Calling Watson to Ward 8 Stat

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This presentation will provide insight into Watson’s DeepQA process, the complexities and
details of the DeepQA challenge, and how these tools and techniques can be applied in a clinical setting. Prototype tools will be presented that open conceptual frameworks for
delivering advanced analytics in the radiologist’s workplace that offer rapid access to critical, specific and highly relevant data with corresponding links to underlying evidence.

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Calling Watson to Ward 8 Stat

  1. 1. Calling Watson™ to Ward 8 Stat Nick van Terheyden, MD Chief Medical Information Officer – Clinical Language Understanding Nuance Communications Inc Wednesday, February 2 9:45 - 10:45 AMDISCLAIMER: 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
  2. 2. Conflict of Interest Disclosure Nick van Terheyden, MD• Salary: Nuance Communications Inc © 2012 HIMSS
  3. 3. 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
  4. 4. Medicine used to be simple, ineffective and relatively safe. Now it is complex, effective and potentially dangerousSir Cyril Chantler, Kings Fund Chantler C. The role and education of doctors in the delivery of health care. Lancet 1999;353:1178-81u
  5. 5. Lifestyle defines „Group Health‟ 60 % - 80% of Group Health issues may be preventable– 58% Reduction in Diabetes – 60% Fewer Cardiac with lifestyle modification Events Hambrecht Circulation 2004;109:1371-78 Tuomilehto, 2001 NEJM 344(18): 1343-50– 60% Less Cancer – 44% Reduction in total De Lorgeril, Arch Int Med 1998;158:1181-87 mortality (NNT=16) Lyon Heart Study, Circulation 1999;99:779-85– 83% less Heart Disease – 45% Reduction in total– 91% less Diabetes mortality (NNT=2.4) Nurses Health Study, NEJM 2000;343:16-22, NEJM 2001;345:790-97 Indian Heart Study, BMJ 1992;304:1015-19– 73% less CHD – 40% Mortality Reduction GISSI-Prevenzione, Med.Diet AHA11/01: Marchioli– 69% less Cancer HALE Project. Knoops JAMA 2004;292:1433-1439 – 67% Mortality Reduction Indo-Med Study, Lancet 2002;360:1455-61] 5 2009 Continua Health Alliance Brigitte Piniewski, MD
  6. 6. Modifiable Health 0 Age 25 65 Wellness 60-80% Lifestyle Pre-Illness Unpredictable Health Predictable (Rules-based) Health Illness Death 62008 2009 Continua Health Alliance Brigitte Piniewski, MD 6
  7. 7. To put it another way…. Age Wellness 0 25 65 Pre-Illness Fun No Fun Illness Death 72008 2009 Continua Health Alliance Brigitte Piniewski, MD 7
  8. 8. Preventive Medicine – A warning Age 0 25 65 Wellness $$$ $$$? 60-80% Lifestyle Pre-Illness Unpredictable Health Predictable (Rules-based) Health Illness Death 82008 2009 Continua Health Alliance Brigitte Piniewski, MD 8
  9. 9. Challenge – Clinical Knowledge-Processing Burden“Current medicalpractice reliesheavily on the Knowledge processing requirementunaided mind torecall a greatamount of detailedknowledge – aprocess which, to This gapthe detriment of all injures patientsstakeholders, hasrepeatedly been Knowledge processing capacityshown unreliable”Crane and RaymondThe Permanente JournalWinter 2003 Volume 7 No.1Kaiser Permanente Institute forHealth Policy Years ago Today Slide courtesy of Dr Mike Bainbridge
  10. 10. 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
  11. 11. 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
  12. 12. Current Rate of Use for Selected Procedures 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)1. Balas, E. A., & Boren, S. A. (2000). Yearbook of Medical Informatics: Managing Clinical Knowledge for Health Care Improvement. Stuttgart, Germany: Schattauer Verlagsgesellschaft mbH
  13. 13. Reading to Keep up – Information Overload• Todays 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.
  14. 14. Meet Gerard Donovan….Cardiology Radiology Billing Plant Administration Pharmacy Food Lab About that Bill$3,943 $1,290 $1,433 services $3,233 Intensive Care $17,664 Operating Room $36,127 ... and his 150 medical staff...
  15. 15. HOW DOES IT WORKDEEPQA™
  16. 16. 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
  17. 17. DeepQA™: The Technology Behind Watson™ Learned Models help combine and weigh the Evidence Evidence Balance Sources & Combine Answer Models Models Sources DeepQuestion 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 sourcesQuestion & Final Confidence Question Hypothesis Hypothesis and Evidence Topic Synthesis Merging & Decomposition Generation Scoring Analysis Ranking Hypothesis Hypothesis and Evidence Answer & Generation Scoring Confidence ... Watson™ and DeepQA™ are trade names of IBM
  18. 18. ArchitectureUser ExperienceBy Nuance and Partners….. …..community of consumers – large and small CLU…… Cloud to Cloud DeepQA™ Solutions for ….community of HealthcareEMRs Content Publishers LargeInstitutional …..community of Providers CASE Content Partners
  19. 19. Comparison• Not simple search• Analysis of multiple concurrent complex contributing conditions and factors
  20. 20. Question and Answer Sets Success• Question: This hormone deficiency is associated with Kallmanns 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.
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. QuestionsFor More information I can be reached atNick van Terheyden, MDChief Medical Information Officer,Nuance Communicationswww.nuance.com/healthcareE-Mail drnick@nuance.com drnic1@gmail.comTwitter 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
  27. 27. Calling Watson™ to Ward 8 Stat Nick van Terheyden, MD Chief Medical Information Officer – Clinical Language Understanding Nuance Communications Inc Wednesday, February 2 9:45 - 10:45 AM

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