Digital Medicine Conference 2017 Tech Immersion Kick-off

3962189473d062fdc76ce9a07cbe89fd?s=47 Shahid N. Shah
December 04, 2017
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Digital Medicine Conference 2017 Tech Immersion Kick-off

3962189473d062fdc76ce9a07cbe89fd?s=128

Shahid N. Shah

December 04, 2017
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  1. Society for Digital Medicine Digital Medicine and ML are here.

    AI is coming. How will health systems and the medical profession change in the next 10 years? Shahid N. Shah (@ShahidNShah) Founding Member, NODE Health Chairman, HealthIMPACT Forum
  2. Technology has digitized our experiences Last and past decades Digitize

    mathematics & engineering Digitize maps, literature, news Digitize purchasing, social networks Predict crowd behavior This and future decades Digitize biology Digitize chemistry Digitize physics Predict human behavior Gigabytes and petabytes, all sharable Petabytes and exabytes, not shareable
  3. There are no business models to do the “right thing”

    in healthcare. Wishful thinking is not a strategy.
  4. Digital Medicine will be slowed by intermediated business models, misunderstood

    regulations such as HIPAA / FDA QSR and protective regulations such as licensure and credentialing.
  5. Intermediation is growing, not shrinking, and continues inefficient marketplaces between

    beneficiaries and funders. (Current administration wants more power in hands of doctors/patients and less with government)
  6. What's not going to change in healthcare? Do no harm,

    safety first, and reliability effect on standard of care Statutory cruft & regulatory burdens increase over time Government as dominant purchaser Outcomes based payments intermediation & pricing pressure Eminence & consensus driven decisions as collaboration increases Increased use of alternate sites of care
  7. NODE’s Inflective (Demand) vs. Reflexive (Supply) Innovation “we need uberization

    of healthcare” “we need to disrupt healthcare” “how would elimination of prior auth increase utilization?” “how can improving provider affinity increase member satisfaction?” “we need to buy more digital health tools” “how can we pay non-clinicians to handle more patient-facing tasks?”
  8. http://www.stripes.com/va-nurse-practitioners-nationwide-no-longer-need-physician-supervision-1.445862

  9. NODE Health partnerships will help define desired outcomes Understand management

    objectives based on desired outcomes Consider using Objectives and Key Results (OKRs) framework for defining outcomes Understand problems to be solved (PTBSs) For each PTBS, understand Jobs to be Done (JTBDs) and journey mapping (JM) Figure out how to model the PTBSs and JTBDs in simple spreadsheets or real simulations Eliminate as many JTBDs as possible through policy or process redesign For JTBDs remaining which cannot be removed (regulatory, statutory, business model, etc.) list remaining PTBSs Find or create solutions, based on remaining PTBSs, JTBDs, and JMs Test your hypotheses against the models and simulations and keep what’s evidence driven These are your “stated needs” (which you’ll use to influence demand)
  10. www.netspective.com © 2017 Netspective. All Rights Reserved. 10 How do

    we move from “institution first” to “patient first” to true “patient centered” innovation diffusion? Hint: it’s not about patient engagement. It’s about outcomes.
  11. www.netspective.com © 2017 Netspective. All Rights Reserved. 11 Seema Verma

    Administrator Last week, CMS announced our new initiative “Patients Over Paperwork” to address regulatory burden. This is an effort to go through all of our regulations to reduce burden. Because when burdensome regulations no longer advance the goal of patients first, we must improve or eliminate them. … We’re revising current quality measures across all programs to ensure that measure sets are streamlined, outcomes-based, and meaningful to doctors and patients. This includes a review of the Hospital Star Rating program. And, we’re announcing today our new comprehensive initiative, "Meaningful Measures.” … “Meaningful Measures” takes a new approach to quality measures to reduce the burden of reporting on all providers…Meaningful Measures will involve only assessing those core issues that are the most vital to providing high-quality care and improving patient outcomes. … It’s better to focus on achieving results, as opposed to having CMS try to micromanage and measure processes. This will help two things: • Help address high impact measurement areas that safeguard public health. • Help promote more focused quality measure development towards outcomes that are meaningful to patients, families and their providers. “ ” SPEECH: Remarks by Administrator Seema Verma at the Health Care Payment Learning and Action Network (LAN) Fall Summit (As prepared for delivery - October 30, 2017) https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2017-Fact-Sheet-items/2017-10-30.html
  12. www.netspective.com © 2017 Netspective. All Rights Reserved. 12

  13. www.netspective.com © 2017 Netspective. All Rights Reserved. 13

  14. www.netspective.com © 2017 Netspective. All Rights Reserved. 14

  15. Health Behaviors Clinical Care Social & Economic Factors Physical Environment

    30% 20% 40% 10% Access to Care Quality of Care Education Employment Income Family/Social Support Community Safety Air & Water Quality Housing & Transit Source: RWJF/UWPHI. Genetics Diet & Exercise Tobacco Use Alcohol & Drug Use Sexual Activity Sleep Inflective innovation outcomes drivers
  16. Vector 2: Evidence-Based Decisions Vector 3: B2C Health Improvement Programs

    Vector 1: Next Generation Primary Care Self-tracking/testing: Wearables/Hardware Personalized Medicine/Genomics Health Information Care Navigation Disease Management Peer Networks Health Coaching Decision-Making Tools Care Access Remote Patient Monitoring Patient Engagement Health Behaviors 30% Wellness Programs Source: RWJF/UWPHI. Genetics Diet & Exercise Tobacco Use Alcohol & Drug Use Sexual Activity Sleep Family support & self- help patient groups Health behaviors inflection points
  17. Vector 5: Analytics and Clinical Decision Support Vector 2: Next

