DHIN Summer Summit, Finding the Humans in Health IT

3962189473d062fdc76ce9a07cbe89fd?s=47 Shahid N. Shah
June 14, 2018
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DHIN Summer Summit, Finding the Humans in Health IT

A landscape review of what HHS, ONC, and other gov't agencies are focused on vs. what the health IT industry is working on and how ML, AI fit into the overall picture.

3962189473d062fdc76ce9a07cbe89fd?s=128

Shahid N. Shah

June 14, 2018
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  1. www.netspective.com © 2017 Netspective. All Rights Reserved. 1 Finding the

    Human(s) in Health and IT By Shahid N. Shah @ShahidNShah
  2. www.netspective.com © 2017 Netspective. All Rights Reserved. 2 Why is

    it so hard to find the Human in Health IT? EVERYONE WANTS TO DO THE RIGHT THING, RIGHT? @ShahidNShah
  3. www.netspective.com © 2017 Netspective. All Rights Reserved. 3 There are

    no business models to do the “right thing” in healthcare. BUT HIEs ARE AS CLOSE AS WE GET THESE DAYS @ShahidNShah
  4. www.netspective.com © 2017 Netspective. All Rights Reserved. 4 HHS &

    ONC Focus TEFCA DATA / APIs PX / ACCESS REDUCE BURDEN @ShahidNShah SAFETY USABILITY TRANSPARENCY OUTCOMES COST VALUE CONSUMERISM EXPERIENCE FHIR
  5. OUTCOMES MATTER

  6. www.netspective.com © 2017 Netspective. All Rights Reserved. 6 CMS QPP

    is good but are APMs the better driver?
  7. www.netspective.com © 2017 Netspective. All Rights Reserved. 7 Can Meaningful

    Measures allow us to make real progress? http://www.modernhealthcare.com/article/20180120/NEWS/180129995
  8. www.netspective.com © 2017 Netspective. All Rights Reserved. 8 Meaningful Measures

    shows some helpful directions
  9. www.netspective.com © 2017 Netspective. All Rights Reserved. 9 RESPONSIBLE AND

    ACCOUNTABLE
  10. www.netspective.com © 2017 Netspective. All Rights Reserved. 10 Industry Focus

    HIEs Cybersecurity Population Health Telemedicine @ShahidNShah APIs / FHIR Conversational UX Productivity ML / AI BLOCKCHAIN
  11. www.netspective.com © 2017 Netspective. All Rights Reserved. 11 Who’s working

    on answering these questions? • Should we go into at-risk (downside, too not just upside) contracts? Is the uncertainty tolerable? • Will our current pricing allow us to maintain the margins necessary for us to stay in business? • Can we bear the scrutiny of pricing transparency? THIS IS THE HARD STUFF! @ShahidNShah
  12. www.netspective.com © 2017 Netspective. All Rights Reserved. 12 Does new

    Health IT create “interruptions at scale”? AI / ML / Telemedicine / Messaging creates significant opportunity for increased unnecessary interruptions if not properly designed for multiple stakeholder workflows @ShahidNShah
  13. www.netspective.com © 2017 Netspective. All Rights Reserved. 13 The ultimate

    goals of health IT should be to increase patient education, answer questions in a timely manner, improve patient outcomes, reduce patients’ administrative burdens, and increase staff productivity by allowing staff to focus on the neediest patients. @ShahidNShah
  14. www.netspective.com © 2017 Netspective. All Rights Reserved. 14 The unintended

    consequences of health IT systems have been to increase staff workloads and reduce productivity. @ShahidNShah
  15. www.netspective.com © 2017 Netspective. All Rights Reserved. 15 What would

    quality measurement look like if MU silliness didn’t make us take our eye off the innovation ball?
  16. www.netspective.com © 2017 Netspective. All Rights Reserved. 16 We’d focus

    on quality improvement (QI) and continuous quality assurance (CQA) not data collection and quality measurement.
  17. www.netspective.com © 2017 Netspective. All Rights Reserved. 17 Insurer |

    Payer Insurance Product 1 Insurance Product 2 Provider 2 Provider 1 Health Systems But we can’t… because of “Institution First” IT
  18. www.netspective.com © 2017 Netspective. All Rights Reserved. 18 How do

    we move from “institution first” to “patient first” or “patient centered” health IT? Hint: it’s not about patient engagement. It’s about transparency and outcomes. @ShahidNShah
  19. www.netspective.com © 2017 Netspective. All Rights Reserved. 19 Let’s reimagine

    quality improvement for a .real-time patient-first, digital- first quality experience. How? AI & ML
  20. Machine Learning and AI in healthcare will be slowed by

    intermediated business models, misunderstood regulations such as HIPAA / FDA QSR and protective regulations such as licensure and credentialing.
  21. How DHIN Can Help Constituents Traverse 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
  22. How DHIN Can Help Constituents Traverse 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
  23. How DHIN Can Help Constituents Traverse 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
  24. How DHIN Can Help Constituents Traverse 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)
  25. How DHIN Can Help Constituents Traverse 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
  26. How DHIN Can Help Constituents Traverse 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
  27. How DHIN Can Help Constituents Traverse 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
  28. How DHIN Can Help Constituents Traverse 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
  29. 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
  30. How will we know if we’ve reached 3.0 ?

  31. How will medical roles evolve or be eliminated? Find other

    opportunities within 2-3 years Clerical Focus on value-added tasks such as revenue growth or new business development within 3- 5 years Administrative •Learn data science •Seek digital imaging and digital pathology integration opportunities •Become telehealth native Early career clinical • Learn to teach computers • Become a digital transformation change agent / leader • Drive telehealth across the multiple institutions Mid career clinical • Learn to teach computers your wisdom • Ignore the transformation and retire early Late career clinical
  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 ML and AI are applicable (data) Proteomics Genomics Biochemical

    Imaging Behavioral Phenotypics Admin Economics Connectivity Integration Transformation Comprehension Enrichment Insights Cognition No ML or AI possible without these
  39. None
  40. None
  41. How should machines go through medical training? Which medical school

    will have the first machine learning algorithm training department?
  42. www.netspective.com © 2017 Netspective. All Rights Reserved. 42 THANK YOU

    Shahid N. Shah @ShahidNShah Finding the Human(s) in Health and IT