Objective To analyze the influencing factors of nursing medication errors during operation and draw a nomogram to help nurses prevent nursing medication errors during operation in major hospitals in Shanghai city.Methods 534 patients with nursing medication errors during operation were selected as medication error group and 1 068 patients without medication errors were selected as non-medication error group from January 2021 to January 2021 in our hospitals.The indicators of the two groups were compared and analyzed.Logistic regression analysis was used to analyze the influencing factors of nursing medication during operation and establish nomogram.Results The error rate of nursing administration in 5 major hospitals in Shanghai was 6.25％.The logistic regression equation showed that age(OR=4.208,95％CI=5.319-24.613),antibiotics(OR=2.604,95％CI=1.238-6.444),anesthetics(OR=2.404,95％CI=1.024-6.693),while the route of intravenous administration with micropump(OR=0.277,95％CI=0.018-0.584),subcutaneous injection route of Administration(OR=1.950,95％CI=1.087-5.859),operation type(OR=3.504,95％CI=1.439-6.386),nurse's age(OR=0.520,95％CI=0.290～0.934),the title of dosing nurse(OR=0.491,95％CI=0.334-0.721)and allocation of full-time anesthesia nurse(OR=0.527,95％CI=0.067-0.758)were included in risk prediction model of surgery nursing medication errors(P＜0.05).The nomogram score was drawn based on the prediction model to describe the risk of nursing medication errors in surgery.The total score of the prediction nomogram was 140-308,and the risk rate was 0.001-0.999.The total score was 140-308,and the risk rate was 0.001-0.999 on the nomogram.Conclusion Nursing administration errors during operation are affec-ted by age,type of administration,route of administration,type of operation,nurse age and professional title of ad-ministration nurses,and the allocation of full-time anesthesia nurses.Corresponding measures should be taken ac-cording to the influencing factors to reduce the occurrence of nursing administration errors during operation.
HospitalsOperation nursingMedication errorPrediction model
维普
万方数据
