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© 2012 International Business Machines Corporation
Putting IBM Watson to Work In Healthcare
Martin S. Kohn, MD, MS, FACEP, FACPE Chief Medical Scientist, Care Delivery Systems IBM Research marty.kohn@us.ibm.com
© 2010 IBM Corporation
IBM Research
Watson Jeopardy!
© 2010 IBM Corporation
IBM Research
Health Plans / Payers Private – BCBS plans, large national plans, mid-sized regional plans
Government / National Plans, Medicare Medicaid
Pharmacies Pharmacy Benefit Management
Retail Clinics
Drug Developers Large Pharma, Integrated Biotech, Research Biotech
Medical Devices Imaging
Archiving & Retention
Solution Providers IT Infrastructure and Service
Providers, Application Providers
Patient Education Healthy Lifestyles
Transaction Services Claims Processing
Banks / Health Savings
Healthcare Providers Integrated Delivery Networks, Large University Medical Centers, Independent Community Hospitals, Physician Private Practices
Public Health Pandemic readiness
Vaccine inventory & distribution Sanitation & public safety
We approach HCLS as an ecosystem of constituents centered around the needs of patients and consumers
Patients / Consumers
Health Clubs Health & Wellness Programs
Government Agencies Regulatory & Research
Agencies, FDA, WHO, DHHSS, CDC, NIH, Health Ministries
© 2012 International Business Machines Corporation 5
90% of the world’s data was
created in the last two years
80% of data in the world is
unstructured making decisions
more complex
200% data growth, in the next two years fed by 1T connected
devices
1 in 5 diagnoses are estimated to be inaccurate or incomplete
Volume
Variety
Velocity
Veracity
75 new clinical trials start every day in the US
alone
2X medical information is doubling every 4
years
$750B or 30 cents of every
dollar spent on healthcare in the US
is wasted
Healthcare is “dying of thirst in an ocean of data”
© 2012 International Business Machines Corporation 6
Personalized Medicine
Evidence-based Medicine
Why Watson for healthcare?
! Shift from Fee-for-Service to ACOs
! Focus on Wellness and Prevention
! Universal coverage
! Costs are 18% of US GDP
! 34% of $2.3T US spend is waste
! Costs can vary up to 10x
! Diagnosis and treatment errors
! Shortage of MDs ! Demand for remote
medicine
! Medical data doubles every 5 years
! Detailed patient biomedical markers
! Targeted therapies
Complexity
Policy C
hanges C
osts
Info Overload
© 2012 International Business Machines Corporation 8
Person Organization
L. Gerstner IBM
J. Welch GE
W. Gates Microsoft
“If leadership is an art then surely Jack Welch has proved himself a
master painter during his tenure at GE.�
Welch ran this?
! Noses that run and feet that smell? ! How can a house burn up as it burns down? ! Does CPD represent a complex comorbidity of lung cancer? ! What mix of zero-coupon, non-callable, A+ munis fit my risk tolerance?
Why is it so hard for computers to understand us?
© 2012 International Business Machines Corporation 9
Understands natural language and human communication
Adapts and learns from user selections and responses
Generates and evaluates evidence-based hypothesis
…built on a massively parallel architecture optimized for IBM POWER7
IBM Watson combines transformational technologies
1
2
3
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Watson enables three classes of cognitive services
Decide
• Ingest and analyze domain sources, info models • Generate evidence based decisions with confidence • Learn with new outcomes and actions • e.g. - Next generation Apps " Probabilistic Apps
Ask
• Leverage vast amounts of data • Ask questions for greater insights • Natural language inquiries • e.g. - Next generation Chat Discover
• Find the rationale for given answers • Prompt for inputs to yield improved responses • Inspire considerations of new ideas • e.g. - Next generation Search " Discovery
© 2012 International Business Machines Corporation 11
Baseline 12/06
v0.1 12/07
v0.3 08/08
v0.5 05/09
v0.6 10/09
v0.8 11/10
v0.4 12/08
Watson made incremental progress in precision and confidence
v0.2 05/08
V0.7 04/10
Prec
isio
n
IBM Watson Playing in the Winners Cloud
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Informed decision making: search vs. Watson
Decision Maker Search Engine
Finds Documents Containing Keywords
Delivers Documents Based on Popularity
Has Question
Distills to 2-3 Keywords
Reads Documents, Finds Answers
Finds & Analyzes Evidence Watson Understands Question
Produces Possible Answers & Evidence
Delivers Response, Evidence & Confidence
Analyzes Evidence, Computes Confidence
Asks NL Question
Considers Answer & Evidence
Decision Maker
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Medical journal concept annotations
Medications
Symptoms Diseases
Modifiers
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Inquiry Decomposition
Answer Scoring
Models
Responses with Confidence
Inquiry
Evidence Sources
Models
Models
Models
Models
Models Primary Search
Candidate Answer Generation
Hypothesis Generation
Hypothesis and Evidence Scoring
Final Confidence Merging & Ranking Synthesis
Answer Sources
Inquiry/Topic Analysis
Evidence Retrieval
Deep Evidence Scoring
Learned Models help combine and weigh the Evidence
Hypothesis Generation
Hypothesis and Evidence Scoring
How Watson works: DeepQA Architecture
1000�s of Pieces of Evidence
Multiple Interpretations of a question
100,000�s Scores from many Deep Analysis Algorithms
100�s sources
100�s Possible Answers
Balance & Combine
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Patient’s Story
Data Acquisition
Accurate Problem Representation
Generation of Hypothesis
Search for & Selection of Illness Script
Diagnosis
Key Elements of the Clinical Diagnostic Reasoning Process
Dr. Martin S. Kohn | Clinical Decision Support: DeepQA
Knowledge
Context
Experience
Bowen J. N Engl J Med 2006;355:2217-2225
© 2013 IBM Corporation
Solution
Use Case: Oncology Diagnosis & Treatment (ODT)
• Clinical support for patient assessment based on objective evidence – patient data, medical info, research, studies, articles, best practices, guidelines, etc.
• Evidence panel identifying key information used to support diagnosis, recommendations (e.g. suggested tests) and treatment options
• Systematic applied learning based on action taken and outcome derived
• Initial focus on lung, breast, prostate and colorectal cancers
Goal • Create individualized cancer diagnostic and treatment plans
• Enhance clinical confidence with greater access and understanding of information
• Speed time to evidence-based treatment
• Reduce diagnostic and administrative errors
• Accelerate the dissemination of practice-changing research
Assisting physicians with the diagnosis and treatment of cancer
IBM Confidential: References to potential future products are subject to the Important Disclaimer provided later in the presentation
IBM Watson goes to work in healthcare
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Watson’s Reasoning
• “Shallower” reasoning over large volumes of data • Delivers weighted responses to clinicians to assist in making
a informed evidence based decison ‒ Considers large amounts of data (e.g. EMR, Literature) ‒ Unbiased ‒ Learns
• Hits sweet spot of human judgment (e.g. problems with bias, Big Data)
• Identifies missing information • Watson’s interactive process helps clinician vector in on the
appropriate decisions • Not limited by database structure
17 Dr. Martin S. Kohn | Clinical Decision Support: DeepQA 14 Feb. 2012
© 2012 International Business Machines Corporation 18