures, and provides references to support the analysis.

Running head: BIG DATA IN HEALTH CARE INDUSTRY
BIG DATA TECHNOLOGY IN HEATH CARE INDUSTRY
Name of the Student:
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Author Note:

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Executive Summary:
The key role is played by Big Data in this industry as the association of this industry is with huge
data sets because they cannot be handled with the traditional data management tools or methods.
Big data in health care industry handles other data such as data of patient in the form of EPR
(Electronic Patient Record), online data such as feedback from the social sites (Twitter,
Facebook, official websites and blogs), and machine generated data and so on. The health care
organizations focus on analyzing the large quantity of data for the useful as well as unknown
facts, trends, associations and patterns. Hospitals also possesses some challenges regarding the
big data technology. The report also showcases those threats and some mitigating measures to
decrease the risk of the threats.

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Table of Contents
Introduction..........................................................................................................................3
Big data use cases................................................................................................................3
Critical analysis...................................................................................................................4
Big data architecture..........................................................................................................11
Big data stack architecture.............................................................................................11
Big data processing architecture....................................................................................12
Conclusion.........................................................................................................................12
References..........................................................................................................................13

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Introduction
In health care industry, the role of big data is to handle all sets of electronic data
associated with health care. The major role of big data is to manage huge data sets because they
cannot be handled with the traditional data management tools or methods. These tools cannot
manage the data due to the diversity in the data types as well as the speed of the data
transmission. This problem is solved by the usage of the big data technology in the health care
which helps to manage many data like clinical data from the CPOE as well as system of clinical
decision support like physician’s written report, medical imaging, insurance, pharmacy and
relevant administrative data. Big data in health care industry is also used to handle other data
such as details of the patient in the form of EPR (Electronic Patient Record), online data such as
feedback from the social sites (Twitter, Facebook, official websites and blogs), machine
generated data and so on. The report mainly aims to showcase the impacts of big data in the
industry of health care which includes both merits and demerits of using the big data technology
in such industry. The report briefly describes the use cases of big data, critical analysis related to
big data in health care and architecture of big data.
Big data use cases
1. Electronic Data Records: It is the common and most famous technology in the use case of
big data in the industry of health care. It keeps tracking all the details of the patients. The
records analyze the patient’s health condition and also keep the record of any kind of test
treated to the patient. This decreases the chance of duplicate test which alternatively
decreases the cost for the patients.

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2. Real Time Alerts: The clinical support is the major real time application which has an
ability to offer prescription after proper examination of the patient’s health.
3. Hospital Readmissions: The big data technology determine all the at – risk patients of the
hospital depending on the medical reports, laboratory reports and so on. This helps the
patient to stay focused on the medical treatment.
4. Fraud detection: Each and every data associated with the health care is critical for both
the patient and the industry and thus big data helps the health care by dealing with
various fraudulence in billing, patient records, insurance frauds and personal identity.
Critical analysis
Huge data are produced every day in various industries like finance, education,
development, research, manufacturing and health care (Tiwari, Wee and Daryanto 2018).
Traditional RDBMS and DBMS methods are unable to manage such huge data of an industry
and thus uses big data to manage this large data sets. The fast growing rate of the heath care data
will soon achieve the zettabyte scale (1021 gigabytes) (Murdoch and Detsky 2013). The health
care organizations focus on analyzing the huge quantity of data for the useful as well as unknown
facts, trends, associations and patterns. This is done by the machine learning algorithms. These
organizations aims to deliver quality health care services to their patients at the lower cost. The
5V’s of the big data associated with the health care are (Demchenko, Ngo and Membrey 2013):
 Volume: As mentioned above that huge amount of data is generated in various industry
every day, the health care industry also generates the data at staggering rate day by day.
As per the report of EMC the rate of data production is increased 48% annually.

