Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems and Analytics

Big Data, CEP and IoT : Redefining Holistic
Healthcare Information Systems and Analytics
Tawseef Naqishbandi
Dept.of Computer Science and Engineering,
Islamic University of science and Technology,
Awantipora Kashmir
Imthyaz Sheriff .C
Dept of Computer Science and Engineering,
B. S. Abdur Rahman University
Vandular, Chennai
Sama Qazi
Dept. Electronics and Communication
Islamic University of Science and Technology
Awantipora, Jammu and Kahmir, India
Abstract— Healthcare industry has been a significant area
for innovative application of various technologies over
decades. Being an area of social relevance governmental
spending on healthcare have always been on the rise over
the years. Event Processing (CEP) has been in use for
many years for situational awareness and response
generation. Computing technologies have played an
important role in improvising several aspects of
healthcare. Recently emergent technology paradigms of
Big Data, Internet of Things (IoT) and Complex Event
Processing (CEP) have the potential not only to deal with
pain areas of healthcare domain but also to redefine
healthcare offerings. This paper aims to lay the
groundwork for a healthcare system which builds upon
integration of Big Data, CEP and IoT.
Keywords—Big Data; Internet of Thing; Complex Event
Processing; CEP; IoT; Body Sensor Networks; Healthcare
I. INTRODUCTION
The health care industry is extremely big incorporating
several sectors that are dedicated to providing health care
services and products. Some of these sectors depend
significantly on data storage, computing and communication
technologies. Healthcare Information Technology (HIT) [16]
is ―the application of information processing involving both
computer hardware and software that deals with the storage,
retrieval, sharing, and use of health care information, data, and
knowledge for communication and decision making‖. In
recent years a newer sector of Healthcare known as
‗Healthcare Informatics‘ has emerged and started to gain
significant popularity. It is a discipline at the intersection
of information science, computer science, and healthcare [17].
Healthcare finance and insurance is also another area which
relies significantly on computing and communication
technologies.
Healthcare information systems have come a long way from
being simple patient data management systems, to being
repositories of huge volume and variety (blood work reports,
diagnosis details, prescription details, scans, x-rays images
etc.). Most of this data goes unanalyzed due to its sheer
volume and also due to lack of meaningful correlative
analytics. Task of analysis all this data becomes even tougher
due increasing use of Personal Health Systems (PHS) and
Remote Patient Monitoring and Treatment (RMT), Body Area
Networks (BAN). Such monitoring devices keep pouring data
at a rapid rate and most of this may go unanalyzed.
Healthcare is an area of concern and importance for both
developed and developing nations owing to its social
relevance. Governments across the world are focusing on all
aspects of healthcare like policy changes, legal statures,
insurance, funding, and technology overhaul. Patient
Protection and Affordable Care Act also popularly known as
Obama care is ushering significant changes in US healthcare
industry. Hearth insurers are expected to see substantial
increase in their cost due to increased risk of covering more
people and they cannot legally deny insurance to individuals
based on prior health conditions. Affordable care ensures that
maximum number of people is covered. This poses as a
challenge to hospitals as they will witness increase in patients,
which means increasing amount of data to be analyzed.
This paper is organized as follows: Section II discuss about
Big Data and its role in Healthcare. Section III details about
IoT, its role in future world and how it contributes to
healthcare. Section IV provides an overview of CEP and how
it is significant in Healthcare information systems and
analytics. Section V proposes a holistic system for healthcare
informatics. Finally, Section VI gives the conclusion and the
step forward.
II. BIG DATA – CRUNCHING HEALTHCARE
DATA
The new era of health care will be driven by an explosion
in health-related data from a growing range of public and
private sources, and can be analysed by increasingly powerful
number-crunching computers. This new era is moving from a
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world where illnesses can be treated to one where it can be
predicted and can be prevented. Big Data technologies will
play a key role in turning this idea into reality. Big Data is a
term encompassing the use of techniques to capture, process,
analyze and visualize potentially large datasets in a reasonable
time frame not accessible to standard IT technologies [1]. It
refers to the ability to crunch vast collections of information,
analyze it instantly, and draw from it sometimes profoundly
surprising conclusions. According to Gartner analyst Doug
Laney [2] data growth challenges and opportunities as being
three-dimensional, i.e. increasing volume (amount of data),
velocity (speed of data in and out), and variety (range of data
types and sources). Big data technologies deal with petabytes
of records, files, transactional data either arriving as streams
or in batches. Rise of disruptive technologies like social and
mobile are contributing to increase in unstructured and semi
structured data.
