IBM Watson

Healthcare Transformation for

Informa Healthcare AnalyticsTodd Kalyniuk, Partner, Healthcare

Transformation

December 10, 2014

• Watson Overview

• Watson for Healthcare

• Case Studies

© 2014 International Business Machines Corporation 2

Jeopardy Grand Challenge


Watson is ushering in a new era of computing

© 2014 International Business Machines Corporation

TabulatingSystems Era

ProgrammableSystems Era

CognitiveSystems Era

1900 1950 2011

4

Watson & HealthcareWatson is cognitive computing


Understands

natural language

Generates

and evaluates

hypotheses

Adapts

and learns

Watson understands me.

Watson engages me.

Watson learns and improves over time.

Watson helps me discover.

Watson establishes trust.

Watson has endless capacity for

insight.

Watson operates in a timely fashion.

Watson has graduated from a Startup to a full-fledged Business

R&D

Demonstration

Validation

Research

Project 2006 – 2010

Jeopardy!

Grand Challenge2011

Internal

Startup Division2011 – 2013

IBM Watson

Group2014 – present

Commercialization

$1B Investment

$100M for Ecosystem

NYC Headquarters


6

IBM Watson

family

IBM Watson

Advisors

IBM Watson

Solutions

IBM Watson

Foundations

IBM Watson

Cognitive FabricProvides the big data and

analytics capabilities that fuel

Watson

Products based on

Watson’s core attributes

and capabilities

APIs, tools, methodologies,

SDKs, and infrastructure that

fuels Watson

Customized solutions designed

to meet some of industries most

demanding needs leveraging

cognitive capabilities

IBM Watson

Ecosystems

The Watson Developer Cloud,

Watson Content Store and

Watson Talent Hub driving

innovation from partners

The Watson Family


• Watson Overview


• Case Studies


Why Watson for healthcare?

Personalized Medicine

Evidence-based Medicine 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

Po

licy

C

ha

ng

es C

ost

sInfo Overload

• Medical notes and discharge

summaries

• Patient history, symptoms and non-

symptoms

• Microbiology & Pathology reports

• Policy, Quality Reviews &

Satisfaction surveys

• Claims and case management

Review

• Forms based data and comments

• Emails and correspondence,

Tweets, text messages and online

forums

• Trusted reference journals

including portals

• Paper based records and

documents

Over 80% of stored patient health information is unstructured



Published

Knowledge

Knowledge-Driven Method Data-Driven Method

Observational

Data

• Longitudinal records

• Claims, Rx, Labs

• Patient reported data

• Scientific papers

• Books

• Guidelines

Closing the translational knowledge gap Personalized Insights from institutional data

From population averages … To insights for individual patient!

IBM’s Cognitive Computing strategy in Healthcare spans all

aspects of knowledge and data

© 2014 IBM Corporation 11

Cognitive computing will transform Healthcare


Engage Patients Improve Outcomes Control Costs

Care is determined by a

proactive plan to meet health

needs, with or without visits

We measure our quality with

analytics and make rapid

changes to improve it

Care is standardized according

to evidence based guidelines

and advance cognitive systems

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Watson empowers consumer engagement

IBM WatsonEngagement Advisor

What it does:

• Transforms client engagement by

knowing, engaging and empowering

clients where they are

• Develops client relationships by

reaching out to clients who do not

leverage traditional channels

• Empowers consumers and contact

center agents to take informed action

with confidence

How it does it:

• Answers questions and guides users

through processes with plain-English

dialogue

• Leverages natural language to

interact with users and build

knowledge and expertise

• Utilizes evidence evaluation and

learning to provide informed and

effective responses to users

© 2014 International Business Machines Corporation 131 Gartner Predicts

By 2020, the customer will manage 85%

of the relationship with an enterprise

without interacting with a human1

Patient IntakeEvidence-

based Insights

Outcomes Driven

Learning System

Watson Clinical Advisor solutions use a common design that enables

machine learning:

Business problem:

Need better individualized cancer treatment plans

Solution:

