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Healthcare Innovation and Transformation - Dr. Ken Yale

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We are living in the greatest time in human history! People are living their lives on smartphones and apps, measuring themselves with wearable devices like the Apple Watch, and improving their health and care with advanced analytic algorithms. Healthcare is adopting AI, Machine Learning, and Deep Learning at an accelerated pace. “Healthcare is very important for people. We are democratizing it. We are taking what has been with the institutions, and empowering the individual to manage their health.
And we’re just getting started!” - Apple CEO Tim Cook, Jan 2019

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Healthcare Innovation and Transformation - Dr. Ken Yale

  1. 1. @docyale Data Science: the Future of Health and Care by Ken Yale, DDS, JD Predictive Analytics World June 19, 2019 Las Vegas, NV
  2. 2. @docyale What shall we Discuss? “Healthcare is very important for people. We are democratizing it. We are taking what has been with the institutions, and empowering the individual to manage their health. And we’re just at the front end of this!” - Tim Cook, CEO, Apple, January 8, 2019
  3. 3. @docyale 1. Strategy, People, Process, Technology “Predictive, Preventive, Personalized, Participatory”* DATA SCIENCE AND THE FUTURE OF HEALTH AND CARE *P4 Medicine Institute (http://www.p4mi.org) Deep Medicine, Eric Topol, 2019 2. Applied Advanced Analytics in Health and Care 3. The Future: ”Personal Health Management” vs “Population Health Management” aka “the average patient does not exist”
  4. 4. @docyale Sophistication Gain Standard Reports What happened? Source: Competing on Analytics, Davenport/Harris, 2007 Ad hoc reports How many, how often, where? Query/drill down What exactly is the problem? Alerts What actions are needed? Statistical analysis Why is this happening? Forecast/extrapolation What if these trends continue? Predictive modeling What will happen next? Optimization What’s the best outcome? Predictive and Prescriptive Analytics Descriptive Analytics DATA SCIENCE MATURITY
  5. 5. @docyale Strategy, People, Process, Technology?
  6. 6. @docyale HEALTH SYSTEM ANALYTICS STRATEGY Source: “Shifting Into High Gear: Health systems have a growing strategic focus on analytics today for the future”, Deloitte Insights, March 28, 2019 (http://bit.ly/2K7WNNA)
  7. 7. @docyaleSource: Preparing the Healthcare Workforce to Deliver the Digital Future, NHS, UK https://topol.hee.nhs.uk/ DATA SCIENCE WORKERS
  8. 8. @docyale Dimensions Capabilities Advanced Analytics Maturity Stages 1 Limited 2 Beginning 3 Momentum 4 Maturing 5 Visionary Structure Governance Policies Processes & Controls Security & Privacy Leadership Organization Culture Impact Strategy Business Need & Use Cases Analytics Tools & Techniques Deployment/Delivery Approach Management/Talent Variety, Volume, Velocity Data Data Access Data Integration Data Architecture/MDM Funding Resources Talent and Skills Roles & Responsibilities Training PROCESSES: ADVANCED ANALYTICS ASSESSMENT Source: “TDWI Advanced Analytics Maturity Model Guide,” The Data Warehouse Institute, 2018 (http://bit.ly/2K8b64G)
  9. 9. @docyale TECHNOLOGY IN HEALTH PLANS 1990 TO PRESENT Sources: “Business Intelligence, Analytics, and Data Science, 4th Edition” Sharda R, Delen D, Turban E, © 2017, Pearson Education, Inc. “Priorities for US Healthcare Payers 2018”, Blue Health Intelligence, July 2018 (https://bluehealthintelligence.com/hype-cycle-for-u-s-healthcare-payers-2018/)
  10. 10. @docyale KEY BARRIERS TO DATA SCIENCE IN HEALTHCARE https://www.datasciencecentral.com/profiles/blogs/seeing-the-ai-ml-future-in-healthcare-through-the-eyes-of-physici
  11. 11. @docyale KEY BARRIERS TO DATA SCIENCE IN HEALTHCARE Source: McKinsey & Company, May 2019, https://mck.co/2ExMb6T
  12. 12. @docyale KEY BREAKTHROUGHS IN HEALTHCARE Source: Oliver Wyman, in Clinically Integrated Care and Future of Population Health, Yale K, Miner G, HIMSS Annual Meeting 2015 Provider Value Evolution - Population Health Management - Descriptive Analytics - Clinical and Claims Data From To Volume, patient turnover Value, patient health Physician-Centered Patient-Centered Transactional, episodic Coordinated Care Sick care Wellness and prevention Inaccessible Convenient, 24/7 Unwarranted variation Evidence based protocol Consumer Retail Revolution - Personal Health Management - Predictive Analytics - Clinical, Claims, and Context Data From To Uninformed Informed/Share Decisions Limited engagement Patient Empowered Patient isolated Patient socially connected Limited Consequences Financial reward/incented Bricks, office hours Virtual, anytime/anywhere Physician opinion Evidence based facts Health System Devolution - Precision Health Management - Prescriptive Analytics - Comprehensive, Cradle-to-Grave Data From To Basic management Comprehensive life plan Symptomatic treatment Continuous monitoring One-size-fits-all Individual treatment Average patient diagnosis Individual patient diagnosis Normal range Specific results Medical competencies Empathy, presence, humankindness
