Adam Perer
Healthcare Analytics Research Group
IBM T.J. Watson Research Center
Visual Analytics for
Evidence-Based Medicine
© 2012 IBM Corporation
Overview
§  Key Trends
–  Proliferation of electronic medical
records
–  Growth of integrated care networks
–  Push for efficiency & improved outcomes
§  Our Hypothesis
–  An evidence-centric healthcare
ecosystem can drive healthcare
transformation, yielding improved
outcomes and lower costs
§  Two Interconnected Research Thrusts
–  Data Analytics
–  Interactive Visual Analytics
§  Our Goal
–  Combine computational power of data
analytics with human expertise via
interactive visual interfaces to enable a
new generation of personalized
evidence-based medicine
© 2012 IBM Corporation
Analytics For Personalized Evidence-Based Medicine
Clinician
Search
Patient
© 2012 IBM Corporation
Analytics For Personalized Evidence-Based Medicine
Patient
Clinician
© 2012 IBM Corporation
From Vision to Practice
§ We focus on two key technical challenges:
– Data Analytics
•  Core question: What does it mean for two patients to be clinically similar?
•  Additional analytics:
- Treatment comparison
- Utilization analysis
- Physician + patient matching
- Risk prediction
– Visual Analytics
•  Core question: What visualization techniques can make data from analytics
more consumable?
- Interpretation
- Refinement
•  How can we integrate these tools within a clinical workflow?
© 2012 IBM Corporation
Data Analytics: Defining a Patient Similarity Metric
• CPT
• CPT CCS hierarchy
• RVU as value
• ICD9
• CCS hierarchy
• HCC hierarchy
• co-occuring HCC
Diagnosis
Patient
• Lab results
• Break down by age and sex
groups
Lab
x1
x2
Procedure
• NDC
• Ingredient
• Days of Supplies
Feature
Extraction
Pharmacy
• Age
• Gender
Demographics
xN
Patient Similarity Factors
Baseline Metric: factors combined using expert defined weights
Customized Metric: context and end point specific distance
metric
6
© 2012 IBM Corporation
Data Analytics
§ Similarity query is a core analytics capability
Similarity
§ Various use cases build on the basic similarity capability
–  Treatment Comparison
–  Utilization Analysis
–  Physician +
Patient Matching
–  Risk Prediction
© 2012 IBM Corporation
Scenario: Congestive Heart Failure
§ Heart cannot supply necessary blood flow
§ Potentially Fatal
§ Affects 2% of adults in developed countries
– Difficult to manage
– No systematic diagnostic criteria
§ Goal: Understanding symptoms and order of onset
correlates with patient outcome
© 2012 IBM Corporation
© 2012 IBM Corporation
© 2012 IBM Corporation
© 2012 IBM Corporation
From Vision to Practice: Key Challenges
§ A Focus on Two Key Technical Challenges
– Data Analytics
•  Core question: What does it mean for two patients to be clinically similar?
•  Additional analytics:
- Treatment comparison
- Utilization analysis
- Physician + patient matching
- Risk prediction
– Visual Analytics
•  Core question: What visualization techniques can make data from analytics
more consumable?
- Interpretation
- Refinement
•  How can we integrate these tools within a clinical workflow?
© 2012 IBM Corporation
Visual Analytics: Areas of Focus for Novel Visualizations
§ Complex datasets/tasks may require more powerful and
interactive techniques
§ Cluster Analysis for
visualizing mined clusters &
multi-faceted relationships
§ Temporal Analysis for
clinical pathway and
outcome visualization
© 2012 IBM Corporation
Cohort Analysis
DICON
SolarMap
© 2012 IBM Corporation
SolarMap
§  Introduces secondary facet for
explaining why connections exist
§ Key Features
– Cluster-aligned “keyword rings”
display secondary facet
information
– Dynamic context switching
• Primary facet for clusters
• Secondary facet for keyword ring
– Interactive entity comparison
• Via dynamic edge highlighting
§ Applications
– Prototype applied to
documents
– Extended to handle similar
patient cohorts and dynamic
relationships
© 2012 IBM Corporation
DICON
§ Key Features
– Iconic representation of
cohorts
• Easy visual comparison
• Dynamic grouping
- Location
- Primary diagnosis
- Etc.
• Embeddable in other
visualizations
– Direct manipulation for
cohort refinement
• Split
• Merge
§ Applications
– Prototype applied to
electronic medical data
– Extended to community
demographics data
© 2012 IBM Corporation
Temporal Analysis
§ Given a group of similar patients, how do they evolve
over time?
§ Potentially high correlations between outcomes and
specific pathways
§ Key visualization questions:
– How can we depict the various clinical pathways followed by a
cohort of patients over time?
– How can we see which were most common? Led to the best
outcome? Which interventions may be responsible?
© 2012 IBM Corporation
Outflow: Visual Analytics for Clinical Pathway Analysis
© 2012 IBM Corporation
Outflow
§ Each patient has a series of
time-stamped events
– e.g., dates of onset for
symptoms (Framingham
criteria)
Patient
Outcome
Time-stamped Events
§ Each patient has an
outcome
– e.g., mortality
19
© 2012 IBM Corporation
Data Transformation: The Outflow Graph
§  Target patient selected to filter input data (to retrieve similar patients)
A
B
C
§  Filtered data aligned and aggregated into graph-based data structure
Past
[A]
Alignment Point
Future
[A,B]
[A,B,C,D]
[]
[B]
[A,C]
[A,B,C]
[C]
[B,C]
Average outcome = 0.4
Average time = 10 days
Number of patients = 10
[A,B,C,E]
© 2012 IBM Corporation
Outflow’s Visual Encoding
Past
NOW
A
[A,B,D]
Horizontal
position shows
sequence of
states.
Future
[A,B]
Height is
number of
people
[A,B,E]
B
Color is
outcome
measure
Width is
duration of
transition
© 2012 IBM Corporation
Outflow
Demonstration
© 2012 IBM Corporation
Conclusion
§  Key Trends
–  Proliferation of electronic medical records
–  Growth of integrated care networks
–  Push for efficiency & improved outcomes
§  Our Hypothesis
–  An evidence-centric healthcare
ecosystem can drive healthcare
transformation, yielding lower costs and
improved outcomes
§  Two Interconnected Research Thrusts
–  Data Analytics
–  Interactive Visual Analytics
§  Our Goal
–  Combine computational power of data
analytics with human expertise via
interactive visual interfaces to enable a
new generation of personalized
evidence-based medicine
Adam Perer
IBM Research
[email protected]
© 2012 IBM Corporation