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Reimagining Physician-Payer Collaboration for the Real-time Digital Age

Shahid N. Shah
February 22, 2018
220

Reimagining Physician-Payer Collaboration for the Real-time Digital Age

Now that the Meaningful Measures Program and the Alternative Payment Models (APMs) and outcomes driven are driving the healthcare agenda, how can we move to a more real-time quality data exchange capability?

Shahid N. Shah

February 22, 2018
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  1. www.netspective.com
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    1
    REIMAGINING PHYSICIAN-
    PAYER COLLABORATION FOR
    THE REAL-TIME DIGITAL AGE
    By Shahid N. Shah, Publisher, Netspective Media

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  2. www.netspective.com
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    WACKY IDEAS
    WELCOME
    IoT-style Digital Health Data Aggregation (+BC!)
    Distributed Gaps in Care & Shared Worklists
    Real-time Quality Data Exchange
    Machine Learning & AI

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    3
    WHY ARE WE
    MEASURING
    QUALITY?
    OUTCOMES
    PROCESS
    COMPLIANCE
    COSTS

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  4. RESPONSIBLE
    AND
    ACCOUNTABLE

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  5. OUTCOMES MATTER

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    Meaningful Use (MU)
    made us take us our eye
    off the ball and we ended
    up with crappy measures

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    What would quality
    measurement look like if
    MU silliness didn’t make
    us take our eye off the
    innovation ball?

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    We’d focus on quality
    improvement (QI) and continuous
    quality assurance (CQA) not
    data collection and quality
    measurement.

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    Let’s reimagine QI and
    CQA for a .real-time
    patient-first, digital-first
    quality experience (PDQX)

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    Can we reimagine QI and
    CQA with a .zero-based
    PDQX approach?
    start from scratch for specific
    APMs or QPP initiatives

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    CMS QPP is good but are APMs the better driver?

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    Can Meaningful
    Measures allow
    us to make real
    progress?
    http://www.modernhealthcare.com/article/20180120/NEWS/180129995

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    Meaningful Measures shows some helpful directions

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    QPP is at semi-digital, mostly file exchange model

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  15. IOT-STYLE
    BLINDABLE DIGITAL
    HEALTH DATA
    AGGREGATION
    BHD

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    AGGREGATOR NODE
    (HIE / PAYER NETWORK)
    IT Systems
    EHR
    PN 1
    RCM
    PN 2
    HL7
    HL7 2.x Watcher with
    CEP knitting
    X.12
    X.12 Watcher with
    CEP knitting
    HL7
    HL7 FHIR
    RCM
    SQL
    Watcher
    What if we moved to a hierarchical IoT based framework?
    Building blocks:
    • IoT Agents
    • DDS / MQTT
    • CEP (e.g. Spark,
    Esper)
    • HL7 FHIR / CQL
    • QRDA
    • DNS-style networks
    • In-app agents
    (browser extensions)
    • GraphQL
    • Subscriptions
    PROVIDER 2
    IDENTIFIABLE
    DATA
    PROVIDER 1
    IDENTIFIABLE DATA
    SYMETRICALLY
    DEIDENTIFIED
    HOMOMORPHIC
    ENCRYPTION
    X.12
    PROVENANCE and
    LINEAGE
    PRESERVED
    LONGITUDINALLY

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    Aggregator
    Node (Payer Network 2)
    EHR
    RCM
    HL7
    EHR RCM
    HL7
    PNA 1
    PNA 3
    PNA 2
    PN 3
    PNA 4 PNA 5
    PN 2
    PN 3
    PN 4
    NOTE:
    Each Node and Aggregator Node is a copy of agent
    with customized configuration
    RCM
    RCM
    PROVIDER NETWORK 2 - ACO
    PROVIDER NETWORK 1
    Aggregator
    Node (Payer Network 1)
    IT Systems
    LEGEND:
    PNA: Provider Node Aggregator
    PN: Provider Node
    IT Systems
    EHR
    PN 5
    May be complicated, but it follows normal Internet design