Figure 1: Big Data Properties
Big data technologies have to deal with all these varieties of
data. Whereas volume, variety, velocity are the native
properties of big data system, it has also three acquired
properties of variability (indication of changing nature of
data), value(significance based on statistics, hypothesis etc,),
and veracity (trustworthiness of data, provenance etc.).
`Big data is an idle fit for dealing with the technology
challenges faced by the Health care industry. Increasing
public health records with all the new sources of health data
generated by wearable sensor devices, Wi-Fi enabled scales,
smart phones and low-cost diagnostic kit, could provide a far
more accurate picture of individual‘s health and the
treatments they receive. In terms of big data for health care
―Volume‖ refers to the rapidly expanding size of the sets of
data being generated in every area of activity in an healthcare
enterprise, from revenue, to patient data, to supply and
operations. ―Variety‖ includes the diversity of data collected.
In a hospital, for instance, data includes patient records
containing a variety of information like lab reports, scans, x-
rays, prescription details and so on. Beyond diagnosis and
treatment related data, all aspects of finance, patient
scheduling and workflow, insurance data and medical
outcomes etc are also available. Synthesizing and analyzing
such disparate elements is challenging on its own. With
newer sources of healthcare monitoring devices like personal
health monitors, Body Area Networks (BAN) there is
significant ―Velocity‖ of incoming healthcare data.
There used to be a time in healthcare industry where more
in-patient days translated to more revenue. But now patients
are increasingly demanding information about their healthcare
options so that they understand their choices and can
participate in decisions about their care. Patients are also an
important element in keeping healthcare costs down and
improving outcomes. Providing patients with accurate and up-
to-date information and guidance rather than just data will
help them make better decisions and better adherence to
treatment programs [4]. As a result of this focus on
meaningful information, all healthcare constituents are
impacted by big data, which supports analytics that predict
how these patients are likely to behave, encourage desirable
behavior and minimize less desirable behavior. Report on Big
Data Analytics [5] indicates that Big data solutions can help
stakeholders personalize care, engage patients, reduce
variability and costs, and improve quality of health delivery.
Big data analytics can also contribute to providing a rich
context to shape many areas of health care like analysis of
effects, side-effects of drugs, genome analysis etc.
As populations grow and people live longer, healthcare
costs are growing to unsustainable levels. Insights derived by
big data analytics can help remove inefficiencies from
system. For example, by analysing prescription drugs for
specific ailments across the country, Big data analytics can
help in saving lots of money if doctors switched from
branded to cheaper - but equally effective generic versions of
the drug. Big data analytics can aide hospitals in efficient
resources management by reducing emergency waiting times,
track patient movements, moderate X-ray dosage levels etc.
Access to huge amounts of healthcare data coupled with
insights provided by big data analytics, it will be possible to
develop predictive algorithms that can forecast which
demographics are likely to cost the most to treat in future, for
conditions like diabetes and asthma. This will enable
healthcare providers intervene earlier and redesign their
services to cope with the expected massive increase in
healthcare demand.There are also certain challenges particular
to using big data for healthcare:
Accuracy: Human tendency to understate negative factors,
such as smoking and failure to comply with treatment is of
concern. People also tend to overstate positive factors, such as
exercise. These biases need to be identified and corrected, or
passive techniques need to be used in order to acquire data
that does not have self-reported bias.
Privacy: People are reluctant to divulge personal information
because of concerns about privacy. Providing guarantees for
security and privacy, incentives will help to address these
concerns.
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Consistency: Standards need to be developed and
implemented to promote consistency, increase usefulness and
facilitate data usage amongst all the stakeholders involved in
various healthcare sectors.
Facility: Mechanisms need to be developed to make it easy
for patients to accurately self-report data. This includes
evolving passive mobile computing, wearable devices that
require no effort. Techniques are needed to get data from
healthy people to make the populations truly representative
and not biased by the ill in any kind of statistical analysis.
Fragmentation: Healthcare data is significantly fragmented.
There is also unwillingness for healthcare participants to share
data.
