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Artificial Intelligence in the Hospital Setting

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This presentation was given at the AI Applications Summit (an event for healthcare and pharma professionals) in December 2017. The presentation itself covers to current traction of artificial intelligence in the hospital setting, as well as the unique challenges of applying AI in healthcare (including compliance, resistance from some doctors, the "black box" problem of machine learning, and more). Includes references to Machine Learning in Healthcare Executive Consensus: https://www.techemergence.com/machine-learning-in-healthcare-executive-consensus/

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Artificial Intelligence in the Hospital Setting

  1. 1. Challenges, Trends, and Potential for Application! ! ! ! Daniel Faggella, CEO at TechEmergence! AI in the Hospital Setting! @danfaggella!
  2. 2. Background Brief! I’m Dan Faggella, CEO/Founder at TechEmergence.com We interview hundreds of AI executives and researchers each year We conduct public and private research on the applications of AI in important industries, including healthcare / finance @danfaggella!
  3. 3. Outline of the Talk! •  3 Major Challenges with AI Applications in the Hospital Setting •  2 Application Trends •  Avoiding Hype and Making the Most of AI @danfaggella!
  4. 4. The State of AI in Healthcare! •  Make no mistake about it: It’s mostly pilots, testing (not concrete ROI) •  For every 100 “AI companies”: •  Only 1/3 is actually leveraging AI •  Only 1/4 of those companies are past the stage of “past” their product or service •  Meaning less than 1 in 10 “AI companies” have actual traction in any meaningful way @danfaggella!
  5. 5. The State of AI in Healthcare! @danfaggella! Results from Our Poll:! #1 - Need to be convinced of the ROI #2 - Lack the resources and technology talent to implement it now
  6. 6. Healthcare - Challenges (1)! •  Old School “Artificial Intelligence”: •  “Baking” human expertise (in terms of “if-then” rules) into a software system •  Present “Machine Learning” / “Deep Learning”: •  Involves setting up a series of pattern-detecting “nodes”, and filtering them with labeled examples, to the point where the system can predict the output based on the input @danfaggella! Compliance and the “Black Box”!
  7. 7. Healthcare - Challenges (1)! •  Machine learning systems can’t explain their reasoning •  Doctors are unwilling to use AI systems without understanding their logic, even if these systems could statistically deliver better diagnostics than doctors themselves •  Compliance (HIPPA) further makes machine learning challenging to apply with patients @danfaggella! Compliance and the “Black Box”!
  8. 8. @danfaggella! Possible Solutions: ! •  Steve Gullans of Excel VM stated in an interview with us that AI may be used for applications not related to diagnosis or treatment planning, such as: •  Drug discovery and development •  Managing patient populations / appointments •  Gleaning best-practices from hospital operations Healthcare - Challenges (1)! Compliance and the “Black Box”! Steve Gullans!
  9. 9. Healthcare - Challenges (1)! Possible Solutions:! •  Fundamental developments must be made to make machine learning “explainable”, or else AI’s role within a hospital setting will be significantly hindered @danfaggella! Compliance and the “Black Box”!
  10. 10. Healthcare - Challenges (2)! •  When Amazon implements AI for product recommendations: •  Amazon pays for the technology •  Customers then have a better experience on the Amazon platform, and spend more money •  All of Amazon’s team members are incentivized towards this goal of improving revenue and/or profit @danfaggella! Complex Stakeholder Relationships!
  11. 11. Healthcare - Challenges (2)! •  When a hospital aims to apply AI for improved diagnostics for MRI and CT scans: •  Will the patients even realize that the AI is helping them? •  How long would it take to know if this application is cost- effective for the hospital… or that it improves patient outcomes? •  If the technologies work well, will the doctors fight to prevent it from being used? @danfaggella! Complex Stakeholder Relationships!
  12. 12. Healthcare - Challenges (2)! @danfaggella! Possible Solutions:! •  AI may require massive research-based patient outcome improvements before widespread adoption occurs •  AI may be used for the kinds of jobs that doctors don’t like to take in the first place •  Specialist AI may be used to supplement the skills of general practitioners outside of big city hospitals (poses less of a threat) Complex Stakeholder Relationships!
