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Presentation given by chief medical scientist, Dr. Med. Martin Kohn, IBM , Copenhagen May 3, 2013
© 2012 International Business Machines CorporationPutting IBM Watson toWork In HealthcareMartin S. Kohn, MD, MS, FACEP, FACPEChief Medical Scientist, Care Delivery SystemsIBM Researchmarty.email@example.com
© 2010 IBM CorporationIBM ResearchWatson Jeopardy!
© 2010 IBM CorporationIBM ResearchHealth Plans / PayersPrivate – BCBS plans, largenational plans, mid-sized regionalplansGovernment / National Plans,Medicare MedicaidPharmaciesPharmacy Benefit ManagementRetail ClinicsDrug DevelopersLarge Pharma, IntegratedBiotech, Research BiotechMedical DevicesImagingArchiving & RetentionSolution ProvidersIT Infrastructure and ServiceProviders, Application ProvidersPatient EducationHealthy LifestylesTransaction ServicesClaims ProcessingBanks / Health SavingsHealthcare ProvidersIntegrated Delivery Networks,Large University Medical Centers,Independent Community Hospitals,Physician Private PracticesPublic HealthPandemic readinessVaccine inventory & distributionSanitation & public safetyWe approach HCLS as an ecosystem of constituents centeredaround the needs of patients and consumersPatients /ConsumersHealth ClubsHealth & Wellness ProgramsGovernment AgenciesRegulatory & ResearchAgencies, FDA, WHO, DHHSS,CDC, NIH, Health Ministries
© 2012 International Business Machines Corporation590% of theworld’s data wascreated in thelast two years80% of data in theworld isunstructuredmaking decisionsmore complex200% data growth,in the next two yearsfed by 1T connecteddevices1 in 5diagnoses areestimated to beinaccurate orincompleteVolumeVarietyVelocityVeracity75new clinical trials startevery day in the USalone2Xmedical informationis doubling every 4years$750Bor 30 cents of everydollar spent onhealthcare in the USis wastedHealthcare is “dying of thirst in an ocean of data”
© 2012 International Business Machines Corporation6Personalized MedicineEvidence-based MedicineWhy Watson for healthcare?! Shift from Fee-for-Service to ACOs! Focus on Wellnessand Prevention! Universal coverage! Costs are 18% ofUS GDP! 34% of $2.3T USspend is waste! Costs can varyup to 10x! Diagnosis andtreatment errors! Shortage of MDs! Demand for remotemedicine! Medical datadoubles every 5 years! Detailed patientbiomedical markers! Targeted therapiesComplexityPolicyChangesCostsInfo Overload
© 2012 International Business Machines Corporation8Person OrganizationL. Gerstner IBMJ. Welch GEW. Gates Microsoft“If leadership is an artthen surely Jack Welchhas proved himself amaster painter during histenure at GE.Welch ranthis?! Noses that run and feet that smell?! How can a house burn up as it burns down?! Does CPD represent a complex comorbidity of lung cancer?! What mix of zero-coupon, non-callable, A+ munis fit my risk tolerance?Why is it so hard for computers to understand us?
© 2012 International Business Machines Corporation9Understandsnatural languageand humancommunicationAdapts and learnsfrom userselections andresponsesGenerates andevaluatesevidence-basedhypothesis…built on a massively parallelarchitecture optimized for IBM POWER7IBM Watson combines transformational technologies123
© 2012 International Business Machines Corporation10Watson enables three classes of cognitive servicesDecide• Ingest and analyze domain sources, info models• Generate evidence based decisions with confidence• Learn with new outcomes and actions• e.g. - Next generation Apps " Probabilistic AppsAsk• Leverage vast amounts of data• Ask questions for greater insights• Natural language inquiries• e.g. - Next generation ChatDiscover• Find the rationale for given answers• Prompt for inputs to yield improved responses• Inspire considerations of new ideas• e.g. - Next generation Search " Discovery
© 2012 International Business Machines Corporation11Baseline 12/06v0.1 12/07v0.3 08/08v0.5 05/09v0.6 10/09v0.8 11/10v0.4 12/08Watson made incremental progress in precision and confidencev0.2 05/08V0.7 04/10PrecisionIBM WatsonPlaying in the Winners Cloud
© 2012 International Business Machines Corporation12Informed decision making: search vs. WatsonDecision MakerSearch EngineFinds Documents Containing KeywordsDelivers Documents Based on PopularityHas QuestionDistills to 2-3 KeywordsReads Documents, FindsAnswersFinds & Analyzes Evidence WatsonUnderstands QuestionProduces Possible Answers & EvidenceDelivers Response, Evidence & ConfidenceAnalyzes Evidence, Computes ConfidenceAsks NL QuestionConsiders Answer & EvidenceDecision Maker
© 2012 International Business Machines Corporation13Medical journal concept annotationsMedicationsSymptomsDiseasesModifiers
© 2012 International Business Machines Corporation14InquiryDecompositionAnswerScoringModelsResponses withConfidenceInquiryEvidenceSourcesModelsModelsModelsModelsModelsPrimarySearchCandidateAnswerGenerationHypothesisGenerationHypothesis and EvidenceScoringFinal ConfidenceMerging & RankingSynthesisAnswerSourcesInquiry/TopicAnalysisEvidenceRetrievalDeepEvidenceScoringLearned Modelshelp combine andweigh the EvidenceHypothesisGenerationHypothesis and EvidenceScoringHow Watson works: DeepQA Architecture1000 s ofPieces of EvidenceMultipleInterpretationsof a question100,000 s Scores frommany Deep AnalysisAlgorithms100 ssources100 s PossibleAnswersBalance& Combine
© 2012 International Business Machines Corporation15Patient’s StoryData AcquisitionAccurate ProblemRepresentationGeneration of HypothesisSearch for & Selection ofIllness ScriptDiagnosisKey Elements of the Clinical Diagnostic Reasoning ProcessDr. Martin S. Kohn | Clinical Decision Support: DeepQAKnowledgeContextExperienceBowen J. N Engl J Med 2006;355:2217-2225
© 2013 IBM CorporationSolutionUse Case: Oncology Diagnosis &Treatment (ODT)• Clinical support for patient assessment based onobjective evidence – patient data, medical info,research, studies, articles, best practices,guidelines, etc.• Evidence panel identifying key information usedto support diagnosis, recommendations (e.g.suggested tests) and treatment options• Systematic applied learning based on actiontaken and outcome derived• Initial focus on lung, breast, prostate andcolorectal cancersGoal• Create individualized cancerdiagnostic and treatment plans• Enhance clinical confidence withgreater access andunderstanding of information• Speed time to evidence-basedtreatment• Reduce diagnostic andadministrative errors• Accelerate the dissemination ofpractice-changing researchAssisting physicians with the diagnosisand treatment of cancerIBM Confidential: References to potential future products are subject to the Important Disclaimer provided later in the presentationIBM Watson goes to work in healthcare
© 2012 International Business Machines Corporation17Watson’s Reasoning• “Shallower” reasoning over large volumes of data• Delivers weighted responses to clinicians to assist in makinga informed evidence based decison‒ Considers large amounts of data (e.g. EMR, Literature)‒ Unbiased‒ Learns• Hits sweet spot of human judgment (e.g. problems with bias,Big Data)• Identifies missing information• Watson’s interactive process helps clinician vector in on theappropriate decisions• Not limited by database structure17 Dr. Martin S. Kohn | Clinical Decision Support: DeepQA14 Feb. 2012
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