Multidimensional data quality assessment in emergency department information systems using a weighted composite index: a cross-sectional study

Abstract

High-quality clinical data are essential for safe and effective decision-making in hospital information systems, particularly in emergency departments (EDs) where documentation is time-sensitive and distributed across multiple professional roles. However, data quality (DQ) in these settings often varies across workflows and documentation sources. This study aimed to evaluate DQ in an emergency department information system (EDIS) using a multidimensional framework and to develop a weighted composite measure for comparative assessment of documentation quality across sources. A retrospective cross-sectional study was conducted using 196 medical records, generating 24,669 DQ events. DQ was assessed across three dimensions: accuracy, completeness, and timeliness. A weighted DQI was constructed using a priori expert-derived weights (accuracy: 0.40, timeliness: 0.35, completeness: 0.25). Differences across documentation sources were analyzed using chi-square tests, one-way ANOVA with Tukey post-hoc comparisons, and generalized linear mixed-effects models (GLMMs). A sensitivity analysis using equal weighting was conducted to assess the robustness of documentation source rankings under alternative weighting assumptions. Overall DQ was 88.8%. Among the evaluated dimensions, completeness showed the highest performance, while timeliness demonstrated the lowest. Significant heterogeneity in DQ was observed across documentation sources ( p < 0.001). ED admission clerks achieved the highest performance (DQI = 96.17), whereas nursing and secretarial staff demonstrated substantially lower scores (58.31 and 67.67, respectively). GLMM results confirmed significant differences in satisfactory DQ across documentation sources. Compared with secretarial staff, ED admission clerks and financial affairs staff had higher odds of satisfactory DQ, whereas nursing staff had lower odds (OR range: 0.64–6.17, p ≤ 0.005). DQ in EDIS varies across documentation sources, highlighting differences in documentation quality patterns across the system. Timeliness represented the lowest-performing DQ dimension, particularly among clinical documentation sources. The consistency of findings across multiple analytical approaches and weighting scenarios supports the robustness of comparative results and suggests the potential value of targeted quality improvement strategies to enhance electronic data quality.

The authors' abstract, as published at the source. BMC Medical Informatics and Decision Making, 2026 · DOI ↗