Validation study demonstrates high consistency and robustness of a multidimensional hospital reporting model, highlighting improved occupational health surveillance.
Existing online reporting systems for infectious diseases among textile workers focus too much on data completeness and timeliness, neglecting the interactive effects of multidimensional indicators and failing to comprehensively reflect worker risks. To address this, and the difficulty in reflecting overall data quality from secondary hospitals, this paper proposes a comprehensive evaluation model using 15 quantitative indicators across five dimensions: completeness, timeliness, accuracy, consistency, and relevance. The model introduces an entropy-weighted CRITIC combined method for objective dynamic weighting and uses an improved TOPSIS model (combining Euclidean distance and gray correlation) for comprehensive ranking. This enhances the discrimination and robustness of data quality evaluation. Experiments confirm the model's effectiveness, showing high consistency (Kendall τ is 0.74-0.83) and low variation (CV is 0.043-0.060). The verified model provides accurate data quality support for infectious disease monitoring and can be applied directly to textile industry occupational health monitoring to offer precise, timely warnings regarding worker exposure and cumulative health risk.
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Zhang et al. (2026) studied this question.
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