Changing Patient and Public Beliefs About Antimicrobials and Antimicrobial Resistance (AMR) Using a Brief Digital Intervention.

A key driver of antimicrobial resistance (AMR) is patient demand for unnecessary antibiotics, which is driven by patients' beliefs about antibiotics and AMR. Few interventions have targeted beliefs to reduce inappropriate demand. To examine whether a brief, online algorithm-based intervention can change beliefs that may lead to inappropriate antibiotic demand (i.e. perceptions of antibiotic necessity and lack of concern about antibiotic harm). Pre- and post-intervention study. Participants were 18 years or older, and residing in the United Kingdom, who self-selected to participate via Amazon mTurk, an online survey plaform, and via research networks. Participants were presented with a hypothetical situation of cold and flu symptoms, then exposed to the intervention. The online intervention comprised: 1) a profiling tool identifying individual beliefs (antibiotic necessity, concerns, and knowledge) driving inappropriate antibiotic demand; 2) messages designed to change beliefs and knowledge (i.e. reduce antibiotic necessity, and increase antibiotic concerns and knowledge), and 3) an algorithm linking specific messages to specific beliefs and knowledge. The profiling tool was repeated immediately after the intervention and compared with baseline scores to assess change in beliefs. A paired samples -test was used to determine intervention effect. A total of 100 respondents completed the study. A significant change in beliefs relating to inappropriate demand was observed after the intervention, with a reduction in beliefs about antibiotic necessity (t = 7.254; < 0.0001), an increase in antibiotic concerns (t = -7.214; < 0.0001), and increases in antibiotic and AMR knowledge (t = -4.651; < 0.0001). This study is the first to demonstrate that patient beliefs about antibiotics and AMR associated with inappropriate demand can be changed by a brief, tailored online intervention. This has implications for the design of future interventions to reduce unnecessary antimicrobial use.