Digital 2014 The Transformation of Healthcare Martin S. Kohn, MS, MD, FACEP, FACPE Chief Medical Scientist, IBM Researc

1. Digital 2014The Transformation of HealthcareMartin S. Kohn, MS, MD, FACEP, FACPEChief Medical Scientist, IBM Research

2. The transformation of healthcare has begun… Health Plans Unnecessary procedures Unsustainable costs Inconsistent prices Aging Populations Healthcare Reform Regulation Population Management Accountable Care Care Providers Individual Highly targeted therapies Disease complexity Shortage of MDs

3. The future will look very different… Cognitive Medical Decision Support Tools Health Plans Accountable Care Providers Care Providers EHRs, Labs, Genetic Biomarkers, Bio-Monitoring, New Targeted Therapies, Claim History, Family Histories, Patient Preferences, Clinical Trials, Drug Disclaimers, Medical Evidence

4. Businesses are“dying of thirst in an ocean of data” • 80% • of the world’s data today is unstructured • 90% • of the world’s data was created in the last two years • 1 Trillion • connected devices generate 2.5 quintillion bytes data / day 1 in 2 business leaders don’t have access to data they need 2.2X more likely that top performers use business analytics 83% of CIOs cited BI and analytics as part of their visionary plan

5. The pace of “Big Data” is accelerating… 4X 1 Billion $500 Billion projected health-related apps downloaded a year by 2016 avoidable annual costs by improving medicine adherence people over 60 unable to care for themselves by 2050 IBM Global Technology Outlook - 2012

6. Why is it sohard for computers to understandus? “If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE.” Welch ran this? • 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?

7. Watson made incremental progress in precision and confidence IBM Watson Playing in the Winners Cloud v0.8 11/10 V0.7 04/10 v0.6 10/09 v0.5 05/09 v0.4 12/08 v0.3 08/08 Precision v0.2 05/08 v0.1 12/07 Baseline 12/06

8. IBM Watson combinestransformational technologies 2 Generates and evaluatesevidence-based hypothesis 1 Understandsnatural language and human communication 3 Adapts and learnsfrom user selections and responses …built on a massively parallel architecture optimized for IBM POWER7

9. Watson enables three classes of cognitive services Ask • Absorb and leverage incredibly vast amounts of data • Ask nuanced questions for greater insights • Understand questions in our natural language Discover • Find deep rationale for given answers • Request additional information to improve responses • Move from basic search to Discovery Decide • Ingest and analyze domain sources • Generate evidence-based decisions with confidence • Learn with each new action and outcome

10. Medical journal concept annotations Diseases Symptoms Modifiers Medications

11. How Watson works: DeepQA Architecture Inquiry Models Models Models Models Models Models Responses with Confidence Learned Models help combine and weigh the Evidence Evidence Sources Balance & Combine Answer Sources Deep Evidence Scoring Evidence Retrieval Answer Scoring Primary Search 100,000’s Scores from many Deep Analysis Algorithms Candidate Answer Generation 1000’s of Pieces of Evidence 100’s Possible Answers 100’s sources Hypothesis Generation Synthesis Final Confidence Merging & Ranking Inquiry/Topic Analysis Hypothesis and Evidence Scoring Inquiry Decomposition Multiple Interpretationsof a question Hypothesis Generation Hypothesis and Evidence Scoring

12. Key Elements of the Clinical Diagnostic Reasoning Process Patient’s Story Knowledge Data Acquisition Accurate Problem Representation Context Generation of Hypothesis Search for & Selection of Illness Script Experience Diagnosis Bowen J. N Engl J Med 2006;355:2217-2225 Dr. Martin S. Kohn | Clinical Decision Support: DeepQA

13. Leveraging Electronic Clinical Documentation to Decrease Diagnostic Error Rates Role for Electronic Documentation Ensure ease, speed, and selectivity of information searches; aid cognition through aggregation, trending, contextual relevance, and minimizing of superfluous data Provide a space for recording thoughtful, succinct assessments, differential diagnoses, contingencies, and unanswered questions Provide checklists to minimize reliance on memory and directed questioning to aid in diagnostic thoroughness and problem solving Provide instant access to knowledge resources through context-specific “infobuttons” triggered by keywords in notes that link user to relevant textbooks and guidelines Goals and Features ofRedesigned Systems • Providing access to information • Recording and sharing assessment • Providing prompts • Providing access to information sources Can Electronic Clinical Documentation Help Prevent Diagnostic Errors? Gordon D. Schiff, M.D., and David W. Bates, M.D. N Engl J Med 2010; 362:1066-1069 Dr. Martin S. Kohn | Clinical Decision Support: DeepQA

14. THANK YOU! Learn more at: Martin S. Kohn, MD, MS, FACEP, FACPE Chief Medical Scientist, Care Delivery Systems IBM Research marty.kohn@us.ibm.com