1. n VISUAL ANALYTICS FOR HEALTHCARE: BIG DATA, BIG DECISIONS David Gotz Healthcare Analytics Research Group IBM T.J. Watson Research Center

2. n Making healthcare smarter

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4. n Source: Keith Ellison, Wikipedia

5. n Making healthcare smarter

6. n Exploiting Electronic Medical Records

7. n Making healthcare smarter Personalized Evidence-Based Medicine Expertise via Interaction PatientClinician Search and Analysis Tens of Thousands to 10+ Million Patients Years of Data Per Patient Thousands of Features Demographics Diagnoses Labs Procedures Claims Sparse, Irregular Data Unstructured Physician Notes Imaging Uncertainty Missing Data

8. n Making healthcare smarter Personalized Evidence-Based Medicine PatientClinician

9. n Making healthcare smarter Is “At the Point of Care” Too Late? Can we exploit medical data to intervene earlier? - Early detection of at-risk patients - Provide personalized evidence to enable pro-active decisions 20% of people generate 80% of costs Health care spending Early Symptoms Health Status Healthy / Low Risk High Risk At Risk Active Disease Early Clinical Symptoms

10. n Making healthcare smarter Challenges for “At Risk” Patient Identification & Intervention Data Challenges – Large Scale: Up to 10s of millions of patients – High Dimensionality: Thousands of dimensions spanning many years – Semi-Structured: Clinical notes, imaging, medical codes – Distributed: Multiple providers and representations – Sparse and Irregular: Periodic visits, different for each person – Uncertain: Subjective, data entry errors, bias for billing – Incomplete: Many items missing from the medical record Task Challenges – Critical decisions: May literally mean life or death – No clear right answer: Evidence is often ambiguous – Limited time: Manage complexity, multiple granularity – Domain experts are people… Too much (or too little) trust in numbers “But my patients are different…” Users resistant to technology change

11. n Making healthcare smarter ICDA: A Platform for “At Risk” Patient Identification & Intervention David Gotz, Harry Stavroppoulos, Jimeng Sun, and Fei Wang. ICDA: A Platform for Intelligent Care Delivery Analytics. To Appear in American Medical Informatics Association Annual Symposium (AMIA), 2012.

12. n Making healthcare smarter Initial Success Stories Successful research prototypes – Treatment efficacy analysis – Physician outcome modeling – Utilization analysis – Heart failure risk prediction Developed with select group of of IBM partner medical institutions – Real patient data – Domain expertise – Clinical evaluation

13. n Making healthcare smarter Analytics Toolbox of Analytics Components – Patient similarity analytics – Predictive modeling – Clustering – Process mining Key Properties – Scalable – Designed for sparse data – Computationally efficient for population-wide analyses Data model designed for analytics Separate model training and scoring phases – Learning techniques that can incorporate user feedback Patient population ? Query patient Patient similarity assessment in clinical factor/feature space x1x1 x2x2 xNxN … …,, Similarity Analysis x1x1 x2x2 xNxN … x1x1 x2x2 xNxN … x1x1 x2x2 xNxN …,

14. n Making healthcare smarter Visualization Analysis of Patient Cohorts ? Clinically similar to ? Visual outcome analysis Patient population ? Query patient Patient similarity assessment in clinical factor/feature space x1x1 x2x2 xNxN … …,, Similarity Analysis x1x1 x2x2 xNxN … x1x1 x2x2 xNxN … x1x1 x2x2 xNxN …,

15. n Making healthcare smarter Dashboard Techniques “At-a-glance” summary of analytics results

16. n Making healthcare smarter Wide Range of Visualization Use Cases Dashboard / “At a Glance” – e.g., “What is the patient’s risk score?” In-Depth Exploratory Analysis – e.g., “Understanding impacts of variations in care” – Expertise through interaction Key for scaling to “big data”

17. n Making healthcare smarter Visualization Analysis of Patient Cohorts ? Clinically similar to Visual cohort refinement ? Visual outcome analysis Patient population ? Query patient Patient similarity assessment in clinical factor/feature space x1x1 x2x2 xNxN … …,, Similarity Analysis x1x1 x2x2 xNxN … x1x1 x2x2 xNxN … x1x1 x2x2 xNxN …,

18. n Making healthcare smarter Scaling for Big Data Many Features Many Patients Feature Selection Cohort Selection Additional Analytics Feedback via Expert Interaction

19. n Making healthcare smarter Interactive Cohort Visualization Co-occurrence Mode Outlier Mode Default Mode FacetAtlas SolarMap Nan Cao, David Gotz, Jimeng Sun, Yu-Ru Lin, Huamin Qu. SolarMap: Multifaceted Visual Analytics for Topic Exploration. IEEE ICDM, 2011. Nan Cao, Jimeng Sun, Yu-Ru Lin, David Gotz, Shixia Liu, Huamin Qu. FacetAtlas: Multi-facet Visualization of Rich Text Corpora. IEEE InfoVis, 2010.

20. n Making healthcare smarter Interactive Cohort Visualization This high mortality subgroup correlates with these medications. DICON OutFlow Krist Wongsuphasawat and David Gotz. Exploring Flow, Factors, and Outocomes of Temporal Event Sequences with the Outflow Visualization. To Appear in IEEE InfoVis, 2012. Nan Cao, David Gotz, Jimeng Sun, Huamin Qu. DICON: Interactive Visual Analysis of Multidimensional Clusters. IEEE InfoVis, 2011.

21. n Making healthcare smarter Conclusions Real-world medical data is challenging, but has great potential – Better treatments – More efficient care Many difficult challenges – Large and Complex Data – Demanding Domain-Specific Requirements Solutions require collaboration across many disciplines – Data analytics and statistics – Visualization and user interaction – Systems – Medical domain experts

22. n David Gotz Healthcare Analytics Research Group IBM T.J. Watson Research Center dgotz@us.ibm.com