  13. 13. @docyale KEY BREAKTHROUGHS FOR DATA SCIENCE IN HEALTHCARE 1) Digital Inputs = Rapid Growth in Sources of Digital Health Data 2) Data Accumulation = Proliferation of Digitally-Native Data Sets 3) Data Insight = Generated by Data Science using Accumulated & Integrated Data 4) Translation = Impact on Therapeutics & Healthcare Delivery 5) Personal Health Management = Consumer Retail Care Source: Adapted from Internet Trends 2017, by Mary Meeker, https://www.kleinerperkins.com/perspectives/internet-trends-report-2017 See Also: Handbook of Statistical Analysis and Data Mining Applications, Nisbet R., Miner G., Yale K., Elsevier/Academic Press, 2017. (http://a.co/d/ihMBBKs) 6) Outcomes = Measure Outcomes & Iterate… Innovation Cycle Times Compressing Ripe for disruption
  14. 14. @docyale Source: PBS, Propeller Health, TechCrunch, Livongo, Ayasdi, Flatiron, Xconomy, Kinsa, Omada Patient Empowerment & Health Management Propeller Health + Bluetooth Inhaler Sensor = Improved Medication Adherence + Insights Livongo + Connected Glucose Meter = Personalized Coaching + $100/Month Savings for Payers Improvements to Clinical Pathways / Protocol Ayasdi AI + Mercy Health System Patient Data = Clinical Anomaly Detection + Improved Clinical Pathway Development Flatiron + Foundation Med (FMI) = 20,000 Linked Cancer Patients Records + Personalized Medicine Preventative Health Kinsa + Crowdsourced Temperature Data = Local Flu Predictions + Proactive Treatments for Populations Omada + Preventative Program = 4-5% Body Weight Reduction + Reduced Risk for Stroke and Heart Disease KEY BREAKTHROUGHS: DIGITAL HEALTH EXAMPLES Source: Internet Trends 2017, by Mary Meeker, https://www.kleinerperkins.com/perspectives/internet-trends-report-2017
  15. 15. @docyale KEY BREAKTHROUGHS: HEALTHCARE CONSUMERISM Source: Internet Trends 2019 by Mary Meeker, https://www.bondcap.com/report/itr19/
  16. 16. @docyale KEY BREAKTHROUGHS: HEALTHCARE CONSUMERISM Source: Internet Trends 2018, by Mary Meeker, https://www.kleinerperkins.com/internet-trends
  17. 17. @docyale KEY BREAKTHROUGHS: HEALTHCARE CONSUMERISM Source: Internet Trends 2018, by Mary Meeker, https://www.kleinerperkins.com/internet-trends
  18. 18. @docyale KEY BREAKTHROUGHS: HEALTHCARE CONSUMERISM Source: Internet Trends 2019 by Mary Meeker, https://www.bondcap.com/report/itr19/
  19. 19. @docyale Examples of Data Science in Healthcare
  20. 20. @docyale * Lynch et al. Documenting Participation in a DM Program. JOEM 2006; 48(5) 1 Frazee et al. Leveraging the Trusted Clinician: Documenting Disease Management Program Enrollment. Disease Mgmt 2007; 10:16-29 EXAMPLE: CONNECTING WITH CONSUMERS 30% 15% 7% 3.5% 0 0.2 0.4 0.6 0.8 1 1.2 Eligible Contacted Participant Behavior Improvement
  21. 21. Prepare Data Segment Develop Personas - Health Insurance and Hospital Data - Aggregated, Organized, Enhanced - ”Exogenous” Data: behaviors and lifestyles Gather Internal Data Gather External Data Combine Data Discovery - Aggregate - Integrate - Normalize - Standardize Recognize Patterns - Machine Learning - Cluster Analysis - Classification and Regression - Retail Marketing “Personas” - “Market of One” - Optimize Service Example of population micro-segmentation: groups with uniform behaviors, attitudes and lifestyles SOLUTION: SOCIAL DETERMINANTS MICRO-SEGMENTATION MicroSegmentation Source: Wiese K., 2014, Aetna Member Experience and Communications Segmentation Pilot, North Carolina State Health Plan https://shp.nctreasurer.com/Board%20of%20Trustees%20Meeting%20Documents/BOT_4a_Segment_Pilot-8-1-2014.pdf Wall Street Journal, April 29, 2019, online: https://www.wsj.com/articles/health-firms-are-looking-at-personal-data-11556589780
