School of Medicine and Health Management, Guizhou Medical University, Guizhou 550025, China; Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
J Infect Public Health. 2020 Sep;13(9):1297-1303. doi: 10.1016/j.jiph.2020.05.027. Epub 2020 Jun 15.
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.
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 displayed 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.
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.
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.
ChiCTR1900021823.
抗生素滥用是中国农村地区主要的处方问题之一,也是抗生素耐药的主要危险因素。降低抗生素处方率可以有效降低抗生素耐药的风险。我们假设在无纸化、基于计算机的反馈系统下,可以降低基层医疗机构医生的抗生素处方率。
一项在 31 家医院开展的集群随机交叉开放对照试验。这些医院被随机分配到两组,每组接受三个月的干预,然后以随机顺序进行三个月的无干预。每隔 10 天更新一次反馈干预信息,显示医生的抗生素处方率和排名。主要结局指标是医生的 10 天抗生素处方率。
第 1 组(干预组首先接受控制组)有 82 名医生,第 2 组(控制组首先接受干预组)有 81 名医生。基线比较显示两组抗生素处方率无显著差异(30.8%比 35.2%,P 值=0.07)。在交叉点,干预组医生的抗生素处方率相对降低明显高于对照组(33.1%比 20.3%,P 值<0.001)。进一步 3 个月后,干预组抗生素处方下降率也明显高于对照组(14.2%比 4.6%,P 值<0.001)。医生的特征并没有显著决定抗生素处方率的变化。
基于计算机网络的反馈干预可以显著降低基层医疗机构门诊医生的抗生素处方率,并持续影响他们的处方行为长达 6 个月。
ChiCTR1900021823。