ibm watson in healthcare

© 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 [email protected]

© 2010 IBM Corporation

IBM Research

Watson Jeopardy!


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”


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


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?


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


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


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


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


Medical journal concept annotations

Medications

Symptoms Diseases

Modifiers


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


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


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


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

ibm watson in healthcare

Health & Medicine

ibm power7ibm watson

evidencescoringhow watson

largenational plans

data growth

theworlds data

ocean of data

info modelsgenerate

medical datadoubles