Maximizing clinical cohort size using free text queries.
BACKGROUND
Cohort identification is important in both population health management and research. In this project we sought to assess the use of text queries for cohort identification. Specifically we sought to determine the incremental value of unstructured data queries when added to structured queries for the purpose of patient cohort identification.


METHODS
Three cohort identification tasks were evaluated: identification of individuals taking gingko biloba and warfarin simultaneously (Gingko/Warfarin), individuals who were overweight, and individuals with uncontrolled diabetes (UCD). We assessed the increase in cohort size when unstructured data queries were added to structured data queries. The positive predictive value of unstructured data queries was assessed by manual chart review of a random sample of 500 patients.


RESULTS
For Gingko/Warfarin, text query increased the cohort size from 9 to 28,924 over the cohort identified by query of pharmacy data only. For the weight-related tasks, text search increased the cohort by 5-29% compared to the cohort identified by query of the vitals table. For the UCD task, text query increased the cohort size by 2-43% compared to the cohort identified by query of laboratory results or ICD codes. The positive predictive values for text searches were 52% for Gingko/Warfarin, 19-94% for the weight cohort and 44% for UCD.


CONCLUSIONS
This project demonstrates the value and limitation of free text queries in patient cohort identification from large data sets. The clinical domain and prevalence of the inclusion and exclusion criteria in the patient population influence the utility and yield of this approach.
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Adi V
AV
Gundlapalli
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
Doug
D
Redd
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Bryan Smith
BS
Gibson
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Marjorie
M
Carter
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Chris
C
Korhonen
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Jonathan
J
Nebeker
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
Matthew H
MH
Samore
IDEAS Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. Electronic address: q.t.zeng@utah.edu.
Qing
Q
Zeng-Treitler
D000465:Algorithms; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005843:Geography; D020441:Ginkgo biloba / Q000737:chemistry*; D006801:Humans; D010936:Plant Extracts / Q000008:administration & dosage*; D011237:Predictive Value of Tests; D012984:Software; D014481:United States; D014493:United States Department of Veterans Affairs; D014859:Warfarin / Q000008:administration & dosage*
D010936:Plant Extracts; D014859:Warfarin
Clinical notes; Cohort identification; Diabetes; Gingko; Overweight; Structured data; Text query; Unstructured data; Warfarin