Emergency department records score 88.8% on weighted data quality index

Emergency department information systems hold some of the most time-sensitive clinical data in any hospital, yet the quality of the records entered into them can differ sharply depending on who is doing the documenting. A retrospective cross-sectional study published in BMC Medical Informatics and Decision Making evaluated data quality in an emergency department information system using a multidimensional framework and built a weighted composite index to compare documentation quality across professional sources. The analysis drew on 196 medical records, which generated 24,669 discrete data quality events for evaluation.

The researchers assessed data quality across three dimensions: accuracy, completeness, and timeliness. To combine these into a single comparative measure, they constructed a weighted data quality index using a priori expert-derived weights, assigning accuracy a weight of 0.40, timeliness 0.35, and completeness 0.25. This weighting scheme reflected expert judgment about the relative importance of each dimension in emergency department documentation, where an inaccurate entry can misdirect care and a delayed one can lose its clinical relevance.

Overall data quality in the system reached 88.8 percent. Among the three evaluated dimensions, completeness showed the highest performance, while timeliness demonstrated the lowest. That pattern matters because emergency department documentation is inherently time-sensitive: the study’s authors note that documentation in these settings is distributed across multiple professional roles, and delays in recording can undermine the value of otherwise complete records. The timeliness deficit was particularly pronounced among clinical documentation sources, according to the study’s conclusions.

The most striking finding was the heterogeneity across documentation sources. Statistical testing showed significant variation in data quality depending on which professional group had entered the records, with chi-square analysis yielding a p-value below 0.001. ED admission clerks achieved the highest performance on the weighted composite index, scoring 96.17. By contrast, nursing staff and secretarial staff demonstrated substantially lower scores of 58.31 and 67.67, respectively. The gap between the best and worst performing sources spanned nearly 38 points on the index.

To probe these differences more rigorously, the researchers applied one-way ANOVA with Tukey post-hoc comparisons and generalized linear mixed-effects models. The mixed-effects models confirmed significant differences in satisfactory data quality across documentation sources. Compared with secretarial staff, ED admission clerks and financial affairs staff had higher odds of satisfactory data quality, whereas nursing staff had lower odds. The reported odds ratios ranged from 0.64 to 6.17, with p-values at or below 0.005, indicating that the professional role associated with a record was a meaningful predictor of whether its documentation met the quality threshold.

Because the composite index depended on the specific weights assigned to each dimension, the team conducted a sensitivity analysis using equal weighting across the three dimensions. This test was designed to assess whether the rankings of documentation sources would hold under alternative weighting assumptions. According to the study’s conclusion, the consistency of findings across multiple analytical approaches and weighting scenarios supports the robustness of the comparative results, suggesting that the observed differences between professional groups were not an artifact of one particular weighting choice.

The study’s objective and rationale emphasize that high-quality clinical data are essential for safe and effective decision-making in hospital information systems, particularly in emergency departments where documentation is time-sensitive and distributed across multiple professional roles. Data quality in these settings often varies across workflows and documentation sources, which complicates any single-number assessment. The multidimensional framework and weighted composite index were developed precisely to enable comparative assessment of documentation quality across those varied sources within a single institution.

The authors conclude that data quality in the emergency department information system varies across documentation sources, highlighting differences in documentation quality patterns across the system. They suggest the potential value of targeted quality improvement strategies to enhance electronic data quality, with the timeliness dimension representing the clearest candidate for intervention given its lowest performance, especially among clinical documentation sources. Because the study was retrospective and conducted within a single emergency department information system using 196 records, the findings describe one institution’s documentation landscape rather than a general estimate for all emergency departments.

For hospitals operating similar systems, the study offers a concrete methodological template: define the dimensions that matter, weight them according to expert judgment, test the rankings under alternative weights, and use mixed-effects models to account for clustering within the data. The 24,669 data quality events generated from 196 records illustrate how granular such assessments can become when every field entry is evaluated for accuracy, completeness, and timeliness. The open-access article, published on 01 November 2026, provides the full weighting scheme and statistical comparisons for institutions seeking to replicate the approach in their own emergency department information systems.

Subject of Research: Data quality assessment in an emergency department information system using a weighted composite index across accuracy, completeness and timeliness

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

Article References: Karami, M., & Sadeqi Jabali, M. (2026). Multidimensional data quality assessment in emergency department information systems using a weighted composite index: a cross-sectional study. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-026-03936-w

Image Credits: AI Generated

DOI: 10.1186/s12911-026-03936-w

Keywords: data quality, emergency department information systems, weighted composite index, documentation accuracy, completeness, timeliness, cross-sectional study, medical records, hospital information systems, generalized linear mixed-effects models, nursing documentation, quality improvement