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.

1. AI for Healthcare Leaders
The New AI Frontier for Improved
Leadership Decision Making
Jason Jones, PhD
Chief Data Scientist, Health Catalyst

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. © 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. • 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. © 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. © 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. © 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. • 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. © 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. © 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. © 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. © 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. • 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. © 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. © 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. © 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. © 2019
Health
Catalyst
Financial/Income Equity
17
https://en.wikipedia.org/wiki/List_of_countries_by_income_equality

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. © 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. © 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. © 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. • 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. © 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. Thank You!