big data solutions for improving patient care
DESCRIPTION
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 IndustryTRANSCRIPT
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Big Data Solutions for Improving Patient Care
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Somenath Nag
Kolkata, 28th Jan, 2014
Director – ISV & Enterprise Solutions
ALTEN Calsoft Labs
www.calsoftlabs.com
www.bigdatainnovation.org www.unicomlearning.com
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
www.bigdatainnovation.org www.unicomlearning.com www.bigdatainnovation.org
Agenda Of The Talk:
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Challenges Faced by Healthcare Industry
Big Data in Healthcare
Use case for improving patient care
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Challenges Faced by Healthcare Industry
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
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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
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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
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Agenda Of The Talk:
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Challenges Faced by Healthcare Industry
Big Data in Healthcare
Use case for improving patient care
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Healthcare Primary Data Pools
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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.
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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
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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
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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
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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
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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
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New Value Pathways
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Agenda Of The Talk:
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Challenges Faced by Healthcare Industry
Big Data in Healthcare
Use case for improving patient care
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Connected Healthcare Framework
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RIS System – Standard Use case
Technician Performs Scan –Images Get
captured
(In Hospitals/Clinics)
Radiologists Analyses the Data
(In Hospitals/Clinics)
Data gets loaded to HER/EMR System
(In Hospitals/Clinics)
Doctors/Nurses refer HER/EMR System for
treatment
(In Hospitals/Clinics)
Patients/Insurance companies get
paper/Digital reports (in a file/CD)
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RIS System – Connected Healthcare Use case
Technician Performs Scan –Images Get captured
(In Hospitals/Clinics)
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
(In Hospitals/Clinics)
Patients/Insurance companies/Physicians
Refer Patients portals for reports
(in cloud server)
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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
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Segmenting and Detecting the Breast cancer through Image analysis
• Sample Image Datasets
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Big data Analytics Platform
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Classification System
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Results: Detection of malignant tumor
• Segmenting the malignant tumor
• Extracting feature set
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Results: Classifying Malignant/Benign cancer
Benign Malignant
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Organized by UNICOM Trainings & Seminars Pvt. Ltd.
Somenath Nag [email protected]
Thank You
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