1. Big Data, Bias and Analytics – What Can Your EHR Really Tell You? ADAM WILCOX, PHD

2. DATA big

3. Source: Nature (Feb 13, 2013)

5. Hype Cycle for Emerging Technologies Gartner (August 2014)

6. Outline Background and Experience Big Data Introduction Big Data – Bias Issues Advancing Big Data Next Steps and Conclusion

7. Outline Background and Experience Big Data Introduction Big Data – Bias Issues Advancing Big Data Next Steps and Conclusion

8. Knowledge Representation vs. Knowledge Discovery

10. Costs/Clinic Salary + training + admin $92,077 Benefits/Clinic Productivity (7 MD’s)$99,986 Hospitalizations ↓ *$0 Total (benefits – cost)+$7,909 * Society would save, per clinic, $79,092 in reduced hospitalizations. Dorr DA, Wilcox AB, et al. The effect of technology-supported, multidisease care management on the mortality and hospitalization of seniors. J Am Geriatr Soc. 2008 Dec;56(12):2195-202. Effect of Care Management: Outcomes

11. Increase in CDR View Access

12. INTEGRATION SERVICES REPLICATED Databases VIRTUAL DATA WAREHOUSE DATAMARTS DM A B C Ad-Hoc Queries – Questions Research Define Recurring – Automated Queries Management Reports Measure OLAP – Analytics Operational Reports Analyze Dashboards Point of Care Reporting Improve Applications Decision Support Control DATA WAREHOUSE TOOLS

13. WICER

15. Improve Use of Information for Learning Health System Informed strategy for healthcare transformation Measures to support real-time process and quality improvement Data and analytics driving research and discovery

16. Outline Background and Experience Big Data Introduction Big Data – Bias Issues Advancing Big Data Next Steps and Conclusion

19. Raw Clinical Matched Clinical Matched Survey Survey Matched vs. Matched Clinical vs. Survey Age 47.5552.3351.1250.120.072p <<.0001 Proportion Female 0.620.790.780.710.963p <<.0001 Proportion Hispanic 0.500.560.940.96p <<.0001 Weight kg 75.6977.1676.9975.420.851 Height cm 160.34158.23161.31161.25p <<.0001 BMI 28.1029.7028.9028.200.207 Prevalence of Smoking 0.090.08 0.060.944p <<.0001 Systolic 127.23128.48127.50127.680.2040.164 Diastolic 73.0774.3479.2480.95p <<.0001 Prevalence of Diabetes (Survey = self- report, Clinical = >1 Diabetes ICD- 9 AND >1 abnormal test) 0.040.090.220.16p <<.0001 Data Collection Methods

22. Outline Background and Experience Big Data Introduction Big Data – Bias Issues Advancing Big Data Next Steps and Conclusion

23. Data Quality and Assessment Weiskopf NG, Weng C. Methods and dimensions of data quality assessment: enabling reuse for clinical research. JAMIA 2013

26. “New” Analytic Methods Bootstrapping Learning curves and over-fitting Hypothesis generation process t-testsNon-parametric tests (Chi-square) Bootstrapping + Easy + Robust + Powerful+ Robust+ Powerful + Widely implemented - Less common - Not appropriate for all data types - Less powerful- Requires special packages or programming

27. Big Data Analytic Approaches Sub-population analysis Investigating surprises – Often more revealing about data quality than real effects

28. Outline Background and Experience Big Data Introduction Big Data – Bias Issues Advancing Big Data Next Steps and Conclusion

29. Big Data Know the data you need Use the data you have Get the data you want Adapt data to user needs Make value accessible Next Steps to Make it Useful

31. Minimum Requirements to Provide Value Secure database Data sources Patient-level integration – Master Patient Index* Semantic integration – Vocabulary* Excellent analysts

32. Patient Data Integration

33. Vocabulary and Data Density

34. Natural Language Processing

35. Factors Influencing Health

36. Collecting Patient- Reported Outcomes Transcribing Patient Portals Scanning Tablet entry

37. Patient Reported Information: Tablets vs. Scanned Documents ScanningTablets Institutional Equipment cost == Infection risk == Security Theft +- Data loss -+ Patient mismatch -+ Disaster recovery +-

38. Patient Reported Information: Tablets vs. Scanned Documents ScanningTablets Functionality Office workflow -= Education/traini ng == Data timeliness =+ Branching logic -+ Extensibility -+ Patient experience Preference =+ Security perception =-

39. GoalTaskUseUserTool QI Life- cycle Cost/ Instance Instances Required Answer a specific question Ad hoc query ResearchResearcherSQLDefine++++++ Defined request Observe trends Recurring query Management reports Manager Reporting application Measure++++++ Available owner Identify dependencies Sub- population analysis Operational analysis AnalystAnalytic toolsAnalyze+++ Content expert/ analyst Assist decision making Dashboard display Point of care improvement Clinical team Registries Improve++++++Pilot site Automate processes Application Decision support Clinician/ Role EMR application Control++++++ Institutional sponsor

42. Physical Activity