  22. 22. @docyale EXAMPLE: LOW PREDICTABILITY Study: Society of Actuaries (2007) A Comparative Analysis of Claims-Based Tools for Health Risk Assessment. Clinical/Financial Model From: Wei H. “Prediction vs. Intervention (2014). Predictive Modeling Summit presentation. November 13, 2014, Washington, DC.Clinical/Real World 20% 30% 40% 50% ACG CDPS Clinical Risk Group DxCG (Verisk) DxCG (Verisk) Ingenix (Optum) Medicaid Rx Impact Pro Ingenix ERG (Optum) ACG Dx+Rx DxCG UW Model MEDai Clin/Fin - Train Clin/Fin - Test Groupers Novel Predictive Models for Metabolic Syndrome Risk: A “Big Data” Analytic Approach, Am J Manag Care. 2014 Jun 1;20(6):e221-8.. SOLUTION: STRATEGIC DATA ACQUISITION & COMBINATION
  23. 23. @docyale EXAMPLE: READMISSION RISK Nearly 1 in 5 Medicare patients discharged from a hospital (approximately 2.6 million seniors) is readmitted within 30 days, at a cost of more than $26 billion every year - CMS, 2016 Affordable Care Act Section 3026 Community Care Transitions Program “Test models to improve care transitions from hospital to other settings and reduce readmissions for high-risk Medicare beneficiaries.” – CMS, 2011 --- “No statistically significant impacts of the CCTP on readmission rates or Medicare Part A and Part B expenditures” – Mathematica Policy Research, Final Evaluation Report, November 2017 Care Transitions Program § Medication self-management: Patient engaged & medication management system § Patient-centered record: Patient use ”PHR” (communication/continuity) § PCP & Specialist Follow-up: Patient schedules appointments § Knowledge of Red Flags: Patient understands when condition is worsening Care Transitions “Red Flags” § Cardiac § Pulmonary/COPD § Heart Failure § Diabetes § DVT § Peripheral Vascular § Stroke § Other: fever, bleeding, confusion, pain, fatigue
  24. 24. @docyale SOLUTION: DATA SCIENCE PREDICTIVE ANALYTICS One in five hospital patients experienced an adverse event within three weeks of discharge; 60% were medication related and could have been avoided, $60 million loss Risk Assessment Medicare readmission penalty Heart failure, CAD, dysrhythmia Acute myocardial infarction COPD, Pneumonia, Asthma Joint replacements Coronary artery bypass Coronary stents Stroke Diabetes LOS > 7 days (any diagnosis) Home Location Home Discharge Right Care, Right Time & Place Pre-discharge hospital visit RN home visit within 24 hours: § Medication reconciliation/education § Schedule follow-up appointments § Educate self management, “red flags,” doctor visits § “Personal Health Record” Follow-up phone calls to reinforce and ensure appropriate follow up and care Results “Significant” readmissions decline 9% decrease in total cost of ER visits “Population Health Management” or “Personal Health Managed” “Market of One”
  25. 25. @docyale EXAMPLE: DIAGNOSE SKIN LESIONS • Smartphone Selfie Skin Lesion Diagnosis • Pattern recognition • Melanoma survival rate: • Early detection: 99% survival • Late stage detection: 14% survival Deep Medicine, Eric Topol, 2019 “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks, Esteva et al. Nature vol. 542, pgs 115–118, Feb. 2, 2017 “The Final Frontier in Cancer Diagnosis,” Leachman S, Merlino G, Nature vol. 542, pgs 36-38, Feb. 2, 2017 ”How Accurate Are Smart Phone Apps for Detecting Melanoma in Adults?” Chuchu, N et al. Cochrane, Dec 4, 2018 (http://bit.ly/2wCJj4w) Risk Assessment • “In Silico” does not equal Real World • Not clinically validated • Accuracy not demonstrated (6.8% to 98.1%) • Poor Sensitivity, highly variable Specificity • “High likelihood of missing melanomas” (Cochran) ”Lesions Learnt” • Classification: Benign or Malignant • If Malignant: Melanoma or Non-Melanoma • Convolutional Neural Network (CNN) • Algorithm outperformed dermatologists • Medical staff replaced by AI? • Algorithm released to the public
  26. 26. @docyale The Future: Personal Health Management
  27. 27. @docyale
  28. 28. @docyale a11 a12 a13 … a1n a21 a22 a23 … a2n : am1 am2 am3 … amn f (:) b11 b12 b13 … b1n b21 b22 b23 … b2n : bm1 bm2 bm3 … bmn social- economic health system tx-related condition- related patient- related interest/social graph spatial/temporal media c11 c12 c13 … c1n c21 c22 c23 … c2n : cm1 cm2 cm3 … cmn MACHINE LEARNING s1(side effects) + s2(personalize) d11 d12 d13 … d1n d21 d22 d23 … d2n : dm1 dm2 dm3 … dmn
  29. 29. @docyale 2. Individual Data Health & Personal Data 1. Curated Medical Knowledge 3. Patient Relationships 4. Outcomes Predict, Prescribe, Perform TRANSLATE OUTPUT
  30. 30. @docyale Link: https://ce.uci.edu/about/magazine/articles/wi19_healthcareAnalytics.aspx MINING DATA TO REVOLUTIONIZE HEALTHCARE
  31. 31. @docyale RESOURCES
  32. 32. @docyale Ken Yale, DDS, JD Health Solutions Network https://www.linkedin.com/in/kenyale/ dr.kenyale@gmail.com QUESTIONS?

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