Effects of a feedback intervention on antibiotic prescription control in primary care institutions based on depth graph neural network technology: a cluster randomized cross-over controlled trial

BackgroundOveruse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of an artificial intelligence (AI)-based, automatic, and confidential antibiotic feedback intervention was evaluated to determine whether it could reduce antibiotic prescribing rates and avoid inappropriate prescribing behaviors by physicians. MethodsA randomized, cross-over, cluster-controlled trial was conducted in 77 primary care institutions of Guizhou Province, China. All institutions were randomly divided into two groups and given either a 3-month intervention followed by a 3-month period without any intervention or vice versa. The intervention consisted of 3 feedback measures: a real-time warning pop-up message of inappropriate antibiotic prescriptions on the prescribing physicians computer screen, a 10-day antibiotic prescription feedback, and distribution of educational brochures. The primary and secondary outcomes are the 10-day antibiotic prescription rate and 10-day inappropriate antibiotic prescription rate. ResultsThere were 37 primary care institutions with 160 physicians in group 1 (intervention followed by control) and 40 primary care institutions with 168 physicians in group 2 (control followed by intervention). There were no significant differences in antibiotic prescription rates (32.1% vs 35.6%) and inappropriate antibiotic prescription rates (69.1% vs 72.0%) between the two groups at baseline (p = 0.085, p = 0.072). After 3 months (cross-over point), antibiotic prescription rates and inappropriate antibiotic prescription rates decreased significantly faster in group 1 (11.9% vs 12.3%, p < 0.001) compared to group 2 (4.5% vs 3.1%, p < 0.001). At the end point, the decreases in antibiotic prescription rates were significantly lower in group 1 compared to group 2 (2.6% vs 11.7%, p < 0.001). During the same period, the inappropriate antibiotic prescription rates decreased in group 2 (15.9%, p < 0.001) while the rates increased in group 1 (7.3%, p < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates. ConclusionThe conclusion is that artificial intelligence based real-time pop-up of prescription inappropriate warning, the 10-day prescription information feedback intervention, and the distribution of educational brochures can effectively reduce the rate of antibiotic prescription and inappropriate rate. Trial registrationISRCTN, ID: ISRCTN13817256. Registered on 11 January 2020