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)
“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”
“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]”
 “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.”
“Why can’t you just give me all the data?”
Washington Heights/Inwood
 5 zip codes: 10031,
10032, 10033, 10034,
10040
 Represents significant
issues in health care
disparities
Making Data PatientCentered
Across care institutions
– Hospital, ambulatory care, home care, longterm care
– Longitudinal
Outside the care setting
– Demographics and social information
– Vital statistics
– Patient assessments
Survey Populations
Existing
Studies
8,000 surveys
Ambulatory
Clinics
Community
Outreach
Center
Household
Surveys
Research Data Warehouse
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
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
Results: Time Spent
1.
2.
3.
4.
5.
6.
7.
Login
Select pt by ICD9/Medcode
Identify Diagnosis medcode
Select by service
Select pt from list
View results
View data type distribution
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
Lessons Learned
User context important for usability
– Still need basic cohort selection tool
(e.g. i2b2)
Patient context important for
understanding data
Next Steps
Finalize governance
Tutorial
Adjust performance according to use
– Speed
– Modeling
Requested Data Types
Lab
Diagnosis
Demo
Procedures
Visits
Structured notes
Mortality
Orders
Note parsing
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?