Patients’ and Clinicians’ Perceptions of Antibiotic Prescribing for Upper Respiratory Infections in the Acute Care Setting

Abstract

Reducing inappropriate prescribing is key to mitigating antibiotic resistance, particularly in acute care settings. Clinicians’ prescribing decisions are influenced by their judgments and actual or perceived patient expectations. Fuzzy trace theory predicts that patients and clinicians base such decisions on categorical gist representations that reflect the bottom-line understanding of information about antibiotics. However, due to clinicians’ specialized training, the categorical gists driving clinicians’ and patients’ decisions might differ, which could result in mismatched expectations and inefficiencies in targeting interventions. We surveyed clinicians and patients from 2 large urban academic hospital emergency departments (EDs) and a sample of nonpatient subjects regarding their gist representations of antibiotic decisions, as well as relevant knowledge and expectations. Results were analyzed using exploratory factor analysis (EFA) and multifactor regression. In total, 149 clinicians (47% female; 74% white), 519 online subjects (45% female; 78% white), and 225 ED patients (61% female; 56% black) completed the survey. While clinicians demonstrated greater knowledge of antibiotics and concern about side effects than patients, the predominant categorical gist for both patients and clinicians was “why not take a risk,” which compares the status quo of remaining sick to the possibility of benefit from antibiotics. This gist also predicted expectations and prior prescribing in the nonpatient sample. Other representations reflected the gist that “germs are germs” conflating bacteria and viruses, as well as perceptions of side effects and efficacy. Although individually rational, reliance on the “why not take a risk” representation can lead to socially suboptimal results, including antibiotic resistance and individual patient harm due to adverse events. Changing this representation could alter clinicians’ and patients’ expectations, suggesting opportunities to reduce overprescribing.