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

3962189473d062fdc76ce9a07cbe89fd?s=47 Shahid N. Shah
February 22, 2018
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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?

3962189473d062fdc76ce9a07cbe89fd?s=128

Shahid N. Shah

February 22, 2018
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  1. www.netspective.com © 2017 Netspective. All Rights Reserved. 1 REIMAGINING PHYSICIAN-

    PAYER COLLABORATION FOR THE REAL-TIME DIGITAL AGE By Shahid N. Shah, Publisher, Netspective Media
  2. www.netspective.com © 2017 Netspective. All Rights Reserved. 2 WACKY IDEAS

    WELCOME IoT-style Digital Health Data Aggregation (+BC!) Distributed Gaps in Care & Shared Worklists Real-time Quality Data Exchange Machine Learning & AI
  3. www.netspective.com © 2017 Netspective. All Rights Reserved. 3 WHY ARE

    WE MEASURING QUALITY? OUTCOMES PROCESS COMPLIANCE COSTS
  4. RESPONSIBLE AND ACCOUNTABLE

  5. OUTCOMES MATTER

  6. www.netspective.com © 2017 Netspective. All Rights Reserved. 6 Meaningful Use

    (MU) made us take us our eye off the ball and we ended up with crappy measures
  7. www.netspective.com © 2017 Netspective. All Rights Reserved. 7 What would

    quality measurement look like if MU silliness didn’t make us take our eye off the innovation ball?
  8. www.netspective.com © 2017 Netspective. All Rights Reserved. 8 We’d focus

    on quality improvement (QI) and continuous quality assurance (CQA) not data collection and quality measurement.
  9. www.netspective.com © 2017 Netspective. All Rights Reserved. 9 Let’s reimagine

    QI and CQA for a .real-time patient-first, digital-first quality experience (PDQX)
  10. www.netspective.com © 2017 Netspective. All Rights Reserved. 10 Can we

    reimagine QI and CQA with a .zero-based PDQX approach? start from scratch for specific APMs or QPP initiatives
  11. www.netspective.com © 2017 Netspective. All Rights Reserved. 11 CMS QPP

    is good but are APMs the better driver?
  12. www.netspective.com © 2017 Netspective. All Rights Reserved. 12 Can Meaningful

    Measures allow us to make real progress? http://www.modernhealthcare.com/article/20180120/NEWS/180129995
  13. www.netspective.com © 2017 Netspective. All Rights Reserved. 13 Meaningful Measures

    shows some helpful directions
  14. www.netspective.com © 2017 Netspective. All Rights Reserved. 14 QPP is

    at semi-digital, mostly file exchange model
  15. IOT-STYLE BLINDABLE DIGITAL HEALTH DATA AGGREGATION BHD

  16. www.netspective.com © 2017 Netspective. All Rights Reserved. 16 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
  17. www.netspective.com © 2017 Netspective. All Rights Reserved. 17 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
  18. www.netspective.com © 2017 Netspective. All Rights Reserved. 18 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
  19. DISTRIBUTED GAPS IN CARE & SHARED WORKLISTS

  20. www.netspective.com © 2017 Netspective. All Rights Reserved. 20 • 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
  21. www.netspective.com © 2017 Netspective. All Rights Reserved. 21 AUTO GENERATE

    GAPS IN CARE MANAGED / SHARED WORKLISTS SHARED DECISION MAKING
  22. REAL-TIME QUALITY DATA EXCHANGE

  23. www.netspective.com © 2017 Netspective. All Rights Reserved. 23 Are Quality

    Measures just CEP-processable data streams?
  24. www.netspective.com © 2017 Netspective. All Rights Reserved. 24 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
  25. www.netspective.com © 2017 Netspective. All Rights Reserved. 25 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
  26. www.netspective.com © 2017 Netspective. All Rights Reserved. 26 How serious

    is CMS about PROMS? http://www.modernhealthcare.com/article/20180120/NEWS/180129995
  27. www.netspective.com © 2017 Netspective. All Rights Reserved. 27

  28. www.netspective.com © 2017 Netspective. All Rights Reserved. 28

  29. www.netspective.com © 2017 Netspective. All Rights Reserved. 29 Are PROMS

    more IoT ready than other measures?
  30. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

  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
  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
  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
  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
  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
  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
  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
  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
  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)
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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)
  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
  51. www.netspective.com © 2017 Netspective. All Rights Reserved. 51 WHAT TECH

    IS DISRUPTIVE DEPLOYABLE? BLOCKCHAIN & CRYPTO MACHINE LEARNING & AI CONVERSATIONAL UX BPMN, FHIR & APIs
  52. None
  53. None
  54. www.netspective.com © 2017 Netspective. All Rights Reserved. 72 THANK YOU

    Shahid N. Shah, Publisher, Netspective Media shahid@shah.org @ShahidNShah REIMAGINING PHYSICIAN- PAYER COLLABORATION FOR THE REAL-TIME DIGITAL AGE