TY - JOUR
T1 - Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications
AU - Fu, Sunyang
AU - Wen, Andrew
AU - Schaeferle, Gavin M.
AU - Wilson, Patrick M.
AU - Demuth, Gabriel
AU - Ruan, Xiaoyang
AU - Liu, Sijia
AU - Storlie, Curtis
AU - Liu, Hongfang
N1 - Publisher Copyright:
©2022 AMIA - All rights reserved.
PY - 2022
Y1 - 2022
N2 - Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.
AB - Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.
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M3 - Article
C2 - 35854735
AN - SCOPUS:85134635342
SN - 1559-4076
VL - 2022
SP - 196
EP - 205
JO - AMIA ... Annual Symposium proceedings. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings. AMIA Symposium
ER -