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

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ask

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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?