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According to the recent research it is found that volume of the health care data in 2013
was 153 exabytes and was believed that it will increase to 2,314 exabytes by 2020.
 Variety: The health care data can be of three types: semi – structured, unstructured and
structured. These data are from gathered from various sectors of the industry like
machine data, patient data, administrative data, and so on.
 Velocity: The speed of data generation is referred as the velocity factor of big data in the
health care institutions. The wearable devices and the sensor devices read and generates
the data of the patient in a rapid speed or pace. This enormous speed is a huge challenge
for the data analysis method for the health care sectors.
 Veracity: The primary aim of the health care industry is to ensure that the gathered data
are reliable. Analysis of such voluminous, fast paced and variable data is a complex task
for the industry (Rubin and Lukoianova 2013). There is no chance in making an error in
the data analysis method. Thus the trustworthiness factor remains stable by using this
technology in such industry.
 Value: Usually the data from the EHR and EMR are considered as the high value data
(Russom 2011). The analysis of this high value data can result to a developed quality,
innovations and effective health care solutions.
Benefits of Big data in the health care
There are various reasons to implement the technology of big data in the health care
sector. Usually the data associated with health care organization are gathered from various
sections of the industry and thus, needs to analyze carefully. Big data is the appropriate and
correct choice for this type of industry. It can also be used to develop the quality of the human
life (Khoury and Ioannidis 2014). The aim of using big data in this industry is not limited in

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providing best services to the patients in low cost but also focus on forecasting the outbreak of
the disease and provide a quality cure to the respective diseases. The users can now access the
data associated with their queries and issues using the developed mobile phones and applications
(Kambatla et al. 2014). These application can provide proper information regarding some useful
health tips, best doctor in some specific departments such as cardiologists and so on. Now the big
data can be useful in many ways like:
From the patient perspective: Historical data set is required when a patient visit a hospital.
These include data regarding the drugs used, symptoms, patient’s response and other details. Big
data analytics formulate the personalized line of treatment for the patient depending on the
genomic data, weather, lifestyle, location, his or her treatment response and medical history.
When the data associated with the gene is established some relation can be made among the
DNA and some specific disease. Big data facilitates numerous facilities to the patient such as it
provides an appropriate and effective line of treatment to the patient (Kayyali, Knott and Van
Kuiken 2013). It helps the patient to take a better health related decisions. In case of some
accidental case the big data can also provide some preventive and immediate preventive cure to
the patient. Peoples can monitor his or her health status by wearing the smart devices and
improve their health according to the monitored data. The technology can also rise the life
expectancy and the quality of living.
From the hospital perspective: From the big data analytics technique the hospital can read the
data of the patient. This method helps the hospital to take decisions regarding which treatment
should be implemented first to cure the detected disease. This also help the hospital authorities to
identify the harmful patients and thus helps them to prepare some plans or policies for such kind
of patients because these patients can harm the hospitalization. The data allow the hospital to

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provide appropriate cure to the patient. The administration can take effective and efficient
decisions depending on the gathered data. The devices or the application can update the people
about the availability of the beds and doctors in the hospital. The hospitals upload their
availability in their websites to update people about the availability status of the hospital. This
technology also helps the authority to make a proper schedule of their daily task where reminder
can be set and the reminders reminds the authority to perform the business operation in time.
From the insurance company’s perspective: Many organizations provide medical claims to
their employees. This medical claim system of an organization comprises of many vulnerable
threats (Srinivasan and Arunasalam 2013). These threats are handled by the big data technology
by analyzing, predicting, minimizing, identifying the possible vulnerabilities associated with the
medical claims of the employees of the organization.
From the pharmaceutical company perspective
Various big data analytics techniques are used to prepare medicines in a rapid pace. This
assists the organization to rise their profit in this competitive market and allow the people to cure
their particular disease. The techniques helps the company to efficiently and quickly manufacture
their products.
From the government perspective: The government can utilize the historical, demographic,
weather, social media data to predict the epidemics of the disease. This is done by identifying the
correlation among the like occurrence of the disease and the weather (Archenaa and Anita 2015).
This is useful for the government as they can raise the awareness regarding the likely disease.
This helps the people to protect themselves from the disease. The data from the sources are
utilized by the government to raise the concern of the disease. For example: in case of dengue

