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© 2012 IBM Corporation
Visual Analytics for
Evidence-Based Medicine
Adam Perer Healthcare Analytics Research Group IBM T.J. Watson Research Center
© 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
Patient Clinician
Sear
ch
© 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
• ICD9 • CCS hierarchy • HCC hierarchy • co-occuring HCC
Diagnosis
• CPT • CPT CCS hierarchy • RVU as value
Procedure • NDC • Ingredient • Days of Supplies
Pharmacy
• Lab results • Break down by age and sex groups
Lab
• Age • Gender
Demographics
Feature Extraction
Patient Similarity Factors
x1
xN
x2
Patient
6
Baseline Metric: factors combined using expert defined weights Customized Metric: context and end point specific distance metric
Data Analytics: Defining a Patient Similarity Metric
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Data Analytics
§ Similarity query is a core analytics capability
§ Various use cases build on the basic similarity capability – Treatment Comparison
– Utilization Analysis
– Physician + Patient Matching
– Risk Prediction
Similarity
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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
§ Cluster Analysis for visualizing mined clusters & multi-faceted relationships
§ Temporal Analysis for clinical pathway and outcome visualization
§ Complex datasets/tasks may require more powerful and interactive techniques
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Cohort Analysis
DICON SolarMap
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§ 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
SolarMap
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§ 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
DICON
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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?
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Outflow: Visual Analytics for Clinical Pathway Analysis
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Outflow
§ Each patient has a series of time-stamped events
– e.g., dates of onset for symptoms (Framingham criteria)
§ Each patient has an outcome
– e.g., mortality
19
Patient Outcome Time-stamped Events
© 2012 IBM Corporation
Data Transformation: The Outflow Graph
§ Target patient selected to filter input data (to retrieve similar patients)
§ Filtered data aligned and aggregated into graph-based data structure
[A,B,C]
[A,B]
[A,C]
[B,C]
[A,B,C,D]
[A,B,C,E]
[A]
[B]
[C]
[ ]
Alignment Point
Average outcome = 0.4 Average time = 10 days Number of patients = 10
A B C
Future Past
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Outflow’s Visual Encoding
NOW Future Past
[A,B
]
A
B
[A,B
,D]
[A,B
,E]
Width is duration of transition
Height is number of
people
Color is outcome measure
Horizontal position shows sequence of
states.
© 2012 IBM Corporation
Outflow Demonstration
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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