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BackgroundMedication errors may occur during prescribing, transcribing, prescription auditing, preparing, dispensing, administration, and monitoring. Medication administration errors (MAEs) are those that actually reach patients and remain a threat to patient safety. The Joint Commission International (JCI) advocates medication error prevention, but experience in reducing MAEs during the period of before and after JCI accreditation has not been reported.MethodsAn intervention study, aimed at reducing MAEs in hospitalized patients, was performed in the Second Affiliated Hospital of Zhejiang University, Hangzhou, People’s Republic of China, during the journey to JCI accreditation and in the post-JCI accreditation era (first half-year of 2011 to first half-year of 2014). Comprehensive interventions included organizational, information technology, educational, and process optimization-based measures. Data mining was performed on MAEs derived from a compulsory electronic reporting system.ResultsThe number of MAEs continuously decreased from 143 (first half-year of 2012) to 64 (first half-year of 2014), with a decrease in occurrence rate by 60.9% (0.338% versus 0.132%, P<0.05). The number of MAEs related to high-alert medications decreased from 32 (the second half-year of 2011) to 16 (the first half-year of 2014), with a decrease in occurrence rate by 57.9% (0.0787% versus 0.0331%, P<0.05). Omission was the top type of MAE during the first half-year of 2011 to the first half-year of 2014, with a decrease by 50% (40 cases versus 20 cases). Intravenous administration error was the top type of error regarding administration route, but it continuously decreased from 64 (first half-year of 2012) to 27 (first half-year of 2014). More experienced registered nurses made fewer medication errors. The number of MAEs in surgical wards was twice that in medicinal wards. Compared with non-intensive care units, the intensive care units exhibited higher occurrence rates of MAEs (1.81% versus 0.24%, P<0.001).ConclusionA 3-and-a-half-year intervention program on MAEs was confirmed to be effective. MAEs made by nursing staff can be reduced, but cannot be eliminated. The depth, breadth, and efficiency of multidiscipline collaboration among physicians, pharmacists, nurses, information engineers, and hospital administrators are pivotal to safety in medication administration. JCI accreditation may help health systems enhance the awareness and ability to prevent MAEs and achieve successful quality improvements.

BACKGROUND: Swallowing disorder is a common clinical symptom that can lead to a series of complications, including aspiration, aspiration pneumonia, and malnutrition. This study aimed to investigate risk factors of post-extubation dysphagia (PED) in intensive care unit (ICU) patients with endotracheal intubation, and to develop a risk-predictive model for PED, which could serve as an assessment tool for the prevention and control of PED.METHODS: Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort. Patients recruited from the same hospital from March to June 2022 served as the external validation cohort for the predictive model. We used a combination of variable screening and least absolute shrinkage and selection operator (LASSO) regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance infl ation factor method. Multivariate logistic regression analysis was performed to calculate the odds ratio (OR; 95% confidence interval [95% CI]) and P-value for each variable to predict diagnosis. The screened risk factors were introduced into R software to build a nomogram model. The performance of the model, including discrimination ability, calibration, and clinical benefi t, was evaluated by plotting the receiver operating characteristic (ROC), calibration, and decision curves.RESULTS: A total of 305 patients were included in this study. Among them, 235 patients (53 PED vs. 182 non-PED) were enrolled in the derivation cohort, while 70 patients (17 PED vs. 53 non-PED) were enrolled in the validation cohort. The independent predictors included age, pause of sedatives, level of consciousness, activities of daily living (ADL) score, nasogastric tube, sore throat, and voice disorder. These predictors were used to establish the predictive nomogram model. The model demonstrated good discriminative ability, and the area under the ROC curve (AUC) was 0.945 (95% CI 0.904-0.970). Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907 (95% CI 0.831-0.983) and good calibration. The decision-curve analysis of this nomogram showed a net benefi t of the model. CONCLUSION:A predictive model that incorporates age, pause of sedatives, level of consciousness, ADL score, nasogastric tube, sore throat, and voice disorder may have the potential to predict PED in ICU patients.

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