Calling Watson™ to Ward 8 Stat

Nick van Terheyden, MDChief Medical Information Officer – Clinical Language UnderstandingNuance Communications Inc

Wednesday, February 29:45 - 10:45 AM

DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

Watson™ and DeepQA™ are trade names of IBM

© 2012 HIMSS

Conflict of Interest DisclosureNick van Terheyden, MD

• Salary: Nuance Communications Inc

Learning Objectives• Recognize how technology can bring real-time knowledge and

the latest clinical developments to the clinicians’ workflow.• Define IBM’s Watson™ - an insight into the DeepQA™ process,

the complexities and details of the DeepQA™ challenge, and how these tools and techniques can be applied in a clinical context.

• Summarize the progress to date on the development, and implementation behind the scenes on Watson in healthcare.

• Demonstrate the data tsunami challenge faced in the clinical settings and how artificial intelligence technology like Watson™ can offer new means for rapid access to critical, specific and highly relevant data with corresponding links to underlying evidence.

• Identify an interim pathway for attendees to develop their own concrete steps to create an information rich yet physician friendly environment

Watson™ and DeepQA™ are trade names of IBM

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

2009 Continua Health Alliance Brigitte Piniewski, MD5

Lifestyle defines ‘Group Health’

– 58% Reduction in Diabetes with lifestyle modification

Tuomilehto, 2001 NEJM 344(18): 1343-50

– 60% Less Cancer De Lorgeril, Arch Int Med 1998;158:1181-87

– 83% less Heart Disease– 91% less Diabetes

Nurses Health Study, NEJM 2000;343:16-22, NEJM 2001;345:790-97

– 73% less CHD– 69% less Cancer

HALE Project. Knoops JAMA 2004;292:1433-1439

– 60% Fewer Cardiac EventsHambrecht Circulation 2004;109:1371-78

– 44% Reduction in total mortality (NNT=16)

Lyon Heart Study, Circulation 1999;99:779-85

– 45% Reduction in total mortality (NNT=2.4)

Indian Heart Study, BMJ 1992;304:1015-19

– 40% Mortality ReductionGISSI-Prevenzione, Med.Diet AHA11/01: Marchioli

– 67% Mortality ReductionIndo-Med Study, Lancet 2002;360:1455-61]

60 % - 80% of Group Health issues may

be preventable

2009 Continua Health Alliance Brigitte Piniewski, MD6

2008 6

0 25 65Age

Illness

Pre

-Illn

ess

Welln

ess

Unpredictable Health

Predictable (Rules-based) Health

Death

60-80% Lifestyle

Modifiable Health

2009 Continua Health Alliance Brigitte Piniewski, MD7

2008 7

0 25 65Age

Illness

Pre

-Illn

ess

Welln

ess

Death

To put it another way….

Fun

No Fun

2009 Continua Health Alliance Brigitte Piniewski, MD8

2008 8

0 25 65Age

Illness

Pre

-Illn

ess

Welln

ess

Unpredictable Health

Predictable (Rules-based) Health

Death

60-80% Lifestyle

Preventive Medicine – A warning

$ $ $ $ $ $ ?

Years ago Today

This gap injures patients

Knowledge processing capacity

Knowledge processing requirement

“Current medical practice relies heavily on the unaided mind to recall a great amount of detailed knowledge – a process which, to the detriment of all stakeholders, has repeatedly been shown unreliable”

Crane and RaymondThe Permanente Journal Winter 2003 Volume 7 No.1Kaiser Permanente Institute for Health Policy

Challenge – Clinical Knowledge-Processing Burden

Slide courtesy of Dr Mike Bainbridge

Information Overload – Big Data

• Watson™ can sift through 200 million pages in 3 secs– Graphic/analogy

• Medical information doubling every 5 years– Reference

• Brent James, MD, MStat, Chief Quality Officer, Intermountain Health Care; subject of The New York Times article “If Health Care is Going to Change, Dr. Brent James Will Lead the Way”

• http://www.nytimes.com/2009/11/08/magazine/08Healthcare-t.html?pagewanted=all

• 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

Time To Market• Studies suggest that it takes an average of

17 years for research evidence to reach clinical practice (it took 25 years for Beta blockers Rx for heart patients) (1)

• It takes an estimated average of 17 years for only 14% of new scientific discoveries to enter day-to-day clinical practice (2)

• Roughly 5% of autopsies reveal lethal diagnostic errors for which a correct diagnosis coupled with treatment could have averted death1. Balas, E. A., & Boren, S. A. (2000). Yearbook of Medical Informatics: Managing Clinical Knowledge for Health Care Improvement. Stuttgart, Germany:

Schattauer Verlagsgesellschaft mbH2. Westfall, J. M., Mold, J., & Fagnan, L. (2007). Practice-based research - "Blue Highways" on the NIH roadmap. JAMA, 297(4), p. 403.3. Shojania, KG, Burton EC, McDonald KM, Goldman L Changes in rates of autopsy-detected diagnostic errors over time: a systematic review. JAMA.