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government request people to keep their society clean. The government authorities also provide
some preventive measures to the common people such that in case of any symptoms they can
resist the disease from spreading.
Challenges of Big Data in Healthcare
Following are the challenges listed for implementation of Big Data in healthcare:
1. Unstructured data and the origin of data – It has already been discussed in the former
part of the report that the process of BDA makes the usage of data from sources that are
varied in nature. Maximum part of the data that are used remain in unstructured format.
Some of the examples are tweets, status updates, blogs, medical prescriptions and the
comments that are made for the diagnosis made (Luo et al. 2016). For the implementation
of the Big Data in the industry of healthcare services, the data needs to be in a structured
format. For the transformation of this structured data from the unstructured data there has
to be a formation of a metadata that will execute this process of formation of structured
data from the unstructured data (Van Der Aalst 2016). The data that would be generated
from the image and video of the medical procedures has to be made structured for the
process of making semantic content and making semantic searches. A process of data
analysis has to be involved for the carrying out of the origin of the data along with the
metadata. It is necessary for tracking the steps of processing in the cases where errors
take place. It is necessary to have some processing techniques formulated, which will be
intelligent in nature for the input of the data from the sensors and the wearables into the
memory. This process will certainly facilitate the filtration or derivation of the
meaningful data from the bulk stored and then the data sorted out can be stored in the
permanent storage and will prove to be an effective way to save space while storing data.

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2. Data that are missing or incomplete – It is common for the patients to hide some of
their details about their personal lifestyle while filling up a form or giving oral interviews
to the physician or the doctors. As many sections remain empty and when this set of data
is placed on the digital format, then many fields remain empty. It is also seen that some
of the fields contain wrong values as the patient tried to make a wrong input in that
particular field. Such fields with empty value or wrong value create inconvenience in the
analysis of the entire data set as these fields with no data or wrong data may not take
participation in the processing of the analysis (Little and Rubin 2019). For both the cases
that are wrong entry and no entry the analysis produce erroneous results. If the empty
fields are not considered for the process of the analysis then the analysis done will not be
done considered as a cumulative analysis. In addition if the wrong data are taken for the
analysis then again the result that will produced will be unreliable and incorrect. Hence,
such issues need addressing (Kayes et al. 2018).
3. Data Quality: Before the processing of the data, all the information should be verified
whether the data that has been collected is from a valid source or not and the data quality
is up to the mark of the process of analysis or not. The determination of thee validation
and quality of the data is another big challenge when it comes to social media data (CAI
and Zhu 2015).
4. Technical challenges – The technical challenges that pose in the front for the following
consideration of the technology implementation in healthcare are listed below:
a. Aggregation of data from different database is considered as a big challenge for
BDA (Hripcsak et al. 2015).

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b. Scaling up of the traditional process of analysis and mining of the data is
necessary (Efara, Marquis and Tremblay 2019).
c. Making the analysis understandable to a non-technical person because that serves
the actual purpose of the data analysis (Pendergast et al. 2017).
d. Selection of the appropriate tool and platform due to availability of various
proprietary and open source platforms (Tapoglou et al. 2015).
5. Security of the data – Due to mass digitization of data, securing the data plays the most
important role in such surveys. People are now more scared of getting their data breached
in such platforms. Hence, if this concern can be addressed properly and solved, then
many other issues can be tackled efficiently (Martínez-Pérez, De La Torre-Díez and
López-Coronado 2015).
6. Lack of experts – Another primary concern is the availability of trained experts and data
analysts. It is needed to be assured that more experts come into the business so that in
future the concept of implementing Big Data into every sector could be done in reality
and not just theoretically (Kache and Seuring 2017).

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Big data architecture
Big data stack architecture