Big Data Solutions for Improving Patient Care

www.bigdatainnovation.org www.unicomlearning.com

www.unicomlearning.com

Big Data Solutions for Improving Patient Care

www.bigdatainnovation.org

Somenath Nag

Kolkata, 28th Jan, 2014

Director – ISV & Enterprise Solutions

ALTEN Calsoft Labs

www.calsoftlabs.com

http://www.unicomlearning.com/

http://bigdatainnovation.org/




Connected Healthcare Time for New Perspective • An IDC source says, the healthcare industry is

one of the highest-ranked industries for year-over-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

[email protected] http://in.linkedin.com/in/somenathnag

www.calsoftlabs.com



http://in.linkedin.com/in/somenathnag

www.bigdatainnovation.org www.unicomlearning.com www.bigdatainnovation.org

Agenda Of The Talk:


Challenges Faced by Healthcare Industry

Big Data in Healthcare

Use case for improving patient care







Strong need for cost reduction

Strong need for operating efficiencies and increased

productivity

Need to automate care delivery processes and systems

Need to modernize legacy applications and systems

Comply with regulations and security mandates

Use data to analyze and improve clinical and business performance

Expand access to care

Transition from reactive to proactive

care

Demonstrate greater healthcare value

to all stakeholders

to improve sustainability

4




New Streams of Data

2014 2016

5

• +1 billion smart phones will enter service

• 3 billion IP-enabled devices

• 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




The Healthcare Data Explosion

6

2012

500

petabytes

Worldwide healthcare

data is expected to

grow to

50 times the current

total

2025

25,000

petabytes




Agenda Of The Talk:










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




Agenda Of The Talk:










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


Doctors/Nurses refer HER/EMR System for

treatment


Patients/Insurance companies get

paper/Digital reports (in a file/CD)




RIS System – Connected Healthcare Use case

Technician Performs Scan –Images Get captured


Data moves to Cloud server, processed by analytics engine for

prognosis

(In Cloud Server)

Radiologists refer to the prognosis and own

findings for arriving at a decision

(In Cloud server)

Reports are pushed to Patient portals/HER/EMR

System

(In Cloud/ Hospitals/Clinics)

Doctors/Nurses refer the HER/EMR system for

Reports


Patients/Insurance companies/Physicians

Refer Patients portals for reports

(in cloud server)




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



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Organized by UNICOM Trainings & Seminars Pvt. Ltd.

[email protected]

Somenath Nag [email protected]

Thank You

www.bigdatainnovation.org

www.calsoftlabs.com






Technology

Big Data Solutions for Improving Patient Care