III. INTERNET OF THINGS – INTEGRATED
MONITORING OF HEALTH
The Internet of Things (IoT), sometimes referred to as the
Internet of Devices or Internet of Everything, is expected to
change the world as one sees and perceives it today. Internet
of Things (IoT) is a worldwide network of interconnected
objects and their virtual representations uniquely addressable
based on standard communication protocols. Identified by a
unique address, any object including computers, mobile
phones, RFID tagged devices, and especially Wireless Sensor
Networks (WSN) will be able to dynamically join the
network, collaborate, and cooperate efficiently to achieve
different tasks. IoT devices gather and share information
directly with each other and the cloud, making it possible to
collect record and analyze new data streams faster and more
accurately [11].
World population is on the raise. Especially in countries
like India and China with higher population growth, health
care has become one of the largest sectors, both in terms of
revenue and employment – and its growth is expected to
continue. Estimates project that the current US $40 billion
Indian health care industry will grow to US $280 billion by
2020. Such massive systems can effectively handled only by
embracing cutting edge technologies. IoT will be key enabler
for holistic healthcare environment. The emergence of the
IoT, in which devices connect directly to data and to each
other, is important in future of healthcare for two reasons:
1. The recent advances in embedded sensors and
connectivity technology are allowing devices to connect,
anywhere, anytime anyplace to collect record and analyze data
that was not accessible before. In healthcare, this means being
able to connect and collect patient data on the air over time
that can be used to help enable preventive care, allow prompt
diagnosis of acute complications and promote understanding
of how a meditation is helping improve a patient‘s parameters.
2. The ability of these smart devices to connect and
gather data on their own removes the limitations of human-
entered data—automatically obtaining the data that suits
doctor‘s need; at the time and in the way they need it. The
automation reduces the risk of error. Fewer errors can mean
increased efficiency, lower costs and improvements in quality
in just. In healthcare, where human error can literally be the
difference between life and death, IoT devices are a boon.
IoT-related healthcare issues today are based on the
essential definition of the IoT as a network of devices that
connect directly with each other to capture and share vital data
through a secure service layer (SSL) that connects to a central
command and control server in the cloud. Energy efficiency
[7],[10] of sensors will play a major role in the success of this
technology. A major concern about sensors is Energy
efficiency of these smart devices; various methodologies have
been discussed in literature for saving energy in sensor nodes
[7].
From the perspective of healthcare, IoT devices can
broadly fall in three categories as shown in Figure 2, 3 and 4.
 Personal Monitoring Devices: Wearable blood pressure
monitors, thermal sensors, smart pacemakers, movement
monitor (posture, gait, step size, step height), EEG nodes,
ECG Nodes, pulmonary monitors, diagnostic aides at
hospitals.
 Smart Home Devices: Home automation has always been a
interesting idea for deployment of sensors. With focus on
healthcare, IoT devices at home can monitoring living
conditions (temperatures, light etc) at home, keep track of
what a person eats (smart fridges). This devices can give
better insights for healthcare.
 Environment Monitoring Sensors: These have been around
for quite some time for monitoring air quality, temperature,
humidity etc in cities. With newer and better sensors smart
cities are becoming a reality. Environmental data can help
understand spread of ailments like asthma, hypertensions etc
which may be attribute to environmental conditions to some
degree.
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Figure 2:Personal Monitoring Devices
Figure 3: Smart Home Devices
While still an emerging trend, machine-to-machine (M2M) --
and the overall Internet of Things phenomenon -- have
Figure 4: Environment Monitoring Sensors
captured the healthcare industry's attention. The two
technologies shared the No. 4 spot on Gartner's 2013 Top 10
Strategic Technology Trends list[12]. Another research firm,
Markets and Markets, recently projected the Internet of
Things and M2M communications market to grow from $44
billion in 2011 to $290 billion by 2017, representing an
estimated CAGR of 30 percent from 2012 to 2017. For
solution providers, the technologies represent a way to grow
their business, provided they take the right steps and forge the
right partnerships. Ability of personal health monitoring
devices to communicate instantly with doctors, hospitals,
emergency services will redefine the landscape of healthcare
industry.
