Digital Medicine Conference 2017 Tech Immersion Kick-off

Transcript

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
   

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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
   

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3. There are no business
   models to do the “right
   thing” in healthcare.
   Wishful thinking is not a strategy.
   

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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.
   

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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)
   

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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
   

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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?”
   

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8. http://www.stripes.com/va-nurse-practitioners-nationwide-no-longer-need-physician-supervision-1.445862
   

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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)
   

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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.
    

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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
    

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12. www.netspective.com
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13. www.netspective.com
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14. www.netspective.com
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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
    

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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
    

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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
    

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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
    

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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
    

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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
    

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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
    

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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
    

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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)
    

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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
    

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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
    

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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
    

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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
    

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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
    

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29. How will we know if we’ve reached 3.0 ?
    

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30. RESPONSIBLE
    AND
    ACCOUNTABLE
    

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31. OUTCOMES MATTER
    

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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
    

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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
    

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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
    

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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
    

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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
    

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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)
    

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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
    

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39. www.netspective.com
    © 2017 Netspective. All Rights Reserved.
    39
    WHAT TECH IS
    DISRUPTIVE
    DEPLOYABLE?
    BLOCKCHAIN
    MACHINE LEARNING & AI
    CONVERSATIONAL UX
    FHIR & APIs
    

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42. How should machines go through medical training?
    Which medical school will have the first machine
    learning algorithm training department?
    

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43. HOW DO YOU PREPARE LEADERS?
    L
    v
    = (i)K
    c
    + (v)S
    PJ
    + C2 + (a)T
    i
    2 + R
    PFU
    + E(wo)
    

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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
    

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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
    

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