• Suggestions to help inform oncologists’ decisions based on 600K+ pieces of evidence and 2M pages of

text from 42 publications

• Analyzes patient data against thousands of historical

cases and trained through 5000+ Memorial Sloan-

Kettering MD and analyst hours

• Evolves with the fast-changing field

Attacking the cause of

one in four deaths

Watson helping oncologists treat cancer patients


IBM WatsonOncologyBuilt with Memorial Sloan Kettering

Watson Oncology helps medical oncologists and their care

teams address these challenges

61 y/o woman s/p

mastectomy is here to

discuss treatment options

for a recently diagnosed

4.2 cm grade 2 infiltrating

ductal carcinoma…

Prioritized Treatment

Options

+

Evidence Profile

Patient Case • Inclusion / exclusion

criteria

• Co morbidities

• Contraindications

• FDA risk factors

• MSK preferred

treatments

• Other guidelines

• Published literature -

studies, reports, opinions

from Text Books,

Journals, Manuals, etc.

Evidence

Watson OncologyKey Case Attributes

Candidate Treatment Options

Supporting Evidence

Extract key attributes from a patient’s case

1

Use those attributes to find candidate treatment options as determined by consulting NCCN Guidelines

2

Use Watson’s analytic algorithms to prioritize treatment options based on best evidence.

4

Guidelines


Search a corpus of evidence data to find supporting evidence for each option

3


Business problem:

• No easy way to search eligibility criteria at point of care to match patients to clinical trials

Solution:

• Identify all the relevant clinical attributes needed to search across clinical trials for a disease

• Instantly check the patient’s eligibility

• Provide an ordered list of relevant clinical trials with the degree of match

• Provide criteria (inclusion / exclusion) level evaluation based on the patient’s attributes

• Dynamically re-evaluate the case based on changes to clinical attributes

Overall only 3%1 of cancer

patients are on clinical trials

Watson quickly matches patients to clinical trials

IBM WatsonClinical Trial Matching

© 2014 International Business Machines Corporation 1 Medscape 18

Business problem:

Give medical students and doctors easier insight into

data to inform their diagnoses and decisions

Solution:

• Intuitive, new user interface to Watson’s power

revealing chains of evidence to support clinical

reasoning

• Analysis of whole EMRs to extract and visually present

summarized knowledge with semantic understanding

of context

Cognitive systems in a

classroom-based

setting

Watson facilitating medical school problem-based learning

methods

IBM WatsonWatsonPaths &EMR Assistant


Identifying patterns in genome sequencing and medical

data to unlock new insights.

IBM WatsonGenomic Analytics

Business problem:

Cannot accurately and comprehensively understand and take action on the Genomic Sequencing results.

Solution:

• Case specific analysis to identify mutations, gene

expression, tumor heterogeneity, etc.

• Identify drug options, provide rapid evidence retrieval

and patient molecular profile analysis.

Bridging the gap between

sequencing and personalized

medicine.

© 2014 International Business Machines Corporation 20http://www.research.ibm.com/articles/genomics.shtml

Business Problem:

•Data overload with 100,000 new cancer articles per

year and 5,000 new P53 related articles per year

•Time intensive process to identify new relationships

•Researchers’ natural bias limits outside perspective

Value of Watson:

•Extract and logically infer and reason over the

biological pathways and chemical / biological

relationships

•Generate hypotheses and new P53 approaches with

increased likelihood of success

Probing literature to

discover new

connections and

insights

Watson accelerates cancer research through understanding

of scientific language to identify protein kinase activity on

P53


IBM WatsonDiscovery Advisor

• Watson Overview


• Engagement Approach


Next Steps – Typical Watson Approach

Identify Opportunities

Cognitive Value

Assessment

Develop Benefits Case

Develop Roadmap

Core Watson

System Training

Train Watson in Domain

Business Domains Lines of Business

Business Functions Customer Support

Field Support

Cross Sell / Up Sell

Semantic Discovery

Users Service Center Agents

Customers

Store Reps

Expand Domains

23

2424

Thank you

Date post:	14-Jan-2017
Category:	Healthcare
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