Online Program Home
My Program

Abstract Details

Activity Number: 401 - Lead with Statistics: Case Studies and Methods for Learning and Improving Healthcare Through EHRs
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #329486 Presentation
Title: Improving Data Quality for Time-Varying Measurements in EHRs via Dynamic Interaction: a Case Study for Growth Chart
Author(s): Qingxia Chen*
Companies: Vanderbilt University
Keywords: data quality; EHR; electronic health record; growth chart; residual; time-varying

Electronic Health Records (EHR) systems, fueled by recent federal provisions in the HITECH Act, have been increasingly implemented at US hospitals. Huge amounts of longitudinal and detailed patient information, including lab tests, medications, disease status, and treatment outcome, have been accumulated and are available electronically. These large clinical databases are valuable and cost-effective data sources for clinical and translational research. Dense and irregularly recorded vital signs and lab measurements are great components of EHRs with potential error inputs. Manual evaluation is time- and cost-consuming and hence infeasible if not impossible. We proposed a residual-based statistical approach to dynamically identify potential error-prone measurements so that clinical practitioners or patients themselves can either confirm or correct the records. The key feature of the method is to learn and update the model from the interactive response from the practitioners or patients. We applied the proposed method to Growth Chart Study using EHR data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program