1. Exploring Patient Data in Context to Support Clinical Research Studies: Research Data Explorer Adam Wilcox, PhD, Chunhua Weng, PhD, Sunmoo Yoon, PhD, RN, Suzanne Bakken, RN, DNSc WICER Columbia University AHRQ grant R01 HS019853-01, Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER)

2.  “All infusions and drips from the I/O flowsheet, as well as blood products [and ventilation data]”  “Patients will be included if they have undergone surgical resection for exocrine pancreatic tumors”  “We would like to see a sample month of … to verify and understand how these values are being extracted in the data we are seeing”

3.  “ PACU admission date and time (defined by the date and time stamp of the first blood pressure recorded on the day of surgery in the PACU; else same in the SICU for those with no vital signs in PACU)”  “Reoperation date and time (reoperation defined as any operative procedure during the index admission, excluding the index operation”  “Text following “Has Patient used Tobacco in past year?” in [note]”

4.  “Other information requested includes: age, gender, ethnicity, clinic location/setting of visit, type of insurance, hemoglobin, hematocrit, mean corpuscular volume, red cell distribution width, serum ferritin, serum iron, serum transferrin, reticulocyte count, serum B12, serum folate, IgA anti-tissue transglutaminase antibodies, IgA endomysial antibodies, IgA anti-gliadin peptide antibodies, reports from endoscopy including esophagogastroduodenoscopy and colonoscopy, endoscopic tissue biopsy pathology reports, all past medical diagnoses and ICD-9 codes.”

5. “Why can’t you just give me all the data?”

7. Washington Heights/Inwood  5 zip codes: 10031, 10032, 10033, 10034, 10040  Represents significant issues in health care disparities

9.  Across care institutions –Hospital, ambulatory care, home care, long- term care –Longitudinal  Outside the care setting –Demographics and social information –Vital statistics –Patient assessments Making Data Patient- Centered

10. Survey Populations 8,000 surveys

11. Research Data Warehouse

25. RedX Usability Study Users were instructed to complete their scenarios (discovery) first, then explore freely  Task Coding 1.Login 2.Select patient by diagnosis 3.Select patient by service 4.Choose patient from list 5.View results 6.View data type distribution

26. RedX Usability Study  Users completed scenarios first, then explored freely  Steps –Login –Create list of patients (search) –Select patient from list –View results –View data type distribution

27. Results: Time Spent 1.Login 2.Select pt by ICD9/Medcode 3.Identify Diagnosis medcode 4.Select by service 5.Select pt from list 6.View results 7.View data type distribution

28. Results of Usability Study  Need example explaining goals and purpose  Patient selection can be difficult  Comfortable with clinical view, but didn’t know next steps  Data navigation depended on user experience

29. Lessons Learned  User context important for usability –Still need basic cohort selection tool (e.g. i2b2)  Patient context important for understanding data

30. Next Steps  Finalize governance  Tutorial  Adjust performance according to use –Speed –Modeling

31. Requested Data Types

32. Barriers, Bottlenecks and Burdens  User navigation of data seems to be one challenge  Data modeling is also a challenge  What are others?  What is the significance of each? –Barriers? –Bottlenecks? –Burdens?