Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

AI for Healthcare Leaders: The New AI Frontier for Improved Leadership Decision Making

409 views

Published on

A new frontier is expanding AI from artificial intelligence to augmented intelligence. Traditional AI has focused on improving analytics efficiency and effectiveness. Augmented Intelligence is about improving the decision-making ability of healthcare leaders.

Our goal is to support leaders in driving systemwide outcomes improvement—do we have more opportunity in readmission or depression, how should we staff the ED on weekends, how long does a nurse manager need to improve safety culture, and so on. There is an opportunity to include AI to assist in decision making in new and innovative ways. In this webinar, you will see specific frameworks and tools to use AI to close the information gap for leaders to drive outcomes improvement.

Published in: Healthcare
  • Be the first to comment

  • Be the first to like this

AI for Healthcare Leaders: The New AI Frontier for Improved Leadership Decision Making

  1. 1. AI for Healthcare Leaders The New AI Frontier for Improved Leadership Decision Making Jason Jones, PhD Chief Data Scientist, Health Catalyst
  2. 2. • Identify reasons AI should be complementary/augmentation not artificial/replacement • Identify specific tools that your organization can leverage to facilitate achievement • Describe how your organization can move forward to implement augmented intelligence for leaders Learning Objectives
  3. 3. © 2019 Health Catalyst What is your role? • Senior Leadership (C-Suite, SVP or above) – 10% • Department/Hospital/Regional Leader – 16% • Clinical or Operational Staff – 11% • Program or Large Project Manager – 27% • IT or Analytics Staff – 36% Poll Question #1 3
  4. 4. • AI at the Highest Level of Leadership • Separating Signal from Noise with a Future Orientation • Unexpected Application • Review and What’s Next Agenda 4
  5. 5. © 2019 Health Catalyst On a scale of 1-5, how likely is your Board of Directors (or senior leadership team if you don’t have a Board) to have a machine as a voting member by 2025? • 0% (inconceivable) – 59% • 25% – 28% • 50% – 7% • 75% – 3% • 100% (certain) – 3% Poll Question #2 5
  6. 6. © 2019 Health Catalyst Why Opinion Matters • Surveys can tell us as much about the respondents as the topic (e.g., 1936 FDR) • Prophecies can be self-fulfilling– especially from leaders 6
  7. 7. © 2019 Health Catalyst Why Opinion Matters … Now • Technology has reached a point where the conversation is meaningful • Technology should serve our values • These need to be clearly stated, interrogated, and refined 7 https://www.theverge.com/2015/5/15/8610667/google-self-driving-car-public-testing
  8. 8. • AI at the Highest Level of Leadership • Separating Signal from Noise with a Future Orientation • Unexpected Application • Review and What’s Next Agenda 8
  9. 9. © 2019 Health Catalyst How effectively does your Board or senior executive team leverage data to support future decisions? • Never (don’t look at data) – 0% • Not often (mostly looking “through the rear-view mirror”) – 22% • Frequently (leverage data and discuss interpretations) – 48% • Always (actively leverage analytics to drive decisions) – 18% • Unsure or not applicable – 11% Poll Question #3 9
  10. 10. © 2019 Health Catalyst Two Challenges With Leadership Reporting 10 Separate Signal from Noise • Is this hospital better than that one? • Have we improved over time? • If we set an improvement goal, is it statistically different from current performance? Decisions Impact the Future • Where will we be in a year? • Are we satisfied with that? • If not, what will we change, and when can we expect to see the result? • If we are satisfied, is performance sustainable?
  11. 11. © 2019 Health Catalyst Sample Report • Which geographies currently have different performance? • Which are the same? • Which will be better, worse, or about the same in a year? • Are we becoming more similar or different as a system? 11
  12. 12. © 2019 Health Catalyst What We’re Learning … Studied and work well … • Computers do at least as well as professionals (1993, https://tinyurl.com/y893wct2) • Simple algorithms do as well as complex ones (2000, https://tinyurl.com/yadjh2u4) • Human+Computer+Process (2010, https://hbr.org/2010/02/like-a-lot-of- people) 12
  13. 13. • AI at the Highest Level of Leadership • Separating Signal from Noise with a Future Orientation • Unexpected Application • Review and What’s Next Agenda 13
  14. 14. © 2019 Health Catalyst What Is Quality? From IOM but nicely captured by AHRQ … • Safe, Effective, Person- Centered, Timely, Efficient, and • Equitable: Quality does not vary by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status 14 https://www.ahrq.gov/talkingquality/measures/six-domains.html
  15. 15. © 2019 Health Catalyst How do you assess/address health(care) equity in your system? • We don’t–not a priority at this time – 24% • A health equity group that discusses opportunities – 27% • A scalable, quantitative approach to finding opportunity – 8% • Formal equity evaluation/optimization embedded in major initiatives – 24% • Other – 17% Poll Question #4 15
  16. 16. © 2019 Health Catalyst Concern about AI Worsening Equity 16 https://www.wyden.senate.gov/news/press-releases/wyden-booker-clarke-introduce-bill-requiring-companies-to-target-bias-in-corporate-algorithms- https://science.sciencemag.org/content/366/6464/447
  17. 17. © 2019 Health Catalyst Financial/Income Equity 17 https://en.wikipedia.org/wiki/List_of_countries_by_income_equality
  18. 18. © 2019 Health Catalyst Using AI to Advance Equity From IOM but nicely captured by AHRQ … • Safe, Effective, Person- Centered, Timely, Efficient, and • Equitable: Quality can’t be predicted by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status 18 U.K. = 0.34 U.S. = 0.41 DK = 0.29
  19. 19. © 2019 Health Catalyst From IOM but nicely captured by AHRQ … • Safe, Effective, Person- Centered, Timely, Efficient, and • Equitable: Quality can’t be predicted by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status 19 Using AI to Advance Equity
  20. 20. © 2019 Health Catalyst From IOM but nicely captured by AHRQ … • Safe, Effective, Person- Centered, Timely, Efficient, and • Equitable: Quality can’t be predicted by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status 20 Using AI to Advance Equity
  21. 21. © 2019 Health Catalyst From IOM but nicely captured by AHRQ … • Safe, Effective, Person- Centered, Timely, Efficient, and • Equitable: Quality can’t be predicted by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status 21 AI Can Help Achieve Equity!
  22. 22. • AI at the Highest Level of Leadership • Separating Signal from Noise with a Future Orientation • Unexpected Application • Review and What’s Next Agenda 22
  23. 23. © 2019 Health Catalyst Review and What’s Next • AI gets a lot of attention at the point of care/service • Opportunity is at least as great in leadership–focus, resource allocation, and accountability • Tools in AI/ML/predictive can be applied in a leadership context • Analytic and technical staff and vendors need to close the gap between tools (“BI” and “ML”) • Leaders need to encourage these attempts and remove fear 23
  24. 24. Thank You!

×