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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 UniversityAHRQ 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
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
Survey Populations
Household Surveys
Com-munity Out-reach Center
Ambulatory Clinics
8,000 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. 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
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Tim
e o
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ask
(S
eco
nd
s)
Task 1 Task 2 Task 3 Task 4 Task 5 Tast 6 Task 7
Task
Average Time on Task by Task
19
240
123113
27
219
75
Results of Usability Study
Need example explaining goals and purpose
Patient selection can be difficultComfortable with clinical view, but didn’t
know next stepsData 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 governanceTutorialAdjust performance according to use
– Speed– Modeling
LabDiagnosisDemoProceduresVisitsStructured notesMortalityOrdersNote parsing
Requested Data Types
Barriers, Bottlenecks and Burdens
User navigation of data seems to be one challenge
Data modeling is also a challengeWhat are others?What is the significance of each?
– Barriers?– Bottlenecks?– Burdens?
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