Big Data Analytics in Medical Applications

Centre for Health Management and Research
(CHMR), IGMPI
Big Data Analytics in medical

Application
By : Dr Hoor Fatima
Associate Professor IGMPI
For feedback on this lecture please mail on –feedback@igmpiindia.org
Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

introduction
 The rapid increase in computational power, the number of internet

enabled data generating devices and the falling costs of data storage
itself which make data available to everybody for virtually no cost
have primarily lead to the emergence of big data.
 Health Care is one of the major areas where the use of big data
analytics has become monumental in rendering productive
performance as compared to the conventional means.
 Big data mainly deals with the storage and processing of large scale
and complex data sets for which the traditional methods prove to be
inept.

introduction
 By definition
“Big data in healthcare refers to electronic health datasets so large and

complex that they are difficult (or impossible) to manage with
traditional software and/or hardware; nor can they be easily managed
with traditional or common data management tools and methods”

introduction

V's OF THE HEALTH CARE BIG DATA

V's OF THE HEALTH CARE BIG DATA
Volume
 Contributing to the huge volume of healthcare data are various sources of data,
from traditional personal medical records and clinical trial data to new types of
data such as various sensor readings and 3D imaging
 Recently the proliferation of wearable medical devices has significantly added
fuel to the healthcare data. Those devices are able to continuously monitor a
series of physiological information, such as bio potential, heart rate, blood
pressure, and so forth
Variety
 Healthcare data could be classified into unstructured, structured, and semi
structured. Historically, most unstructured data usually come from office medical
records, handwritten notes, paper prescriptions, MRI, CT, and so on.[30]
 The structured and semi structured data refers to electronic accounting and
billings, actuarial data, laboratory instrument readings, and EMR data converted
from paper records.

V's OF THE medical BIG DATA
Velocity
 Compared with relatively static data such as paper files, xray films, and
scripts, it is gradually becoming more important and challenging to
process
 A real-time stream, such as various monitoring data, accurately and in a
timely manner, in order to provide the right treatment to the right patient
at the right time.
Veracity
 Healthcare data contains biases ,noise, and abnormalities, which poses a
potential threat to proper decision-making processes and treatments to
patients.
 The biggest challenge is determining the proper balance between
protecting the patient’s information and maintaining the integrity and
usability of the data
Stakeholders in healthcare
industry
Patients
 Patients want their everyday use of technology to flow seamlessly into
their medical care. Some want to comparison shop for medical treatment
as they do for consumer products.
 Everyone wants customer-friendly service, one-stop shopping, and better
coordination of care between themselves, caregivers and various
providers, with an ultimate goal of error-free, compassionate and
effective care.
Providers
 Providers want real-time access to patient, clinical and other relevant
data to support improved decision-making and facilitate effective,
efficient and error-free care.
 They want technology to be a transparent tool, not an encumbrance.

Researchers industry
 Researchers want new tools to improve the quality and quantity of
workflow – e.g., predictive modeling, statistical tools and algorithms that
improve the design and outcome of experiments and provide a better
understanding of how to develop treatments that meet unmet needs while
successfully navigating the regulatory approval and marketing process.
Pharmacy
 Pharmacy companies want to better understand the causes of diseases,
find more targeted drug candidates, and design more successful clinical
trials to avoid late failures and market safer and more effective
pharmaceuticals.
 Once in the market, they want accurate formulary and reimbursement

information to customize their marketing efforts, as well as less costly
post-marketing surveillance.
industry
Medical device companies
 Medical device companies, many of which have been collecting data for
some time from hospital and home devices for safety monitoring and
adverse event prediction, are beginning to wonder what to do with this
data, and how to integrate it with old and new forms of personal data.

Benefits of big data analytics
in medical
 Earlier detection of diseases and ailments when they are in early

stages and can be controlled and treated more easily and efficiently
 Individual health management by providing patient centric
services
 Improving the treatment methods and detecting healthcare fraud
more quickly and efficiently
Note: McKinsey estimates that big data analytics can enable more
than $300 billion in savings per year in U.S. healthcare, two thirds of
that through reductions of approximately 8% in national healthcare
expenditures

Opportunities of big data
in medical
Clinical Operations:
1)Comparative effectiveness research to determine more clinically relevant

and cost effective ways to diagnose and treat patients.
2) Clinical decision support systems to enhance the efficiency and quality of

operations; i.e., providing real-time information to emergency technicians,
nurses and doctors to improve triage, diagnosis, treatment choice, prevent
iatrogenic infections and readmissions, prescription and other medical
errors.
3) Other areas include increasing transparency about medical data, remote

patient monitoring, and predictive analytics to identify individuals who
would benefit from proactive care.
in medical
Research & development:
1) Predictive modeling to lower attrition and produce a leaner, faster, more

targeted R & D pipeline in drugs and devices;
2) Statistical tools and algorithms to improve clinical trial design and patient
recruitment to better match treatments to individual patients, thus reducing
trial failures and speeding new treatments to market; and
3) Analyzing clinical trials and patient records to identify follow-on

indications and discover adverse effects before products reach the market.

in medical
Public health:
1)Analyzing disease patterns and tracking disease outbreaks and

transmission to improve public health surveillance and speed response;
2) Faster development of more accurately targeted vaccines, e.g., choosing

the annual influenza strains; and,
3) Turning large amounts of data into actionable information that can be

used to identify needs, provide services, and predict and prevent crises,
especially for the benefit of populations

in medical
Genomic analytics:
 Add genomic analysis to the traditional healthcare decision making
process by developing efficient and effective gene sequencing
technologies. Utilize high throughput genetic sequencers to capture
organism DNA sequences and perform genome-wide association studies
(GWASs) for human disease and human microbiome investigations.
Fraud detection:
 Analyze a large amount of claim requests rapidly by using a distributed
processing platform (e.g., MapReduce for Hadoop) to reduce fraud,
waste, and abuse, such as a hospital’s overutilization of services, or
identical prescriptions for the same patient filled in multiple locations

in medical
Device/remote monitoring:
 Capture and analyze continuous healthcare data in huge amounts from

wearable medical devices both in the hospital and at home, for
monitoring of safety and prediction of adverse events.

Faculty of Healthcare Administration (FHA), IGMPI
Thank You

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Centre for Health Management and Research

Big Data Analytics in medical

For feedback on this lecture please mail on –feedback@igmpiindia.org

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

 The rapid increase in computational power, the number of internet

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

“Big data in healthcare refers to electronic health datasets so large and

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

 Once in the market, they want accurate formulary and reimbursement

Medical device companies

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

 Earlier detection of diseases and ailments when they are in early

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

1)Comparative effectiveness research to determine more clinically relevant

2) Clinical decision support systems to enhance the efficiency and quality of

3) Other areas include increasing transparency about medical data, remote

1) Predictive modeling to lower attrition and produce a leaner, faster, more

3) Analyzing clinical trials and patient records to identify follow-on

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

1)Analyzing disease patterns and tracking disease outbreaks and

2) Faster development of more accurately targeted vaccines, e.g., choosing

3) Turning large amounts of data into actionable information that can be

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

 Capture and analyze continuous healthcare data in huge amounts from

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

Centre for Health Management and Research (CHMR), IGMPI www.chmr.org.in

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