Though a completely viable world of IoT is in near-distant
future, some of the ingredients like Wireless Sensor Network
(WSN), Body Areas Networks(BAN) are already existent and
deeply being explored. Wireless Sensor Network (WSN)
refers to a distributed network, consisting of dispersed and
autonomous sensing stations. Each sensing station—also
known as a sensor node—consists of a computing component,
communication component, a power source normally a
battery, and some sensors depending upon the application
area. Some smart sensors are equipped with an actuator [8]—
an electro-mechanical device used to control different
components of the system. Wireless Body Sensor Networks
(WBSNs) has offered a paradigm shift which can be used for
early detection of the different diseases so that health issues
can be detected at an early stage. These smart things can
collect and analyze the vital sign-related data of patients by
deploying different types of bio-medical sensors (for example:
body temperature, heartbeat, blood pressure,
electrocardiogram (ECG), electro encephalogram (EEG), etc.
sensors) for a long period of time, thus reducing the healthcare
costs. The bio-medical smart sensor nodes can either be
suitably placed on the body or implanted inside the body as
depicted in Figure.5. These bio-medical sensor nodes send the
sensed information to a coordinator (base station), located on
or near the body. The coordinator (base station) is responsible
for forwarding the collected information to the sink node. The
sink node will send the received data to the health care centre
or any other destination.
Figure.5.Body Area Network as part of IoT
Benefits IoT in Health Care include:-
 Clinical care: Hospitalized patients whose
physiological status requires close attention can be constantly
monitored using IoT-driven monitoring. This type of health
care solution employs sensors to collect comprehensive
physiological information and uses gateways and the cloud to
analyze and store the information and then send the analyzed
data wirelessly to caregivers for further analysis and review. It
replaces the process of having a health professional come by
at regular intervals to check the patient‘s vital signs, instead
providing a continuous automated flow of information. In this
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way, it simultaneously improves the quality of care through
constant attention and lowers the cost of care by eliminating
the need for a caregiver to actively engage in data collection
and analysis.
 Ongoing monitoring: With Internet of Things
technology — such as wireless EKG machines , devices for
diabetics to track their data, and necklaces or belts that
transmit heart rate data — healthcare professionals can collect
and store real-time information about their patients, and even
be alerted when something is wrong and action is needed. The
Internet of Things can transform healthcare from a reaction-
based process into an ongoing one.
 Scaled expertise: A challenge in healthcare today is
that expertise is often limited to an individual or location. For
example, a doctor who‘s an expert in a particular procedure
can only be in one place, and with one patient at a time. But
the Internet of Things is helping technology evolve to allow
for physicians to be able to perform procedures remotely.
 Remote monitoring: There are people all over the
world whose health may suffer because they don‘t have ready
access to effective health monitoring. But small, powerful
wireless solutions connected through the IoT are now making
it possible for monitoring to come to these patients instead of
vice-versa. These solutions can be used to securely capture
patient health data from a variety of sensors, apply complex
algorithms to analyze the data and then share it through
wireless connectivity with medical professionals who can
make appropriate health recommendation .
 Hospital prevention: Thanks to ongoing
monitoring, Internet of Things technology can also help
patients know when a trip to the hospital isn‘t needed.
The devices continually sending health data to doctors can
open up the communication and treatment channels so that
unnecessary hospital trips are prevented.
IV. COMPLEX EVENT PROCESSING
Complex Event Processing (CEP) technologies have been
used for situational awareness and decision making for quite
few years. It has regained focus owing to the rising demand
for on-the fly processing of events. Growing popularity of
Big Data and Internet of Things (IoT) coupled with the need
for real-time analytics have fueled the rapid growth of CEP
technologies and tools.
The idea of Complex Event Processing (CEP) was first
introduced and discussed in detail by Luckham [14]. It is in
recent years that CEP has evolved and gained prominence as
separate discipline by building upon different technology
paradigms like discrete event systems, active databases, Data
Stream Management Systems (DSMS), stream reasoning and
network management etc. Eckert and Bry [15], defined
Complex Event Processing as a technology which
encompasses methods, techniques, and tools for processing
events while they occur, i.e., in a continuous and timely
fashion. According to Luckham [13], CEP consists of
principles for processing clouds of events to extract
information, together with technologies to implement those
principles.
Several CEP engines and frameworks have been proposed
and/or developed over past decade and deployed over various
domains and problem areas like business process monitoring,
healthcare, smart cities, traffic analysis, fraud detection,
automation etc. Comprehensive CEP frameworks [18] will be
an idle fit for healthcare domain. CEP engines can analyze
events and related data which come from various sources
(health sensors, environment sensors etc.) in real-time and
provide insights for a better healthcare.
