Visual Analytics for Evidence-Based Medicine€¦ · © 2012 IBM Corporation Analytics For Personalized Evidence-Based Medicine Patient Clinician

© 2012 IBM Corporation

Visual Analytics for

Evidence-Based Medicine

Adam Perer Healthcare Analytics Research Group IBM T.J. Watson Research Center


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


Analytics For Personalized Evidence-Based Medicine

Patient Clinician

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Analytics For Personalized Evidence-Based Medicine

Patient Clinician


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?


• 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


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


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





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?


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


Cohort Analysis

DICON 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

SolarMap


§ 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


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?


Outflow: Visual Analytics for Clinical Pathway Analysis


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


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


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.


Outflow Demonstration


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]

Documents

Visual Analytics for Evidence-Based Medicine€¦ · © 2012 IBM Corporation Analytics For Personalized Evidence-Based Medicine Patient Clinician