IRJET- A Survey on Big Data Frameworks and Approaches in Health Care Sector

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 472
A Survey on Big Data Frameworks and Approaches in Health
Care Sector
Jay L. Borade1, Joel Dsouza2, Gunjan Munde3, Divya Varghese4
1Assistant Professor Department of Information Technology, Fr. Conceicao Rodrigues College of Engineering,
Mumbai, Maharashtra, INDIA
2,3,4Student, Department of Information Technology, Fr. Conceicao Rodrigues College of Engineering, Mumbai,
Maharashtra, INDIA
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Abstract - Big data is becoming a booming technology in
many industries. There are many aspects of big datathathave
been recognized by researchers and technology consultants
around the world. Big data analytics shows high potential in
the healthcare industry and can be used to reveal causal
relationships. Analysis of big data in healthcare can reveal
valuable relationship between IT transformation, practices,
benefits and business value. In this paper we mainly look at
many approaches proposed by different researchers about
how with the collaboration of big data and electronic
healthcare records we can bring in many benefits in the field
of healthcare and well-being. In this paper we breakdown the
discussion into four sections starting with introduction,
methodology, results and conclusion.
Key Words: Big Data, Electronic HealthRecords,Healthcare,
Hadoop, Apache Framework
1. INTRODUCTION
Data processing is one of the biggestmotivationsintheareas
of research with the main focusofanalyzinghugeamountsof
data. In the present era the combination of big data, cloud
computing and data warehousing are becoming popular in
the field of healthcare. Healthcare information in theformof
Electronic Health Records(EHR)canbecreated,used,stored
and retrieve important patient data. The volume of EHR is
too much to be applicable using any scale. Any data can be
related to healthcare such as physician’s notes, biological
data, lab reports, patient metadata, case history diet regime
and list of doctors in a particular hospital [1]. We can
consider many sources ofdata inhealthcaresuchasPubMed,
SEER and NCI database, HCUP (Health Cost and Utilization
Project) and CMS. We need to understand what type of data
that we are dealing with and what significant role does it
play in acquiring knowledge. Knowledge can be briefly be
thought of as tacit or explicit [2].Tacitknowledgecanbesaid
to be knowledge developed due to individual experience.
This type of knowledge can be very subjective and could be
very complex. On the other hand explicit knowledge iswhen
it easy to collect format and distribute data of various
persons. When it comes to healthcare we can consider
different things like medical reports and records thathelpin
acquiring explicit knowledge. It could also include medical
procedures and diagnosis of certain diseases. Different data
sources can be considered when it comes to healthcare
analysis like the following:-
1. Electronic Health Record (EHR)
2. Genetic Data
3. Medical Imaging Data
4. Unstructured Clinical Notes
5. Documentation and Test Reports
Beneficiaries of Big Data analytics in healthcare:-
1. Doctors: A doctor can check the health records of
a patient at any place. Consider a patient that has
moved to a new place. The treatment of that
patient can continue without any hassle as the
data is available in the form of EHR present online
2. Government:- The usage of this data can help the
government to provide subsidies to private
hospital where government health centres are not
available
3. Insurance companies: Insurance companies can
use this information in order to prevent false
claims. It can also help in improving health
products
4. Pharmaceutical Companies:-Pharmaceutical
companies can use these records for research and
development work. Customizable drug can
created for diseases based on gained information
5. Marketing and Advertisement Agencies:-They can
use big data information about different diseases
prevalent in society. They can then make
appropriate training programs for educating the
rural people of India
In the following section we will have a look at how
different big data tools and architecture can be used to in
providing the in depth knowledge of healthcare analytics.
2. METHODOLOGY
This section briefly gives an overview of different tools
that can be used in big data and healthcare analysis.

2.1 Tools and Technology in Big Data
Hadoop Architecture: Apache Hadoop uses the master
slave architecture where in you have two nodes namely the
datanode and namenode. The namenode performs as a
master and datanode as a slave [3]. The namenode manages
the access to datanodes. The datanodes on the other hand
administrate and store data across multiple nodes
Fig -1: Hadoop File System Architecture
Hadoop MapReduce:
The Hadoop MapReduce is a programming model used to
process huge size of data sets across a Hadoop Cluster [3].
Hadoop framework also provides the scheduling,
distribution, and parallelization services to process the big
data [3].
Apache Sqoop:
Apache Sqoop can extract data from Hadoop Distributed
File System and export it to some external data storage
such as relational database.
2.2 Data Warehouse Based EHR platforms
OpenEHR and EHR4CR arethemostcommonplatformsused
as data warehouse for EHR analytics. The EHR4CR use
something known as a Common Information Model (CIM).A
CIM is a higher order query applied on EHR data warehouse.
EHR data is unstructured data according to CIM. We need to
apply the ETL operations in order to get unstructured data
into structured data and the EHR4CR takes care of local
information models and the EHR4CR CIM. OpenEHR is
designed based on requirement captured through many
years. The minimum requirements need to build openEHR
system are, data warehouse includes EHR, archetype
repositories, terminology, and demographic or identity
information [3]. The demographic repository is used as a
front end to store patient master index (PMI). The EHR can
be configured to include either no demographic or some
identifying data [3]. It is clear that the above platforms are a
good option for the analysis of historical healthcare data.
However in the present era the driving needs tend to
implement new technology other than data warehousing in
order to handle the new requirements.
2.3 EHR, Big Data Systems and Retrieval of EHR
records
A cumulative approach of structured and unstructured data
stemming from clinical and nonclinical modes of existence
will help us to understand and predict diseases. Fig 2 shows
a basic framework of more complicated platform which can
process big data of EHR and give us more desirable
performance in complicated analytics rather than data
warehouse platform. In fact, this method allows healthcare
institutions to productively document a total clinical
confrontation rapidly and recover necessary data from EHR
big data cluster in high execution processing. Therefore,this
approach gives most demands that are needed in big EHR
era. To examine tasks and to produce insight that enable
decision makers and to take steps and enhance health
performance and functional impact, Healthcare institutions
created data warehouses. An increment in data should
encourage the Healthcare organizations to move to big data
technology to provide new features that are mentioned in
data warehouse approach. Parallel and distributed
computing are some of the basic architectures that are used
in the handling of big data, because of which it can execute
processes concurrently on number of machines. An open-
source package called Hadoop was released by Apache for
distributed data handling recently. Hadoop Distributed File
System also known as HDFS can access, handle, and retrieve
all data files simultaneously among the Hadoop group. In[4]
it is discussed that how big data systems can be used for
designing platforms for conductingelasticsearch.Inorderto
conduct elastic search we can crawl a setofwebsites.Extract
data from these web structures and index them by elastic
search methods. After the entire procedure is conducted a
client can search by setting a search key and search option.
The steps of conducting elastic search is also briefly
elaborated in [4] as below:

Step 1: Collection and storage
Step 2: Classification
Step 3: Create Index
Step 4: Search and Analyze
Some applications of big data and elastic search can
therefore be used in order to develop sustainable
applications for ease of search useful patterns that can be
used in long term for healthcare.
Fig 2:- Big Data Analytics in Healthcare
3. Some Applications in Discussed Papers
On observation we see that there exists many technology
and tools that can be used in order to develop systems that
can provide easy access to healthcaredata.Oncombiningthe
Big Data like Hadoop and openEHR or EHR4CR we can
develop systems where easy access to EHR is possible.
Following are some of the results that canbeobtainedonthe
combination of the above tools
1. Healthcare analysis: On analysis a patients past
reports and current health checks a doctor could
possibly get better discernment on providing
prescription
2. Prognosis: Prognosis is nothing but the early
diagnosis of chronic age related diseases.
3. Report Management: Quick access to ones reports
and health info any doctor can consider what
treatment needs to be given to a patient
4. Healthcare Service Recommendations: Better
services could be providedbyanalysisofHealthcare
Service Providers on the basis of user ratings and
feedback
5. Follow Up Systems: Follow Up Systems could be
used in order to keep track of a patients current
health status with regards to his/her last visit.
Thus the above mentioned applications can possibly be
brought to reality by the integration of two common
available technology stacks
4. Conclusion
Nowadays, space (from hospital tohomeandcarry)andtime
(from discrete sampling to continuous tracking and
monitoring) are no longer a stumbling stone for modern
healthcare by using more powerful analysis technologies.
Medical diagnosis is evolving to patient-centric prevention,
prediction, and treatment. The big data technologies have
been developed over the years and will be implemented
everywhere. Consequently, healthcare will alsoenterthe big
data era. More precisely, the big data analysis technologies
can be used as guide in lifestyle, as a tool to support in the
decision-making, and as a source of innovation in the
evolving healthcare ecosystem. This paper has presented a
smart health system assisted by cloud and big data, which
includes 1) a unified data collection layer for the integration
of public medical resources and personal healthdevices,2)a
big data enabled and data-driven platform for multisource
heterogeneous healthcaredata storageandanalysis,and3)a
unified API for developers and a unified interface for users.
Supported by Health-CPS, various personalizedapplications
and services are developed to address the challenges in the
traditional healthcare, including centralized resources,
Information Island, and patient passive participation. In the
future, we will focus on developing various applications
based on the Health-CPS to provide a better environment to
humans.
REFERENCES
[1] Gunasekaran Manogaran, Chandu Thota, Daphne Lopez,
V. Vijayakumar, Kaja M.AbbasandRevathiSundarsekar,“Big
Data Knowledge System in Healthcare” Springer
International Publishing AG 2017 C. Bhatt et al. (eds.),
Internet of Things and Big Data Technologies for Next
Generation Healthcare, Studies in Big Data 23, DOI
10.1007/978-3-319-49736-5_7
[2] Komal Sindhi, Dilay Parmar and Pankaj Gandhi, “AStudy
on Benefits of Big Data for Healthcare Sector of India”
Springer Nature Singapore Pte Ltd. 2019 D. K. Mishra et al.
(eds.), Data Science and Big Data Analytics, LectureNoteson
Data Engineering and Communications Technologies16
[3] Youssef M.Essa, Gamal ATTIYA2, Ayman El-Sayed and
Ahmed ElMahalawy, “Data processing platforms for

electronic health records” Received: 15 February 2017
/Accepted: 3 January 2018 IUPESM and Springer-Verlag
GmbH Germany, part of Springer Nature 2018
[4] Aiqin Yang, Shiwei Zhu, Xianyi Li, JunfengYu,Moji Wiand
Chen Li, “The research of Policy Big Data Retrieval and
Analysis based on Elastic Search” 978-1-5386-6987-
7/18/$31.00 ©2018 IEEE

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