Abstract
BACKGROUND: Closed-loop electronic medication management systems are effective measures for preventing medication errors (MEs). However, there is limited evidence supporting this, and few studies have evaluated the long-term effects of these systems on safe medication. OBJECTIVE: To evaluate the long-term effects of implementing a closed-loop medication order executive system on the safe clinical use of medications. DESIGN: A quasi-experimental design. METHOD: Data from 2017 to 2023 were extracted and retrospectively analyzed. The primary outcome indicator was the ME rate. Secondary outcome indicators were the accuracy of order verification and patient identification and the implementation rate of fresh medicine dispensing. The autoregressive integrated moving average (ARIMA) model in time-series analysis was used to evaluate the level and trend changes in ME rates using SPSS 25.0 before and after system implementation. Root cause analysis and descriptive statistics were used to assess changes in types, stages, and causes of ME rates. The independent samples t-test was used to analyze secondary outcomes. RESULTS: Overall, 295 MEs were reported with a mean of 0.26 ± 0.26 ME rates per month during 2017-2023. The ARIMA model showed a decrease in the average level of ME rates after system implementation, with no statistically significant decrease in the long term, and a significant drop in the ME rate in the short and medium term (p < 0.01). Nurses' administration accounted for more than 60% of errors post-implementation, and lack of communication was a prominent factor. The accuracy of order verification and patient identification and the implementation rate of fresh medicine dispensing all increased after implementation. CONCLUSION: Adopting a closed-loop executive system is beneficial for ensuring patient medication safety, but a single integrated system does not completely eliminate MEs. Optimizing system functionalities and applying structured handoff tools are recommended to meet clinical needs and enhance system usability.