MP96-10 INITIAL VALIDATION OF AUTOMATED DATA EXTRACTION METHODS IN UROLOGIC ONCOLOGY PRACTICE

Abstract

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality Improvement & Patient Safety III1 Apr 2017MP96-10 INITIAL VALIDATION OF AUTOMATED DATA EXTRACTION METHODS IN UROLOGIC ONCOLOGY PRACTICE Renu Eapen, Samuel Washington III, Annika Herlemann, David Tat, Mark Bridge, Niloufar Ameli, Janet Cowan, Frank Stauf, Peter Carroll, and Matthew Cooperberg Renu EapenRenu Eapen More articles by this author , Samuel Washington IIISamuel Washington III More articles by this author , Annika HerlemannAnnika Herlemann More articles by this author , David TatDavid Tat More articles by this author , Mark BridgeMark Bridge More articles by this author , Niloufar AmeliNiloufar Ameli More articles by this author , Janet CowanJanet Cowan More articles by this author , Frank StaufFrank Stauf More articles by this author , Peter CarrollPeter Carroll More articles by this author , and Matthew CooperbergMatthew Cooperberg More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.3033AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The Urological Outcomes Data Base (UODB) has existed for 15 years and contains data on over 6,000 patients treated for prostate cancer at University of California, San Francisco (UCSF). Until recently, clinical data in UODB have been manually abstracted from patient records. We are now implementing automated data extraction from the EPIC electronic health record system. EPIC is supported by a research database that automatically extracts patient data. We aim to study a set of chosen variables and compare the types and degrees of miscorrelation between automated and manual data extraction, to see if manual data extraction can be minimized or eliminated. METHODS In early 2016 we developed a set of Smart Data Elements (SDEs) for urologic oncology, including SDEs for men with prostate cancer. These SDEs are populated automatically from the EPIC clinician interface during routine clinical documentation, using either SmartForms or SmartLists embedded within a dozen new standardized templates. SDEs are available immediately in EPIC's Clarity database, and can be populated in future documentation notes. We selected 15 core sample SDEs for validation against manually abstracted data in UODB for patients seen in 2016. Manually abstracted values were compared directly to SDEs values to assess match frequency. RESULTS The 15 SDEs encompassed a wide range of variables from diagnosis to pathologic staging, including clinical risk characteristics at diagnosis, biopsy Gleason score and surgical pathology findings. The median number of patients per variable was 37 (IQR 17-39). Median number of matches per variable was was 14% (IQR 5-37) with median match rate of 70.6% (IQR 35.7-97.4%). Detailed match rates are shown in the table. CONCLUSIONS Next steps include expanding validation rules across a larger set of variables and exploring the limitations of the match strategy. In some cases, data sources such as a computerized system may prove more accurate than manual entry. Working with the AUA Quality (AQUA) registry, we plan to transfer subsets of SDEs to the EPIC Foundation repository, allowing access to any EPIC center. Automated data extraction can improve clinical workflow and streamline data collection within urologic oncology. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1298 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Renu Eapen More articles by this author Samuel Washington III More articles by this author Annika Herlemann More articles by this author David Tat More articles by this author Mark Bridge More articles by this author Niloufar Ameli More articles by this author Janet Cowan More articles by this author Frank Stauf More articles by this author Peter Carroll More articles by this author Matthew Cooperberg More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...