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已发表论文
中国门诊药房药品调配差错特征分析、风险预警模型构建及评价：一项回顾性研究
Authors Xu T , Wang W, Zhang W, Huang C, Li Y, Chen R
Received 27 August 2025
Accepted for publication 11 December 2025
Published 19 December 2025 Volume 2025:18 Pages 3937—3948
DOI https://doi.org/10.2147/RMHP.S557884
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Keon-Hyung Lee
Tao Xu,* Wanqing Wang,* Wei Zhang, Chunyan Huang, Yi Li, Rong Chen
Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Rong Chen, Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China, Email 498338436@qq.com Yi Li, Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China, Email lyi751002@163.com
Background/Objectives: Dispensing errors have the potential to cause significant and preventable patient harm, including adverse drug events, hospitalization, or even death. This study aims to analyze the characteristics of drug product dispensing errors in the outpatient pharmacy, identify risk factors, and develop a risk warning model for error prediction.
Methods: A retrospective study analyzed 930 prescriptions with product dispensing errors and 1860 control prescriptions without errors in an outpatient pharmacy of a tertiary hospital from April 2021 to March 2023. Univariate and multivariable logistic regression were used to identify risk factors. A risk warning model with a cutoff value was constructed and its reliability evaluated using Receiver Operating Characteristics(ROC) curve analysis. The cutoff value was then used to assess the model’s test effectiveness with validation dataset.
Results: Logistic regression analysis identified six independent risk factors for product dispensing errors in outpatient pharmacies: work experience, professional title, education level, similar drug names, similar drug appearances, and multiple specifications. A risk warning model (p=ex/(1+ex), x=3.721– 2.133×X1-0.424×X2-0.382×X3+0.736×X4+0.890×X5+0.701×X6) was established. ROC curve analysis showed an AUC of 0.921 (95% CI: 0.908, 0.933), cutoff value of 0.508, sensitivity of 86.0%, specificity of 91.7%, and Youden index of 0.777 for the training dataset. For the validation dataset, results revealed an AUC of 0.928 (95% CI: 0.901, 0.956), sensitivity of 85.90%, specificity of 83.10%, and Youden index of 0.69.
Conclusion: The risk warning model demonstrated high accuracy in predicting product dispensing errors in outpatient pharmacies. Validated externally, it provides a practical reference for preventing such errors.
Keywords: drug dispensing, product errors, risk factors, logistic regression, ROC curve
Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Rong Chen, Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China, Email 498338436@qq.com Yi Li, Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China, Email lyi751002@163.com
Background/Objectives: Dispensing errors have the potential to cause significant and preventable patient harm, including adverse drug events, hospitalization, or even death. This study aims to analyze the characteristics of drug product dispensing errors in the outpatient pharmacy, identify risk factors, and develop a risk warning model for error prediction.
Methods: A retrospective study analyzed 930 prescriptions with product dispensing errors and 1860 control prescriptions without errors in an outpatient pharmacy of a tertiary hospital from April 2021 to March 2023. Univariate and multivariable logistic regression were used to identify risk factors. A risk warning model with a cutoff value was constructed and its reliability evaluated using Receiver Operating Characteristics(ROC) curve analysis. The cutoff value was then used to assess the model’s test effectiveness with validation dataset.
Results: Logistic regression analysis identified six independent risk factors for product dispensing errors in outpatient pharmacies: work experience, professional title, education level, similar drug names, similar drug appearances, and multiple specifications. A risk warning model (p=ex/(1+ex), x=3.721– 2.133×X1-0.424×X2-0.382×X3+0.736×X4+0.890×X5+0.701×X6) was established. ROC curve analysis showed an AUC of 0.921 (95% CI: 0.908, 0.933), cutoff value of 0.508, sensitivity of 86.0%, specificity of 91.7%, and Youden index of 0.777 for the training dataset. For the validation dataset, results revealed an AUC of 0.928 (95% CI: 0.901, 0.956), sensitivity of 85.90%, specificity of 83.10%, and Youden index of 0.69.
Conclusion: The risk warning model demonstrated high accuracy in predicting product dispensing errors in outpatient pharmacies. Validated externally, it provides a practical reference for preventing such errors.
Keywords: drug dispensing, product errors, risk factors, logistic regression, ROC curve