Adam Wilcox, PhD, Chief Analytics Officer for University of Washington Medicine and Professor of Biomedical Informatics and Health Education at the University of Washington, and Dale Sanders, Chief Technology Officer at Health Catalyst, discuss the importance of improving methods for applying healthcare analytics in the real world. Adam and Dale have been friends and colleagues for 22 years, and combined, have over 50 years of experience in the field. Adam walks through real-world examples of analysis problems, including emerging lessons from COVID-19, and Dale offers commentary and facilitates questions. Together, they suggest changes in healthcare to raise our analytic methods and literacy. In this webinar, Adam and Dale talk about the following: -Important changes in healthcare that directly affect our approach to analytics. -Methods for improving analytics in healthcare. -Understanding factors that make a difference in what we see in the data. -Early observations about the data challenges of COVID-19.

1. Advancing Methods of
Healthcare Data &
Analytics
Adam Wilcox, PhD
Professor, Biomedical and Health Informatics, University of Washington
Chief Analytics Officer, UW Medicine

3. 3

6. 6

7. • Common data models
• Query tools
• Analytic applications
• Methods of using data
Getting to Farming

8. • Methods
• Information display
• Data context
• Sustainability
• Data quality
• Data access
• Knowledge management
• Examples
• COVID
• LVAD
Outline

10. !

11. 0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
Jan-04
M
ar-04
M
ay-04
Jul-04
Sep-04
N
ov-04
Jan-05
M
ar-05
M
ay-05
Oddsratio
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Pre-intervention
Intervention
Post-intervention
Percentage

12. !

16. Sustainable Assets
Data Collaborations
Methods

18. The PCORI Methodology Report

19. Time to Get Data à Who Can Get It
minute
hour
day
week
month
Everyone
Leaders
Experts
Analysts
Developers
Analytics Curriculum
Query Library
Data Modeling

21. “… Remember, never take no cut-offs,
and hurry along as fast as you can.”
Virginia Reed
Donner Party Survivor

22. Knowledge Management
Then
•How data are modeled
•Concepts, Surface Forms,
Hierarchies
•Decision support needs
driving documentation
approaches
Now
• How data are stored
• Data Definitions,
Phenotypes
• Decision support based
on predictive analytics
and pattern recognition
Integration and Reuse of Data

25. Regulatory Reporting

28. Heart Pumps
Left Ventricular Assist Device
Total Artificial Heart

29. Cause of death: 32% stroke
Adverse events: 21% internal bleeding
15% stroke
15% re-exploration for bleeding

30. > Partial Thromboplastin Time (PTT)
– Measures the time it takes for a blood clot to form
– Therapeutic range: 60 – 100
> Heparin Anti-Xa (Anti-Xa)
– Measures amount of heparin by measuring its inhibition of factor Xa
activity
– Therapeutic range: 0.3 – 0.7
Measuring Bleeding Risk

31. Anti Xa
PTT
>10060 ≤ x ≤ 100<60
>0.7
<0.3
0.3 ≤ x ≤ 0.7
14%
704
192 (21%)
896
12%
47%
2825
318 (10%)
3143
5%
259
49 (16%)
308
1%
30
9 (23%)
39
12%
714
95 (12%)
809
11%
573
126 (18%)
699
0.2%
11
5 (31%)
16
1%
43
5 (10%)
48
10%
520
139 (21%)
659
95 (12%)
714809
Total count
in cell
Non-
bleed
count
Bleed
Count
(and rate)
% of total

32. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120 140 160 180 200
AntiXa
PTT
Discordance Scatter Plot

37. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120 140 160 180 200
AntiXa
PTT
Combined

38. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
sens
1 - spec
ROC
PTT Y Xa Y

39. 0 0.5 1 1.5 2
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
20 40 60 80 100 120 140 160 180 200
Xa valueBleedRate
PTT value
Bleed Rates
PTT Bleed rate
Xa Bleed rate

40. Coin Flip Probability
Probability of a run of ≥ m heads in a sequence of n coin flips:
For m = 8 and n = 200, P = 32%

41. Q&A

42. Thank you!