CEP can analyses event from personal sensors in
correlation with smart home sensor are provides insights
which can answers questions pertaining to linkage between
personal health, food consumed, quality of lifestyles etc. CEP
can also analyzed health changes in correlation with
environmental factors like humidity, air quality, temperature
etc by subscribing to events from environment monitoring
sensors.
Parts of the big data that is of no interest it can be filtered
with Complex Event Processing (CEP) as they get into the
system and selected data can be compressed by orders of
magnitude. One challenge is to define these filters in such a
way that they do not discard useful information. Complexities
of Big Data associated with the sheer volume can be handled
by suitable integration with Complex event processing.
V.HOLISTIC HEALTHCARE
Big Data, CEP and IoT together holds a great potential to
change the present scenario of healthcare, starting from drug
discovery to remote monitoring of patients, to faster
settlement of health insurance, to improved clinical
outcomes. The three native properties (volume, variety,
velocity) along with the acquired properties (variability, value
and veracity) define the key characteristics of big data which
are very much suited to healthcare issues. IoT is useful in
collecting real time data from patients anytime, anywhere,
any place, and easily relates to all the three native properties
of big data. Current practice of collecting patient's data at
bedside involves writing it down to a paper spreadsheet and
then the notes are typed in a data entering finally The data is
transmitted to a database server that organizes, indexes, and
make it accessible through a database interface and then data
is analyzed by doctors, pharmacists etc. It is now clear that
there is latency between data gathering, information
accessibility and this will grow more as there is increase in
volume of data in terms of health databases, clinical data,
health insurance data, repositories which can provide better
results for better future health care if analyzed properly with
the help of complex event processing and big data analytics.
This paper proposes a holistic health care approach for
performing complex event processing on the real time data
coming from wearable and non wearable sensors whose
volume, velocity; variety is huge in terms of storage and
processing. The elements and interactions are described
below and shown in Figure.6.
IoT Module: Contains the smart elements to extract real
time data, transform and load the health data. The data
generated from these smarter things is very sensitive and
large enough in terms of volume, variety and velocity. So,
instead of storing the data in databases for future use, better is
to analyze the data at that time only, with the help of
Complex Event Processing.
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CEP Module: - CEP will extract real time data from IoT
devices as soon as it is generated, and that will be analyzed
by applying real time event analytics , and more over CEP
can extract the information from the Big data databases on a
need basis, which are large enough in terms of volume and
byapplying analytics to find the patterns and analyze the data
of the patients regarding the past and present health.
Big Data Analytics: - Big data analytics comes into play in
order to unlock the value of data, organizations need Inferring
knowledge from complex heterogeneous patient sources
Leveraging the patient/data correlations in longitudinal
records. Understanding unstructured clinical notes in the right
context. Efficiently handling large volumes of medical
imaging data and extracting potentially useful information
and biomarkers. Analyzing genomic data is a computationally
Figure .6. Holistic Healthcare System (Integration of CEP, Big Data, IoT)
intensive task and combining with standard clinical data adds
additional layers of complexity. Capturing the patient‘s
behavioral data through several smart sensors; their various
social interactions and communications. Take advantage of
the massive amounts of data and provide right intervention to
the right patient at the right time. Personalized care to the
patient Potentiallywill benefit all the components of a
healthcare system i.e., provider, payer, patient, and
management.
VI. CONCLUSION
Big Data Analytics ,Complex Event Processing and IoT
together have extreme potential in solving exsisting and
future problem of healthcare industry. Application of these
technologies is still in infancy in the healthcare domain. With
renewed focus on better healthcare, growth in population ,
increasing prices, healthcare industry has to embrace cutting
edge technoliges like for effective and efficeint functioning.
Such application of technology should happen in a integrative
manner such as to deliver a complete healthcare solution.
VII. REFERENCES
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[3] Aleksandar Milenkovic, Chris Otto, and Emil
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[4] Big Data is the Future of Healthcare, White Paper Cognizant
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[5] TDWI_Best Practices Report on Big Data Analytics, 4th
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[7] Tawseef Ahmad Naqishbandi,Imthyaz Sheriff C, ―A Resilient
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[11] Dave EvansThe Internet of Things How the Next
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[20] Fabienne Abadie, Cristiano Codagnone, Marc van

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