This document discusses using IBM's Watson technology in healthcare applications, specifically in radiology. It describes how Watson can consume medical reports and patient information to provide differential diagnoses, treatment recommendations, and next steps. It also outlines challenges of integrating Watson with existing medical systems and ensuring ongoing access to updated medical data sources. While Watson excels at data analysis, the document notes that technology may struggle to replace the human qualities patients desire in their doctors, such as empathy and personalization.

1. Calling Dr Watson to Radiology
Watson in Healthcare from Nuance
Nick van Terheyden, M.D.
Chief Medical Information Officer – Clinical Language Understanding
Nuance

2. Medicine used to be simple, ineffective
and relatively safe.
Now it is complex, effective and
potentially dangerous
Sir Cyril Chantler, Kings Fund Chantler C. The role and education of doctors in the delivery of health care.
Lancet 1999;353:1178-81u

3. Information Overload – Big Data
 Medical information is doubling approximately every 5 years
- Brent James, MD, MStat, Chief Quality Officer, Intermountain Health Care
 1.8 zetabytes of information created this year – majority of it
unstructured – 57 Billion 32Gb iPods (Source: IDC)
- That’s enough information to fill 57 billion 32GB Apple iPads (which
could build a mountain of iPads 25 times higher than Mt Fuji
 Watson can sift through 200 million pages in 3 secs

6. Watson DeepQA
HOW DOES IT WORK

7. DeepQA: The Technology Behind Watson
Learned Models
help combine and
weigh the Evidence
Evidence Balance
Sources & Combine
Answer Models Models
Sources Deep
Question Answer Evidence Models Models
Evidence
Candidate Scoring Retrieval 100,000’s Scores from
Primary 1000’s of
Scoring
many Deep Analysis
Answer Models Models
Search Pieces of Evidence Algorithms
Generation
100’s Possible
Answers
Multiple 100’s
Interpretations sources
Question & Final Confidence
Question Hypothesis Hypothesis and Evidence
Topic Synthesis Merging &
Decomposition Generation Scoring
Analysis Ranking
Hypothesis Hypothesis and Evidence Answer &
Generation Scoring Confidence
...

8. Architecture
User Experience
By Nuance and Partners…..
…..community of consumers
– large and small
CLU…… Cloud to Cloud DeepQA
Solutions for
….community of Healthcare
EMRs Content
Publishers
Large
Institutiona …..community of
l Providers CASE Content Partners

9. How Does Watson Perform in
Healthcare

10. Question and Answer Sets
Success
 Question: This hormone deficiency is associated with Kallmann's
syndrome.
- Passage: Isolated deficiency of GnRH or its receptor causes failure of
normal pubertal development and amenorrhea in women. This disorder
is termed Kallmann syndrome when it is accompanied by anosmia and
has also been termed idiopathic hypogonadotropic hypogonadism
(IHH).”
 Answer: GnRH
 Notes: We know that “GnRH” is a hormone (from the ontology) so
that lets us choose it as the most likely answer.

11. Question and Answer Sets
Miss
 Question: Eponym from Victorian literature for obesity
hypoventilation syndrome.
- Correct passage: Obesity-hypoventilation syndrome is also known as
pickwickian syndrome, in reference to Charles Dickens’…
- Correct answer: Pickiwickian Syndrome
- Wrong passage: Other clinical features associated with obesity-
hypoventilation syndrome are daytime hypersomnolence and cor
pulmonale.
- Wrong answer: cor pulmonale

12. Radiology Use Case
 Radiologists identifies radiological findings in a report
- Hepatic Lesion in Woman with History of Breast CA
- Too Small to Classify (TSTC)
- Offer follow up and alternative assessment criteria helping classify more
accurately for interval follow up or imaging work up
 Ask Watson
- discrimination points, appropriate follow up, critical results etc
 Watson Consumes the report and patient context information
- Offers next steps, differential list, new/recent information & links
 What possible next steps/studies would be appropriate to rule in/out
diagnosis and/or determine best course of treatment

13. Generic Use Cases
 Medical Diagnosis
- Consumption of medical records, results etc offering differential
diagnosis and probability analysis with links to underlying literature
sources
- True personalization of medicine based on large cohort historical data
analysis
 Interactive Model
- Requires real time access to patient data from multiple sources
including provider/patient interaction
- Ongoing refinement based on dynamic interaction and learning

14. Challenges
 Ambiguous human language
 Integration with existing systems – extract of complete data set for
history, results etc
- Often in disparate systems
- Non standard interfaces
- Non standard format
- Unstructured narrative
 Medical Data Sources and Ongoing Access and Updating
 Patient interaction with technology vs humans
- Telemedicine and consumer trend towards home based care

15. Replacing the Doctor?
 Study done by the Mayo Clinic in 2006 identified the most important
characteristics patients feel a good doctor must possess
 The Ideal clinician is
- confident,
- empathetic,
- humane,
- personal,
- forthright,
- respectful, and
- thorough
 These facets are entirely human and will be hard for technology to replace
Mayo Clin Proc. 2006;81(3):338-344

16. Calling Dr Watson to Radiology
Watson in Healthcare from Nuance
Nick van Terheyden, M.D.
Chief Medical Information Officer – Clinical Language Understanding
Nuance