2003;289(21):2849-22856

Clinical Procedure Landmark Trial Current Rate of Use

Flu Vaccination 1968 (7) 55% (8)

Thrombolytic therapy 1971 (9) 20% (10)

Pneumococcal vaccination 1977 (11) 35.6% (8)

Diabetic eye exam 1981 (4) 38.4% (6)

Beta blockers after MI 1982 (12) 61.9% (6)

Mammography 1982 (13) 70.4% (6)

Cholesterol screening 1984 (14) 65% (15)

Fecal occult blood test 1986 (16) 17% (17)

Diabetic foot care 1983 (18) 20% (19)

Current Rate of Use for Selected Procedures

1. Balas, E. A., & Boren, S. A. (2000). Yearbook of Medical Informatics: Managing Clinical Knowledge for Health Care Improvement. Stuttgart, Germany: Schattauer Verlagsgesellschaft mbH

Reading to Keep up – Information Overload

• Today's experienced clinician needs close to 2 million pieces of information to practice medicine

• Doctors subscribe to an average of seven journals representing over 2,500 new articles each year, making it literally impossible to keep up-to-date with the latest information about diagnosis, prognosis and therapy

• Comparison of the time required for reading (for general medicine, enough to examine 19 articles per day, 365 days per year ) with the time available (well under an hour per week by British medical consultants, even on self-reports ).

• Furthermore, the interpretation of patient data is difficult and complicated, mainly because the required expert knowledge in each of the many different medical fields is enormous and the information available for the individual patient is multi-disciplinary, imprecise and very often incomplete.

Plant Administration Pharmacy$1,433

Foodservices

Lab$3,233

About that BillRadiology$1,290

Cardiology$3,943

Billing

Intensive Care$17,664

Operating Room$36,127

Meet Gerard Donovan….

... and his 150 medical staff...

DEEPQA™HOW DOES IT WORK

Watson™ DeepQA™ Technology

• Analyzing large volumes of structured and unstructured data

• Interprets and understands natural language questions

• Generates and evaluates hypothesis and quantifies confidence in answers

• Supports iterative dialog to refine results• Adapts and learns over time improving

results

. . .

Answer Scoring

Models

Answer & Confidence

Question

Evidence Sources

Models

Models

Models

Models

ModelsPrimarySearch

CandidateAnswer

Generation

HypothesisGeneration

Hypothesis and Evidence Scoring

Final Confidence Merging &

RankingSynthesis

Answer Sources

Question & Topic

Analysis

EvidenceRetrieval

Deep Evidence Scoring

Learned Modelshelp combine and

weigh the Evidence

HypothesisGeneration

Hypothesis and Evidence Scoring

QuestionDecomposition

1000’s of Pieces of Evidence

Multiple Interpretations

100,000’s Scores from many Deep Analysis

Algorithms

100’s sources

100’s Possible Answers

Balance& Combine

DeepQA™: The Technology Behind Watson™

Watson™ and DeepQA™ are trade names of IBM

Architecture

Cloud to Cloud

…..community of CASE Content Partners

…..community of consumers – large and small

CLU……

User ExperienceBy Nuance and Partners…..

….community ofContent Publishers

DeepQA™ Solutions for Healthcare

Large Institutional

Providers

EMRs

Comparison

• Not simple search• Analysis of multiple concurrent

complex contributing conditions and factors

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.

Question and Answer SetsMiss

• 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

Potential Use Cases• If We Only Knew What We Knew

– Bringing Evidence to the Point of Care– Consumption of medical records, results etc offering differential diagnosis and

probability analysis with links to underlying literature sources– Draws on the specifics of a patient case and vast volumes of clinical data and

medical– Highly granular results tailored to a particular patient’s conditions, demographics,

history– True personalization of medicine based on large cohort historical data analysis

• Acting on What We Know– Medication dosage: guidelines, clinical research findings for specific patient– Adverse drug reactions: computational model + research database populated by

Watson– Treatment Options: contextualized to patient– Standard of Care: aligning treatment to standards– Trending guidelines: recently published, pre-official– Post-Operative Discharge and Follow up– Entry of symptoms or symptomatic trends can trigger alerts for follow up– Ongoing refinement based on dynamic interaction and learning– Medical avatar for treatment and management of chronic conditions

Long Term Objectives• Creation of a state of the art system oriented to evidence

based decision making in healthcare, where such a system – Reports the suggested decisions and decision processes– Reports the aggregated data from clinical processes– Defined as real-time or retrospective system– Designed to assist medical professions involved in the patient life cycle, in

diagnosis and treatment of a patient

• Applying and expanding Watson’s framework in conjunction with Clinical Language Understanding, medical data and medical ontology

• Integrated into medical workflow and learn over time

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

• Patient interaction with technology vs humans– Telemedicine and consumer trend towards home

based care

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

Questions

For More information I can be reached atNick van Terheyden, MDChief Medical Information Officer,Nuance Communicationswww.nuance.com/healthcare

E-Mail drnick@nuance.comdrnic1@gmail.com

Twitter http://twitter.com/drnic1Voice of the Doctor http://drvoice.blogspot.com/LinkedIn http://www.linkedin.com/in/nickvtPlaxo http://nvt.myplaxo.comFaceBook http://facebook.com/drnic1Google Voice (301) 355-0877

Calling Watson™ to Ward 8 Stat

Nick van Terheyden, MDChief Medical Information Officer – Clinical Language UnderstandingNuance Communications Inc

Wednesday, February 29:45 - 10:45 AM