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    Aggregator
    Node (Payer Network 2)
    RM
    PNA 1
    PNA 3
    PNA 2
    PN 3
    PNA 4 PNA 5
    PN 2
    PN 3
    PN 4
    PROVIDER NETWORK 2 - ACO
    PROVIDER NETWORK 1
    Aggregator
    Node (Payer Network 1)
    IT Systems IT Systems
    PN 5
    Can use standard centralized view or IPFS blockchains

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  19. DISTRIBUTED GAPS IN
    CARE & SHARED
    WORKLISTS

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    • Industry and vendor neutral business process, case management, and decision
    model notations
    • Superb tooling support on top of easily exchangeable XML
    • Complex event processing (CEP) frameworks can generate processes, cases,
    and decision trees
    • CQL can still be used for querying language
    Healthcare
    Standards
    Social determinants of health (SDoH –
    environmental, retail, financial, etc.) cannot rely
    on healthcare specific standards

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    AUTO GENERATE GAPS IN CARE
    MANAGED / SHARED WORKLISTS
    SHARED DECISION MAKING

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  22. REAL-TIME
    QUALITY DATA
    EXCHANGE

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    Are Quality Measures just CEP-processable data streams?

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    No measure that isn’t understandable by patients or
    their caregivers would be prioritized for inclusion.
    MUST be UNDERSTANDABLE by PATIENTS and CAREGIVERS
    Create a maximum of 10 measures per condition or
    procedure and then every time we have a great idea
    for another one, eliminate an older one.
    EVERY NEW MEASURE MUST ELIMINATE an OLDER MEASURE
    If a measure isn’t demonstrating outcomes easily
    understood by patients or loved ones, we’d ignore it.
    MUST be OUTCOMES FOCUSED, not PROCESS CENTRIC
    PDQX Measures
    Reimagine drastically reducing what we measure

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    Patients don’t really have a voice today in quality
    measures, how will we setup that feedback loop?
    HOW DO WE KNOW WHAT MATTERS TO PATIENTS and CAREGIVERS?
    VALIDATED “PROMS” (PATIENT REPORTED OUTCOMES MEASURES)
    What kind of telemetry and continuous learning can
    we put into place to know which are useful vs. not?
    HOW DO WE KNOW IF SOMEONE IS USING AN OLD MEASURE
    WHEN WE WANT TO DEPRECATE OR ELIMINATE IT?
    Healthcare outcomes are notoriously difficult to
    determine, do we not measure process at all?
    WHO DETERMINES OUTCOMES FOCUSED vs. PROCESS CENTRIC?
    USE SAME APPROACH AS PROMS DEVELOPERS
    PDQX Challenges
    Patient-centric and outcomes-focused easier said than done

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    How serious
    is CMS about
    PROMS?
    http://www.modernhealthcare.com/article/20180120/NEWS/180129995

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    Are PROMS
    more IoT
    ready than
    other
    measures?

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  30. MACHINE LEARNING AND
    ARTIFICIAL INTELLIGENCE

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  31. 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
    Where should we focus first?
    “Meaningful Measures” is still too broad

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  32. 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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  33. 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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  34. 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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  35. 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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  36. 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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  37. 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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  38. 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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  39. 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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  40. 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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  41. 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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  42. 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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  43. 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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  44. 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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  45. 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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  46. 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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  47. 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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  48. 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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  49. 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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  50. 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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    WHAT TECH IS
    DISRUPTIVE
    DEPLOYABLE?
    BLOCKCHAIN & CRYPTO
    MACHINE LEARNING & AI
    CONVERSATIONAL UX
    BPMN, FHIR & APIs

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    THANK YOU
    Shahid N. Shah, Publisher, Netspective Media
    [email protected] @ShahidNShah
    REIMAGINING PHYSICIAN-
    PAYER COLLABORATION FOR
    THE REAL-TIME DIGITAL AGE

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