Paper presentation at the Workshop on Visual Analytics in Healthcare in conjunction with the IEEE VisWeek 2011, Providence, RI, 2011. Abstract: Electronic Medical Record (EMR) databases contain a large amount of temporal events such as diagnosis dates for various symptoms. Analyzing disease progression pathways in terms of these observed events can provide important insights into how diseases evolve over time. Moreover, connecting these pathways to the eventual outcomes of the corresponding patients can help clinicians understand how certain progression paths may lead to better or worse outcomes. In this paper, we describe the Outflow visualization technique, designed to summarize temporal event data that has been extracted from the EMRs of a cohort of patients. We include sample analyses to show examples of the insights that can be learned from this visualization.

1. OUTFLOW
Visualizing Patients Flow
by Symptoms & Outcome
Krist Wongsuphasawat
David H. Gotz
IBM T.J. Watson Research Center
mm

2. Electronic Medical Records
Congestive Heart Failure
(CHF)
m

3. Time
Patient #1
Aug 1998 Oct 1998 Jan 1999
Ankle Edema Cardiomegaly Weight Loss
m

4. Many patient records
Time
Patient #1
Ankle Cardio. Weight
Patient #2
Ankle Cardio. Rales
Patient #3
Ankle Rales Cardio.
Patient #n
Ankle Weight Rales Cardio.
m

5. with outcome
Time
Patient #1 Live (1)
Ankle Cardio. Weight
Patient #2 Live (1)
Ankle Cardio. Rales
Patient #3 Die (0)
Ankle Rales Cardio.
Patient #n Live (1)
Ankle Weight Rales Cardio.
m

6. information overload!
6,000 patients
200,000 symptoms
6,000,000 medications
m

7. consumable
m

8. Overview / Summary
Millions of records
m

9. Steps
1. Aggregation
2. Visual Encoding
3. Interactions
m

10. m
Step 1: Aggregation
Patients Outflow graph

11. Patient records
Patient #1
Patient #2
Outflow Graph
Patient #3
Patient #4
Patient #5
Patient #6
Patient #7
…
Patient #n
m

12. Assumption
• Symptoms are accumulative.
Patient #1
Ankle Cardio. Weight
Patient #1
m

13. Assumption
• Symptoms are accumulative.
Patient #1
Ankle Cardio. Weight
Patient #1
Ankle Ankle Ankle
m

14. Assumption
• Symptoms are accumulative.
Patient #1
Ankle Cardio. Weight
Patient #1
Ankle Ankle Ankle
Cardio. Cardio.
m

15. Assumption
• Symptoms are accumulative.
Patient #1
Ankle Cardio. Weight
Patient #1
Ankle Ankle Ankle
Cardio. Cardio.
Weight
m

16. Assumption
• Symptoms are accumulative.
Patient #1
Ankle Cardio. Weight
Patient #1
Ankle Ankle Ankle
[A] Cardio. Cardio.
[A,C] Weight
[A,C,W]
State
m

17. Select alignment point
Target patient’s current state
Ankle
Cardio.
Weight
[A,C,W]
m

18. Filter patients
Patient #1
[A] [A,C] [A,C,W] [A,C,R,W]
Patient #2
[A] [A,W] [A,R,W] [A,C,R,W]
Patient #3
[A] [A,W] [A,C,W] [A,C,D,W]
m

19. Select alignment point
Target patient’s current state
What are the paths What are the paths
that led to ? after ?
Ankle
Cardio.
Weight
[A,C,W]
m

20. Outflow Graph
Alignment Point
[A] [A,C]
[]
[A,C,W]
[A,C,D,W]
m

21. Outflow Graph
Alignment Point
[A] [A,C]
[] [A,W]
[A,C,W]
[A,C,D,W]
m

22. Outflow Graph
Alignment Point
[A] [A,C]
[A,C,R,W]
[] [A,W]
[A,C,W]
[A,C,D,W]
m

23. Outflow Graph
Alignment Point
[A] [A,C]
[A,C,R,W]
[] [C] [A,W]
[A,C,W]
[A,C,D,W]
[W] [C,W]
Average outcome = 0.4
Average time = 10 days
Number of patients = 10
m

24. m
Step 2: Visual Encoding
Outflow graph Outflow visualization

25. Past Future
NOW
Node’s horizontal position
shows sequence of states.
A!
C!
W! End of path
A!
A!
C!
time link A!
Node’s height is
edge edge C! number of patients.
D!
C!
Color is outcome Time edge’s width is
measure. duration of transition. m

26. m

27. m
Step 3: Interactions
Static vis. Interactive vis.

28. Interactions
• Panning
• Zooming
• Brushing + Freezing
• Tooltip
• Highlight target
m

29. m
Sample Analysis
What can we learn from it?

30. Analysis Demo
• outflow_analysis_demo.mp4
m

31. Steps
1. Aggregation
– Outflow graph
2. Visual Encoding
– Sketch
– Visualization
3. Interactions
– Details on demand
m

32. Future Work
• Evaluation & Design Improvement
• Use outcome from predictive modeling
• Similarity measure to select similar patients
m

33. Conclusions
• Electronic Medical Records
– Rich information
– Large
• Visualization: Outflow
– Visual summary: overview
– Interactive exploration: zoom, filter and details
• Not specific to CHF, or medical domain
Contact me
krist.wongz@gmail.com
@kristwongz
m

34. Soccer Results
Alignment Point
1-0 2-0
2-2
0-0 1-1
2-1
3-1
0-1 0-2
Average outcome = win/lose
Average time = 10 minutes
Number of games = 10
m

35. Acknowledgement
• Charalambos (Harry) Stavropoulos
• Robert Sorrentino
• Jimeng Sun
m

36. Conclusions
• Electronic Medical Records
– Rich information
– Large
• Visualization: Outflow
– Visual summary: overview
– Interactive exploration: zoom, filter and details
• Not specific to CHF, or medical domain
Contact me
krist.wongz@gmail.com
@kristwongz
m

37. m
THANK YOU
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