1. Big Data and Patient- Reported Outcomes: Making Sense of the Noise Mike Van Snellenberg, CTO and co-founder, Wellpepper Kristin Helps, RN, Director of Clinical Operations, Wellpepper

2. 3 Takeaways 1.Patient-generated data is important 2.Patient-generated data is different 3.You need new tools and approaches

3. Where Are We

4. Example from Retail Hired data scientist in 2000 Problem: How to capture valuable mother demographic Mothers are loyal shoppers but heavily targeted—how to find them earlier? Hypothesis: There are purchasing patterns that can determine whether someone is pregnant Discovery: 25 products when purchased in relation to each other can predict pregnancy Result: 10,000 pregnant women identified in existing Target data set and one irate father of 15-year old

5. Patient Data Today Clinical Observations EMR Data Everything Outside Clinic Historical diagnosis and billing codes, labs Blood pressure, heart rate, weight, temperature, chief complaint Patient’s actual experience Data for clinical decision making

6. Patient Data Future Clinical Observations EMR Data Patient Generated Data: Passive and Active Historical diagnosis and billing codes, labs Blood pressure, heart rate, weight, temperature, chief complaint Patient’s actual experience PROs Diary Consumer sensor data Vital signs Passive data Data for clinical decision making

7. HL7 Reference Data Model

8. Patient Reference Data Model

9. Key Point Patient generated data is most valuable when set in the context of care

10. Patient-Generated Data Messy Most not clinically-validated Too much PROs, medical devices / home monitoring, consumer devices (fitbit), patient satisfaction scores, portal engagement metrics, healthcare app usage, mobile usage, search engine traffic

11. Working with Patient-Generated Data First you need to get it! Use tools to deal with the structure and size Retail, science, machine learning all have same problems Then you need to understand it

12. Build a Data Pipeline Acquire & Aggregate EMR (HL7) Labs HIE Patient Engagement Health Data Aggregators Public records Clean, Transform, Merge Data Warehouses? Hadoop SQL Lots of vertical industry players Analyze, Report, Predict Tableau SPSS / R Excel! Lots of vertical industry players

13. An Approach To Patient-Generated Data Science Stay curious Iterate Supports / refutes?  Business change? Test with data you already have Start with a hypothesis Get some data, clean it up

14. Data Analysis Examples Based on Wellpepper data set Over 3,000 patients who have recorded over 125,000 data points Investigations What’s the optimal number of Care Plan Tasks to maximize adherence? What patient population is most adherent? Is there a correlation between time to login and future adherence to care plan?

15. What’s the optimal number of care plan tasks to maximize adherence? Hypothesis: Inverse relationship, fewer would be better

17. Optimal Number of Care Plan Tasks Answer 6-7 Business/Clinical Outcomes Nudge provider when assigning more than 7, less than 5 tasks Break complex plans into groups of 6-7 More Investigation Demographics variance? Variance by condition or procedure?

18. What patient population is most adherent? (aka: “This is fine for 30-somethings, but old people will never use this.”)

20. What Patient Population is Most Adherent Answer Over 50 Business/Clinical Outcomes Ensure design supports population’s adherence goals: recording results & getting credit More Investigation Does this vary by type of treatment? Are there outliers?

21. Is there a correlation between time to first login and future adherence to care plan? Hypothesis: Patients who log in more quickly will also be more adherent in the long run

23. Is There A Correlation Between Initial Activation and Adherence? Answer No, doesn’t seem to be. Business/Clinical Outcomes Don’t give up! Extend reminder regimen. More Investigation What other datapoints do we have that will correlate / predict patient engagement?

24. Considerations Attitudes & Motivations Regulations, Privacy and Ethics

25. Public attitudes about data sharing

26. Motivation for Sharing Personal Data Trust Value *Control

27. Attitudes about healthcare data sharing 20132015

28. What about sharing healthcare data with other organizations? (n=1,849)

29. Attitudes about Privacy

30. Regulations, Privacy & Ethics The Common Rule & HIPPA Ethics Should traditional research regulations apply to Big Data research? Informed consent practices

31. The future of health data sharing Building Trust Informed Consent Create data-enabled incentives

32. 3 Takeaways 1.You need patient-generated data  Engage your patients 2.Patient-generated data is different  Embrace unstructured data 3.You need new tools and approaches  Lots of great tools, policy issues evolving

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