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Transcript of Big Data in Healthcare Raj Dalal Principal – CC&C Solutions Founder - BigInsights.
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Big Data in Healthcare
Raj DalalPrincipal – CC&C Solutions
Founder - BigInsights
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.• A Consulting company, established in 2002 with primary focus on Enterprise
and Solution Architectures.• Providing thought leadership in as a Gold member• Operating globally through dedicated professional teams
• Drawing on international best practices from TOGAF ® & Other standards • Providing Professional Services to numerous enterprises with a primary focus
on:– Health services– Public Sector– Financial Services & Insurance– Supply Chain & Distribution– Education– IT service providers
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Some of our worldwide customers
.
Fortune 500 Corporations, Leading Government Agencies, Global Service Providers, Universities
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New era of accelerated transformation
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Big Data
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Primary data pools are at the heart of the big data revolution in healthcare
Source: McKinsey Global Institute
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Big Data: Foundation for evolution of healthcare
Source: Hewlett Packard
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Empowering the Individual/Patient
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Personalised consumer healthcare
Source: Hewlett Packard
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Big Data is changing the paradigm: New value pathways in healthcare
Source: McKinsey Global Institute
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Application of early successes at scale could reduce US healthcare costs by $300-450Billion
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Big Data Use Cases
Source: Flutura
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Key word Analysis - Keyword mining of doctor’s/Lab transcripts using text mining and co-relations to patient outcomes
Source: Flutura
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Source: Flutura
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Telemedicine Analytics
Source: Flutura
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Apriori sequence analysis to define new clinical pathways
Source: Flutura
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Source: Flutura
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Predictive Analytics• Improve patient experiences through continuum of care by
examining referral patterns across providers
• Track spread of infection inside hospitals through well-placed sensors; predict risk of outbreaks through monitoring of multiple data sources
• Clinical revolution: predictive analytics for diagnosis, risk assessment, prognosis, therapeutic management
• Learn best ways of contacting patients to ensure that they follow treatments, nutrition guidelines, and can recognize symptoms
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Healthcare providers must develop a range of big data analytics capabilities
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Case StudyUS Depart of Veterans Affairs
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Overview of Project
• US Veterans are committing suicide at an alarming rate• Some disturbing statistics:
– 22 US veterans per day commit suicide– Suicide rates are roughly double those of general US population– 21% of overall suicides in USA are veterans– 12,500 calls per month to veterans crisis line (30% are related
of suicide)
• Durkeim project aims to proactively predict negative events such as suicide in the US veterans population and prevent it.
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Outcomes
• Dedicated to applied research on predictive suicide risk:– Multidisciplinary team of psychiatrists, US Veterans
Administration and AI (Machine learning)
• Phase 1:– Developed linguistic-driven prediction models to estimate
the risk of suicide. (from VA medical records).– Generated Datasets of single keywords and multi-word
phrases and constructed prediction models using machine learning algorithms
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Outcomes – Phase 2
• Sucidality prediction at scale
• Expand the data available for VA physicians with real time social media feeds from 100,000+ US Veterans to medical database
• Clinicians get a dashboard to monitor risks based on this realtime data
• Can provide real-time triage upon detection of critical event
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Durkheim Project (Phase 2) - ProactiveOpt-In Signup•Enabled privacy controls for user•Both ‘opt-in’ and ‘opt-out’ options are supported•Users are informed of the social networks that are enabled
Realtime monitoring of SocialMedia:•Facebook post and statusUpdates•Twitter •Google+ (coming soon)
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Clinician dashboard•Patients are listed by with overall risk rating/statistics and clinical notes•Patients flagged for risk ie Green (Low) to Red (High)•Status updated every 1 minute
Drill Down:- Suicide Ideation Rating- Time Series/Trend (Mental health ticker) -- Risk against other patients
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Clinician dashboard
Source content:Can view the social media updates
Clinician notes:Combined analysis of patients self-reported text as well as observations
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TechnologyMedical Database residing behind the medical firewall at Geisel School of Medicine where participant data is stored for analysis by medical professionals according to Human Subject Study/ HIPAA privacy rules
Information technology:• Hadoop technology use to store potentially massive datasets• Attivio used for unstructured search • Cloudera Search and Cloudera Impala being introduced into their machine learning framework to simplify the environment and reduce data movement
Statistical tools• Bayesian counters (B-counts) used for on-line near realtime model building and prediction. Underlying technology for B-counts is HBase.
• Initial prediction algorithm is Naïve Bayes (NB). The framework is currently being extended to incorporate Nearest Neighbours (NN) and Bayesian Network (BN) learning algorithms.
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Benefits/Metrics
• Phase 1 - Data model’s predictive accuracy was statistically significant (at least 65% accurate)
• Proactive platform for refining predictive models and suicide prevention at scale
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Summary
OutcomesReduction in number of
Veterans committing suicide
MetricsData model’s predictive
accuracy was statistically significant (at least 65%
accurate)
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New Style of IT in healthcareShaped by cloud, Big Data, security and mobility
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EA in Healthcare transformationNational Health vision, strategy and blue print for Healthy Nation
Users portal- 720 degree view
Business LayerTraditional | Collaborative | Outcome and Data Driven
Application LayerAdministration | Drug control | Tele Medicine | Real time and Continuous
Monitoring (Internet Of Things) (Services - based Models)
Data LayerStructured | Un-Structured | Real-Time | Bigdata
Technology LayerOn Premises -> Service-based Models (Cloud) | Hadoop | NoSQL
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CC&C Solutions Focus
• Big Data integral in Enterprise Architecture transformation
• Leadership in Open Group – standardisation (Platform 3.0)
• EA / TOGAF / Archimate training and consultation• Experiences in HealthCare domain– F100 customer (Johnson & Johnson)– E-health in various Asian countries and NSW Health