The document summarizes an IBM Watson presentation about how Watson works and potential opportunities for integration. It describes how Watson uses natural language processing to understand unstructured data and formulate hypotheses by weighing evidence from its training corpus. Examples of Watson's applications include customer engagement by resolving issues, medical diagnosis by analyzing patient records, and scientific discovery by providing insights from research data. The presentation notes IBM's $1B investment in Watson and examples of partnerships with Wellpoint to support physicians and CVS to provide health recommendations at pharmacies using Watson.

1. Lunch and Learn July 29 2015
IBM Watson

2. ● How it works
● Brainstorm ideas for Potential Opportunities for Ayogo development,
● Integration into Empower. Ayogo.com/Empower
Agenda

4. The Era of Cognitive Computing

5. Summary
Watson combines individual, social, clinical, public-domain and licensed data
sources on a cloud data sharing hub, and uses cognitive analytic technology to
provide relevant information to drive new insights.
Users can ask Watson for help using natural language questions. Watson then
formulates and validates hypotheses based on past behaviours and available
information.
IBM is investing $1B in Watson and $100M in cognitive app development to
improve health engagement.
Watson is being marketed as part of IBM’s BlueMix (IBM Cloud) Platform as a
Service

6. How It Works
https://youtu.be/_Xcmh1LQB9I

7. Watson Uses Natural Language
● Watson can understand understand unstructured data (80% of all data)
● Uses natural language, which is ambiguous, jargony and idiomatic.
● Doesn’t use search for keywords.
● Reads and interprets text, using grammar, relational content & modes of
thought to understand intent.

8. Watson Learns a Corpus
● Entering a particular field, Watson is assigned a corpus to learn focused on
a specific problem and the entire history of evidence for that specific
problem
● It can be trained using human-curated content or a new standard of care.
● Once trained it draws inferences

9. Human Experts Train Watson
● Humans train it, using machine learning
● An expert uploads question/answer pairs that teach it linguistic patterns of
meaning in the domain

10. Weighted Evidence Scores
1. Accepts a question in natural language
2. Forms a Hypothesis
3. Looks for evidence - Watson selects relevant passages from the corpus
4. Weighs what it finds and rates its confidence in its answers
5. Learns from the interaction, improves confidence

11. Use Case: Customer
Engagement1. Uses naturalistic communication patterns to isolate the root of a
customer’s problem.
2. Takes customer’s history into account in devising a solution.
3. Can scale expertise rapidly leading to cost reduction and increased
customer satisfaction.

12. Use Case: Data Exploration
1. Uses an organization’s body of data to present digested relevant
information to users.
2. Runs analytics on natural language content to provide insight into trends,
etc.
3. Customizable Question/Answer API to access Watson capabilities by
organizations.

13. Use Case: Scientific Discovery
1. Trained to use corpus of internal research, as well as public-domain
(google scholar) and licensed data sources to provide relevant
information to drive insights.
2. Watson learns patterns of information from past exposure
3. Watson then formulates and validates hypotheses based on available
information.

14. Use Case: Medical Diagnosis
1. Watson analyzes a patient’s medical record.
2. It identifies potential evidence-based treatment options from medical
corpus
3. Finds and provides supporting evidence from a wide variety of sources.

15. Case Study: Wellpoint
● Watson-supported physician decision support
● Wellpoint prompts physicians with refined, relevant hypothesis and
confidence-scored evidence to support diagnosis & treatment
recommendations
● WellPoint trained Watson with 25,000 historical cases.

16. Dr. Nussbaum
Wellpoint AI/Integrative
Information Goal - to lead patient to MOST
appropriate treatment.
Trained on policies and
procedures. Watson provides a
more consistent path for
treatment. Watson interprets
longitudinal patient records +
personality insights to
customize and support decision
Streamlines gathering and
sharing of information, reduces
reviews and improves decision
support at point of care

17. Wellpoint
50% of WellPoint nurses using Watson.

18. Oncology an excellent use case
● 14 US & Canadian cancer institutes using Watson to choose therapies
based on a tumor’s genetic fingerprint including BC Cancer Agency, Duke,
NYGC, Sanford, USCCAMM, Cleveland Clinic, Buffett Cancer Center, Yale
Cancer Center, UNC Lineberger Cancer Center.
● Each cancer has multiple treatment options. DNA of patient’s tumor
analyzed to see mutation driving the malignancy, then fit drug to mutation.
● Researchers at Memorial Sloan Kettering taught Watson NCCN guidelines
and American Oncology and 1.5M patient records over time.
● Probabilistic Therapy approach: 22 cancers - being used by Anthem -
Watson decision support program to personalize recommendations by
patient and cancer make-up.
● Sources:
● http://www.reuters.com/article/2015/05/06/ibm-cancer-
idUSL1N0XV26220150506

19. CVS – IBM Partner for Massive
Roll Out• IBM and CVS hope to find people at risk for illnesses before they've gotten
sick and create custom programs to help them avoid the illness.
• CVS will use Watson and work with IBM to create algorithms that relate
physiological indicators and behaviors to better flag declines in health.
• The IBM-CVS partnership could transform the roles of a pharmacist,
healthcare providers and specialists.
• CVS has 7800 Stores. Imagine them equipped with Watson at a kiosk that
you and/or your pharmacist consult about your health, verify a doctor's
prescription or ask for one based on the symptoms, and that can tell you to
alert your doctor about the need for a med change.
• Partnership announced July 30 2015 – so watch for developments!

20. Presented by Mavis Dixon
Mavis@ayogo.com