    Generation Primary Care Vector 3: Value-Based Care Vector 4: Operational Efficiency Vector 1: Disease-Specific Care Pathways Care Coordination Patient Engagement Big Data Personalized Medicine Medication Management Clinical Care 20% Access to Care Quality of Care Nanotechnology Source: RWJF/UWPHI. Knowledge Sharing Clinical care inflection points Practice Management, EMRs, Pharmacy Management Transparency Tech-enabled services Retail Clinics, DPC House Calls
  18. Advance Directives Programs/Services Next Gen Benefits Social Services Access/Management Vector

    1: Equilibrating Healthcare Expense Vector 2: Community-Based Health Initiatives Vector 3: Aging & End-of-Life Programs Social & Economic Factors 40% Education Employment Income Family/Social Support Community Safety House Calls Hospice Programs Virtual Medicine Incentive Programs Wellness Programs Source: RWJF/UWPHI. Early ID and prevention programs Social & economic factors inflection points
  19. GPS-enabled sensors Physical Environment 10% Air & Water Quality Housing

    & Transit Vector 1: Targeted Monitoring and Rapid Response Vector 2: Community-Based Health Initiatives Vector 3: Affordable Living and Access Food , Housing, and Transportation Access Next Generation Public Transport Environmental Response Mechanisms Continuous Monitoring Source: RWJF/UWPHI. Built Environment Design Broadband connectivity Physical environment inflection points
  20. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Docs and nurses as clerical staff TODAY
  21. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence PGHD, Med Device Connectivity TODAY, ACCELERATING
  22. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Automating retrospective visibility TODAY
  23. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Pattern matching mastery (unsupervised and supervised)
  24. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Use past knowledge to make rudimentary predictions about the future
  25. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Finding known needles in haystacks and pop health TODAY, MAY SKIP FOR ML
  26. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Semi autonomous intelligence which needs humans ARRIVING SOON
  27. The Digital Transformation Spectrum Manual Data Collection Systems Integration Reporting

    and Analytics Data Mining Predictions Machine Learning Augmented Intelligence Artificial Intelligence Real intelligence indistinguishable from humans and fully autonomous YEARS AWAY
  28. Data ushering in Scientific Method 3.0 . 1.0 Identify phenomenon

    Think about nature Fit to known patterns Guess at answers 3.0 Identify data Generate questions Mine data Answer questions 2.0 Identify problem Ask questions Collect data Answer questions
  29. How will we know if we’ve reached 3.0 ?

  30. RESPONSIBLE AND ACCOUNTABLE

  31. OUTCOMES MATTER

  32. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Cohort specific Personalized Risk Data Sharing
  33. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Auto Literature Review Specialty-specific Content
  34. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Auto Adjudication Fraud Detection Quality Compliance Contract Adherence
  35. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Patient Self Diagnostics Unlicensed Pro Diagnostics Digitally and Heuristically Guided Diagnostics Images (self, guided, consulted) Labs and Chemistry (self, guided, consulted) Multi-omics (self, guided, consulted) Molecular Biology
  36. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Auto Triage for Low-risk Augmented Triage for Higher risk Infection control / Anti-microbial Stewardship Consulted Tele Diagnostics Med Device Continuous Diagnostics
  37. Where ML and AI are applicable (care) Therapies Therapeutic Tools

    Diagnostics Diagnostic Tools Patient Administration Payer Admin Clinical Professional Education Public Health Education Patient Education Most Regulation Least Regulation Physical Mental (chat, VR, etc.) Digital (nutritional, etc.) Clinical Research ( “systematic review automation”) Drug Development Clinical Discovery (unattended and digital)
  38. Where Digital Medicine is applicable (data) Proteomics Genomics Biochemical Imaging

    Behavioral Phenotypics Admin Economics Connectivity Integration Transformation Comprehension Enrichment Insights Cognition No outcomes driven medicine possible without these
  39. www.netspective.com © 2017 Netspective. All Rights Reserved. 39 WHAT TECH

    IS DISRUPTIVE DEPLOYABLE? BLOCKCHAIN MACHINE LEARNING & AI CONVERSATIONAL UX FHIR & APIs
  40. None
  41. None
  42. How should machines go through medical training? Which medical school

    will have the first machine learning algorithm training department?
  43. HOW DO YOU PREPARE LEADERS? L v = (i)K c

    + (v)S PJ + C2 + (a)T i 2 + R PFU + E(wo)
  44. Here’s the formula that can keep you relevant Lv →

    Leadership value = (target a large number greater than 1) • Kc → inquisitive knowledge of industry led by curiosity about why things are the way they are + • Spj → visionary strategy informed by problems to be solved and jobs to be done + • C2 → communication & coordination + • (a)Ti 2 → application of actionable transformative technology fully integrated into complex workflows + • Rpfu → understanding performance, financial, and utilization risk (shared, one-sided, two-sided) • Ewo → execution through workforce optimization • SQ → status quo is a constant, the size of which depends upon your organization. It means do no harm, focus on patient safety, reliability, intermediation, & maintain eminence and consensus based decision making Lv = SQ Kc + Spj + C2 + (a)Ti 2 + Rpfu + Ewo
  45. Society for Digital Medicine Digital Medicine and ML are here.

    AI is coming. How will health systems and the medical profession change in the next 10 years? Shahid N. Shah (@ShahidNShah) Founding Member, NODE Health Chairman, HealthIMPACT Forum