  13. 13. Healthcare - Challenges (3)! @danfaggella! Chicken-and-Egg Problem of Case Studies!
  14. 14. Healthcare - Challenges (3)! @danfaggella! •  Hospitals don’t seem comfortable buying without strong case studies •  Case studies are challenging to get, and often require a long time to garner a verifiable results •  This factor compounds on top of compliance issues to slow down the industry adoption of AI in general Chicken-and-Egg Problem of Case Studies!
  15. 15. Healthcare - Challenges (2)! @danfaggella! Possible Solutions:! •  Isolated hospitals may adopt AI for “specialist” roles, rather than big hospitals using AI and scaring doctors (example: Diagnosing conditions of the eye, or throat, with images) •  Business intelligence applications (appointment management) may see more traction due to the fact that it’s less likely to be resisted (when compared to diagnostic tech) Chicken-and-Egg Problem of Case Studies!
  16. 16. Healthcare - Trends (1)! •  At TechEmergence we don’t believe that retail robotics will be viable or worthwhile in next 5 years. We believe that the “instrumented retail environment” trend will be much more meaningful in changing the retail experience •  Warehouse robotics (like those used at Amazon) hold greater near-term promise •  Simbe Robotics seems to be the most viable for me, because it is a step-change towards the “instrumented retail environment” @danfaggella! Diagnostics!
  17. 17. Healthcare - Trends (1)! @danfaggella! Diagnostics!
  18. 18. Healthcare - Trends (1)! •  Diagnostic tech was the most common AI applications being developed in our assessment of hundreds of healthcare AI companies •  Machine vision for medical imagery is exceptionally common in terms of new AI applications, and may become a commodity @danfaggella! Diagnostics!
  19. 19. •  Medical operations software was was the second most common AI applications being developed in our assessment of hundreds of healthcare AI companies •  Use cases include: •  Financial collections •  Patient scheduling •  Operational efficiencies @danfaggella! Healthcare - Trends (2)! Medical Operations Software!
  20. 20. •  This is more or less just business intelligence software, applied to the operation of a hospital •  HIPPA and other compliance issues aren’t as stringent in this domain •  Even so, these applications are still “feeling out” the industry and are by no means common, even in the big R&D hospitals @danfaggella! Healthcare - Trends (2)! Medical Operations Software!
  21. 21. Adopt or Wait?! For any given AI application area (marketing, business intelligence, procurement, etc), determine where you want to be on the adoption curve (FEW established firms must be or should be “innovators” or even “early adopters”) @danfaggella!
  22. 22. Concluding Thoughts! Priorities for executives: 1.  Understand the precedents for AI use-cases in hospitals (See reference materials from this talk)! 2.  Have a fundamental understanding of what kinds of problems are solve-able by AI (See reference materials from this talk)! @danfaggella!
  23. 23. That’s All, Folks! dan@techemergence.com Twitter: @danfaggella emerj.com Podcast on iTunes: “AI in Industry” @danfaggella!
  24. 24. AI Resources for Healthcare Executives! •  TechEmergence consensus of over 60 healthcare AI executives: •  https://www.techemergence.com/machine-learning-in-healthcare-executive- consensus/ •  AI use-cases at top hospitals in the USA: •  https://www.techemergence.com/top-5-hospitals-using-machine-learning/ •  AI for diagnostic applications: •  https://www.techemergence.com/machine-learning-medical-diagnostics-4-current- applications/ •  https://www.techemergence.com/deep-learning-applications-in-medical-imaging/
  25. 25. Resources for a Fundamental Understanding of AI! •  https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail ^ Quote from this article: “We believe AI will indeed transform industries. But the companies that will succeed with AI are the ones that focus on creating organizational learning and changing organizational DNA” •  https://hbr.org/2017/04/how-companies-are-already-using-ai ^ Good article, but author is downplaying the job automation concerns of AI. All big, bloated consulting companies do this, be wary of people-heavy companies assuring everyone that AI won’t replace people. •  https://www.techemergence.com/how-to-apply-machine-learning-to-business-problems/ •  Applying AI to Business Problems (General Understanding): •  https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai ^ Extremely useful perspective on the “baby steps” needed to begin working with AI seriously.

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