Big Data Solutions for Improving Patient Care

www.bigdatainnovation.org

www.unicomlearning.com

Big Data Solutions for Improving Patient Care
Somenath Nag
Director – ISV & Enterprise Solutions

ALTEN Calsoft Labs
Kolkata, 28th Jan, 2014
www.calsoftlabs.com




Connected Healthcare Time for New Perspective
•

An IDC source says, the healthcare industry is
one of the highest-ranked industries for yearover-year growth and five-year compound
annual growth rates with a worldwide
average of 7.0% growth for FY12 in software.

•

Increasing pressure to both mine & Report
clinical, operational, supply chain, finance &
HR, and workforce data to improve patient
care, while complying with federal
regulations and manage costs.

•

This presentation discusses the concepts of
Big Data in Healthcare & how it can help care
providers to improve operational efficiency,
productivity, and quality of care. This
presentation discusses the concepts of
connected healthcare and how it will change
the Healthcare Industry

Somenath Nag
Director – ISV & Enterprise
Solutions,
ALTEN Calsoft Lab
Somenath.nag@calsoftlabs.com
http://in.linkedin.com/in/somenathnag

www.calsoftlabs.com



Agenda Of The Talk:

Challenges Faced by Healthcare Industry

Big Data in Healthcare

Use case for improving patient care




Challenges Faced by Healthcare Industry
Strong need for cost reduction

Strong need for operating
efficiencies and increased
productivity

Expand access to care

Need to automate care delivery
processes and systems

Transition from reactive to
proactive
care

Need to modernize legacy
applications and systems

Demonstrate greater healthcare
value
to all stakeholders

Comply with regulations and
security mandates

4

Use data to analyze and improve
clinical and business performance

to improve sustainability



New Streams of Data

• +1 billion smart
phones will enter
service
• 3 billion IP-enabled
devices

2014

• 4.9 million patients will use
remote health monitoring
devices
• 3 million patients will use a
remote monitoring device
via smartphone hub
• 142 million healthcare and
medical app downloads

2016
5



The Healthcare Data Explosion

2012

500
petabytes

Worldwide
healthcare
data is
expected to
grow to

50 times
the current
total

2025

25,000
petabytes

6


Healthcare Primary Data Pools




Characteristics of Healthcare Data - Volume
• In healthcare, data growth comes both from digitizing
existing data and from generating new forms of data.
• The is already exists a huge volume of healthcare data
that includes:
–
–
–
–
–
–

Personal medical records
Radiology images
Clinical trial data
FDA submissions
Human genetics and population data
Genomic sequences

• Newer forms of big byte data, such as 3D imaging,
genomics and biometric sensor readings, are also fueling
this exponential growth.



Characteristics of Healthcare Data - Variety
• Enormous variety of data
– Structured
– Unstructured
– Semi-structured

• Sources of new data streams, structured and unstructured
–
–
–
–

Fitness devices
Genetics and genomics
Social media
Research and other sources

• The potential of Big Data in healthcare lies in combining
traditional data with new forms of data, both individually
and on a population level



Characteristics of Healthcare Data - Velocity
• Most healthcare data has traditionally been quite static
– Paper files
– X-ray films
– Scripts

• But in some medical situations, real-time data becomes a
matter of life or death
– Trauma monitoring for blood pressure
– Operating room monitors for anesthesia
– Bedside heart monitors

• In between are the medium-velocity data
– Multiple daily diabetic glucose measurements
– Blood pressure readings
– EKGs



Characteristics of Healthcare Data - Veracity
• Data quality issues in Healthcare
– Life or death decisions depend on having the information right
– The quality of healthcare data, especially unstructured data, is
highly variable and all too often incorrect

• Issues faced in Healthcare data
– Is this the correct patient, hospital, payer, reimbursement code,
dollar amount?
– Diagnoses data
– Treatment data
– Prescription data
– Procedural data
– Correctly capturing outcomes



Different Stakeholders’ View of Big Data in Healthcare
• Patients:
–
–
–
–

Seamlessly medical care.
Customer-friendly service
Better coordination of care between themselves, caregivers and various providers
Error-free, compassionate and effective care.

• Providers wants Real-time access to patient, clinical and other relevant data to
– Support improved decision-making
– Facilitate effective, efficient and error-free care

• Researchers
– Improve the quality and quantity of workflow
– Provide a better understanding of how to develop treatments that meet unmet needs
while successfully navigating the regulatory approval and marketing process.

• Medical device companies
– Safety monitoring and adverse event prediction
– Integrate it with old and new forms of personal data



Different Stakeholders’ View of Big Data in
Healthcare (Contd.)
• Pharma companies
– Better understand the causes of diseases
– Find more targeted drug candidates
– Design more successful clinical trials to avoid late failures and market safer and more
effective pharmaceuticals
– Accurate formulary and reimbursement information to
• Customize their marketing efforts
• Less costly post-marketing surveillance.

• Payers
– Stratify population risk
– Sustainable business models

• Governments
– Reduce costs
– Enforce regulations
– Maximize the social value of data


New Value Pathways



Connected Healthcare Framework




RIS System – Standard Use case

Technician Performs
Scan –Images Get
captured
(In Hospitals/Clinics)

Radiologists Analyses
the Data

Data gets loaded to
HER/EMR System



Patients/Insurance
companies get
paper/Digital reports
(in a file/CD)

Doctors/Nurses refer
HER/EMR System for
treatment



RIS System – Connected Healthcare Use case


Patients/Insurance
companies/Physicians
Refer Patients portals for
reports
(in cloud server)

Radiologists refer to the
prognosis and own
findings for arriving at a
decision

(In Cloud Server)

Technician Performs Scan
–Images Get captured

Data moves to Cloud
server, processed by
analytics engine for
prognosis

(In Cloud server)

Doctors/Nurses refer the
HER/EMR system for
Reports

Reports are pushed to
Patient portals/HER/EMR
System
(In Cloud/
Hospitals/Clinics)



Prognosis of Bio Medical Image Data
• Mammogram images data is huge by nature and needs
distributed storage and computing capabilities
• Hadoop HDFS as the distributed file system and Mahout for
analyzing
• Eigencuts in Mahout for spectral clustering for image
segmentation
• Classification techniques like Logistic Regression for
classifying the cases into Benign, Malignant categories
under prognosis



Segmenting and Detecting the Breast cancer through
Image analysis

• Sample Image Datasets


Big data Analytics Platform



Classification System



Results: Detection of malignant tumor
• Segmenting the malignant tumor
• Extracting feature set




Results: Classifying Malignant/Benign cancer
Benign

Malignant



Thank You
Somenath Nag
somenath.nag@calsoftlabs.com

www.calsoftlabs.com

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UNICOM Trainings & Seminars Pvt. Ltd.
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Big Data Solutions for Improving Patient Care

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