Theodoropoulou Andriana, Lisi Matteo, Rolision Jonathan, Sirota Miroslav
Department of Psychology, University of Essex, Colchester, UK.
Essex ESNEFT Psychological Research Unit for Behaviour, Health and Wellbeing, University of Essex, Colchester, UK.
Br J Health Psychol. 2025 Sep;30(3):e70020. doi: 10.1111/bjhp.70020.
Patients' expectations for antibiotics are among the strongest predictors of clinicians' decisions to overprescribe antibiotics. In this registered report, we used a signal detection theory framework to investigate the experimental effects of the communication interventions that family physicians can use to reduce patients' diagnostic uncertainty, and consequently, their antibiotic expectations.
UK participants (N = 769) read hypothetical consultations for respiratory tract infections and were randomly assigned to one of three conditions: standard information (control), recommended information about the nature of the illness and antibiotic efficacy (recommended communication) or recommended information accompanied by point-of-care test results (recommended communication and CRP). Using a multilevel Bayesian probit regression, we estimated both decision bias (criterion) and sensitivity (d-prime).
Aligned with our bias hypotheses, participants displayed a more liberal antibiotic bias in the control condition compared to both the recommended communication (Δc = -1.34, 95% CI [-1.57, -1.11]) and the recommended communication and CRP (Δc = -1.73, 95% CI [-1.99, -1.48]) conditions. They also showed greater liberal bias in the recommended communication condition compared to the recommended communication and CRP condition (Δc = -0.39, 95% CI [-0.65, 0.13]). Aligned with our sensitivity hypotheses, participants displayed significantly higher sensitivity in both the recommended communication (Δd' = 2.34, 95% CI [1.92, 2.79]) and the recommended communication and CRP (Δd' = 2.49, 95% CI [2.08, 2.95]) conditions compared to control.
Simple, evidence-based communication strategies-particularly when combined with diagnostic test results-can reduce antibiotic expectations, offering practical tools for clinicians to support appropriate prescribing.
患者对抗生素的期望是临床医生过度开具抗生素处方决策的最强预测因素之一。在本注册报告中，我们使用信号检测理论框架来研究家庭医生可用于减少患者诊断不确定性从而降低其抗生素期望的沟通干预措施的实验效果。
英国参与者（N = 769）阅读了关于呼吸道感染的假设性会诊内容，并被随机分配到三种情况之一：标准信息（对照组）、关于疾病性质和抗生素疗效的推荐信息（推荐沟通组）或伴有即时检验结果的推荐信息（推荐沟通加CRP组）。使用多级贝叶斯概率回归，我们估计了决策偏差（标准）和敏感性（d'值）。
与我们的偏差假设一致，与推荐沟通组（Δc = -1.34，95%可信区间[-1.57, -1.11]）和推荐沟通加CRP组（Δc = -1.73，95%可信区间[-1.99, -1.48]）相比，参与者在对照组中表现出更宽松的抗生素偏差。与推荐沟通加CRP组相比，他们在推荐沟通组中也表现出更大的宽松偏差（Δc = -0.39，95%可信区间[-0.65, 0.13]）。与我们的敏感性假设一致，与对照组相比，参与者在推荐沟通组（Δd' = 2.34，95%可信区间[1.92, 2.79]）和推荐沟通加CRP组（Δd' = 2.49，95%可信区间[2.08, 2.95]）中均表现出显著更高的敏感性。
简单的、基于证据的沟通策略——特别是与诊断测试结果相结合时——可以降低对抗生素的期望，为临床医生支持合理开药提供实用工具。
