OBJECTIVE To provide references for medical institutions’ outpatient pharmacies to increase the scanning rate， scanning efficiency and scanning accuracy of drug traceability codes.METHODS A total of 4 157 unscanned traceability code records from the first two‑week period of June 2025 were selected from the outpatient pharmacy of our hospital. Pareto chart was adopted to analyze the reasons for unscanned codes， and the Objective-Rules-Training-Check-Culture （ORTCC） model was utilized to carry out fine management. The traceability code scanning rate， pharmacists’ work efficiency （number of prescriptions dispensed per pharmacist per hour， number of drug boxes dispensed per pharmacist per hour）， patients’ medication-waiting time and dispensing errors were compared among four periods： before scanning implementation （June 2024）， before management intervention （June 2025）， the first phase after management （September 2025）， and the second phase after management （December 2025）， so as to evaluate the management effects.RESULTS The traceability code scanning rates in the first phase and second phase after management were remarkably higher than those before management （ P ＜0.05）， and the scanning rate in the second phase after management was obviously higher than that in the first phase after management （ P ＜0.05）. Compared with the period before scanning implementation， the number of prescriptions dispensed per pharmacist per hour， the number of drug boxes dispensed per pharmacist per hour， decreased significantly and patients’ medication-waiting time prolonged markedly before management （ P ＜0.05）. In the second phase after management， both the number of prescriptions dispensed per pharmacist per hour and the number of drug boxes dispensed per pharmacist per hour increased significantly， and patients’ medication-waiting time shortened greatly in contrast with the status before management （ P ＜0.05）. Besides， no statistically significant differences were found in the number of drug boxes dispensed per pharmacist per hour and patients’ medication-waiting time between the second phase after management and the period before scanning implementation （ P ＞0.05）.After the implementation of traceability-code scanning management， dispensing errors presented a gradually declining trend in each stage.CONCLUSIONS The fine management system for outpatient drug traceability codes constructed based on Pareto chart and ORTCC model can improve the traceability code scanning rate and pharmacists’ work efficiency of outpatient pharmacies， and reduce drug dispensing errors.

OBJECTIVE To analyze the non-genetic risk factors for severe cutaneous adverse reactions （SCARs） related to antiepileptic drugs （AEDs） in HLA-B*15：02 negative patients， and provide a basis for clinical precision medication. METHODS A retrospective case-control design was used to include patients who underwent HLA-B*15：02 testing at our hospital from January 2022 to December 2024. Patients were divided into SCARs group （15 cases who were HLA-B*15：02 negative and diagnosed with SCARs） and control group （38 cases who were HLA-B*15：02 negative and used AEDs）. Risk factors were evaluated using univariate analysis and a multivariable Firth penalty likelihood logistic regression model （Firth regression）， and Benjamin-Hochberg false discovery rate （FDR） and Firth regression were used for correction， and sensitivity analysis was used to quantify the impact of potential biases in carbamazepine exposure rates in the control group on the results. RESULTS Univariate analysis showed that age≥50 years， use of carbamazepine， and combination use of antibiotics/antiviral drugs were risk factors for developing AEDs- related SCARs （OR＝18.15， 7.54， 13.46， 95%CI of 4.13-79.84， 1.89-30.08， 1.36-133.18， all P＜0.05）， while taking lamotrigine was a protective factor [OR＝0.10， 95%CI of 0.02-0.39， P＜0.05]. After FDR correction， the above factors still maintained statistical significance （P＜0.05）. The results of multivariate analysis showed that age≥50 years [adjusted OR＝16.27， 95%CI of 3.98-66.55， P＜0.001] and taking carbamazepine [adjusted OR＝7.11， 95%CI of 1.82-27.85， P＝0.005] were independent risk factors for the occurrence of SCARs-related AEDs. The results of sensitivity analysis showed that the adjusted risk OR range for taking carbamazepine was between 14.2 and 28.4. CONCLUSIONS Age≥50 years and use of carbamazepine are independent non- genetic risk factors for the development of SCARs-related AEDs in HLA-B*15：02 negative patients. It is recommended that elderly patients should prioritize the use of AEDs other than carbamazepine.