Effect of a computer network-based feedback program on antibiotic prescription rates of primary care physicians: A cluster randomized crossover-controlled trial
Effect of a computer network-based feedback program on antibiotic prescription rates of primary care physicians: A cluster randomized crossover-controlled trial
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DOI：
10.1016/j.jiph.2020.05.027
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发表时间：
2020-09-01
影响因子：
6.7
通讯作者：
Chongsuvivatwong, Virasakdi
中科院分区：
文献类型：
--
作者：
Chang, Yue;Sangthong, Rassamee;Chongsuvivatwong, Virasakdi
目的：抗生素滥用是中国农村地区存在的主要处方问题之一，也是导致抗生素耐药的主要危险因素。低抗生素处方率可以有效降低抗生素耐药性的风险。我们假设，在无纸化、基于计算机的反馈系统下，初级保健医生的抗生素处方开药率可以降低。方法：在31家医院进行整群随机交叉开放对照试验。这些医院被随机分配到两组，接受干预三个月，然后按随机顺序不干预三个月。反馈干预信息显示医生的抗生素处方使用率和排名，每10天更新一次。结果：第1组(先干预后对照)有82名医师，第2组(先对照后干预)有81名医师。基线比较，两组抗生素处方率分别为30.8%和35.2%，差异无统计学意义(P=0.07)。在交叉点，干预组医生的抗生素处方相对减少率显著高于对照组(33.1%vs20.3%，P值0.001)。再过3个月，干预组抗生素处方的下降率也明显高于对照组(14.2%vs4.6%，P值0.001)。结论：基于计算机网络的反馈干预可以显著降低基层医疗门诊医生的抗菌药物处方率，并持续影响其长达6个月的处方行为。(C)2020年提交人(S)。由爱思唯尔有限公司代表沙特国王本·阿卜杜勒阿齐兹健康科学大学出版。
Objective: Antibiotic overuse is one of the major prescription problems in rural China and a major risk factor for antibiotic resistance. Low antibiotic prescription rates can effectively reduce the risk of antibiotic resistance. We hypothesized that under a paperless, computer-based feedback system the rates of antibiotic prescriptions among primary care physicians can be reduced.Methods: A cluster randomized crossover open controlled trial was conducted in 31 hospitals. These hospitals were randomly allocated to two groups to receive the intervention for three months followed by no intervention for three months in a random sequence. The feedback intervention information, which dis played the physicians' antibiotic prescription rates and ranking, was updated every 10 days. The primary outcome was the 10-day antibiotic prescription rate of the physicians.Results: There were 82 physicians in group 1 (intervention first followed by control) and 81 in group 2 (control first followed by intervention). Baseline comparison showed no significant difference in antibiotic prescription rate between the two groups (30.8% vs 35.2%, P-value = 0.07). At the crossover point, the relative reduction in antibiotic prescription rate was significantly higher among physicians in the intervention group than in the control group (33.1% vs 20.3%, P-value < 0.001). After a further 3 months, the rate of decline in antibiotic prescriptions was also significantly greater in the intervention group compared to the control group (14.2% vs 4.6%, P-value < 0.001). The characteristics of physicians did not significantly determine the change in rate of antibiotic prescriptions.Conclusion: A computer network-based feedback intervention can significantly reduce the antibiotic prescription rates of primary care outpatient physicians and continuously affected their prescription behavior for up to six months. (C) 2020 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences.