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Table 6 Case studies—summary descriptions of interventions for example studies of the three intervention types found to be most effective

From: How effective are social norms interventions in changing the clinical behaviours of healthcare workers? A systematic review and meta-analysis

Trial design
Target healthcare worker
Aims Outcome measure
SMD(95% CI)
Control arm Intervention description
Credible source + social comparison
Hallsworth et al. (2016) [32]
Doctor (primary care)
To reduce the number of unnecessary prescriptions of antibiotics by GPS in England The rate of antibiotic items dispensed per 1000 population
(0.03 to 0.29)
Delayed intervention (after the end of the trial (no BCTs were coded). A letter was sent to GPs from the Chief Medical Officer. The letter stated that the practice was prescribing antibiotics at a higher rate than 80% of practices in its NHS Local Area Team, and used three concepts from the behavioural sciences. The first was social norm information about how the recipient’s practices prescribing rate compared with other practices in the local area. Second, the letter was addressed from a high-profile figure with the assumption that this would increase the credibility of its content. Finally, the letter presented three specific, feasible actions that the recipient could do to reduce unnecessary prescriptions of antibiotics: giving patients advice on self-care, offering a delayed prescription and talking about the issue with other prescribers in his or her practice. The letter was accompanied by a copy of the patient-focused “Treating your infection” leaflet, which acted to reinforce the message of the letter by supporting delayed or reduced prescribing. (9.1 Credible source, 6.2 Social comparison, 2.2 Feedback on behaviour, 4.1 Instruction on how to perform the behaviour).
Social comparison + prompts/cues
Vellinga et al (2016) [33] Arm A
Cluster RCT
To increase the number of first-line antimicrobial prescriptions for suspected urinary tract infections (UTIs) in adult patients Adherence to guidelines for antimicrobial prescribing in primary care
(0.32 to 0.77)
Phase 1—a coding workshop: routine coding for UTIs using standardised codes were demonstrated. The purpose of this was to facilitate the generation of electronic audit and feedback reports (not available to control until after the trial). Control practices then provided 'usual care’ for the remainder of the intervention (no BCTs were coded). Arm A: phase 1—a coding workshop (same as control).
Phase 2—interactive workshops were designed to promote changes in antimicrobial prescribing for the treatment of UTIs by presenting an overview of prescribing and antimicrobial resistance, discussing the role of the GP in the spread of anti-microbial resistance. A computer prompt was developed for use within the selected GP practice management software system. This prompt summarised the recommendations for first-line antimicrobial treatment and appeared on the computer screen when the GP entered the International Classification of Primary Care code (U71) for 'cystitis, urinary infection, other’. This prompt also reminded the GP to collect patients’ mobile telephone numbers. Electronic audit and feedback reports were available to download by GPs. These reports provided the practice with information on antimicrobial prescribing for UTI in comparison with the aggregated information from the other practices participating in the intervention. (7.1 Prompts/cues, 2.2 Feedback on behaviour, 6.2 Social comparison)
Social comparison + social reward
Persell et al. (2016) [34]
2 × 2 × 2 Factorial
Doctor (GP)
To reduce inappropriate antibiotic prescribing for acute respiratory infections (ARIs) Physician rate of oral antibiotic prescribing for non-antibiotic-appropriate ARIs, acute sinusitis/pharyngitis and all other diagnoses of respiratory infection
(− 0.06 to 0.94)
Intervention 1 (accountable justifications): Clinicians received electronic health record (EHR) alerts summarising the treatment guidelines corresponding to the ARI diagnosis for which the antibiotic was being written, prompted the clinician to enter a free-text justification for prescribing an antibiotic, and informed the clinician that the free-text justification provided would be included in the patient’s medical record where it would be visible to other clinicians. Clinicians were also informed that if no free-text justification was entered, a default statement “No justification for prescribing antibiotics was given” would appear in the record. If the antibiotic order was cancelled, no justification was required, and no default text appeared. Alerts were suppressed for patients with comorbid chronic conditions that exempted these patients from clinical guidelines (4.1 Instruction on how to perform the behaviour, 7.1 Prompts/cues)
Intervention 2 (suggested alternatives): when entering an ARI diagnosis for a patient, clinicians received a computerized alert containing multiple non-antibiotic prescription and non-prescription medication choices as well as educational materials that could be printed and given to the patient. (7.1 Prompts/cues)
Intervention 3 (peer comparison): clinicians received emailed monthly performance feedback reports which included the clinician’s individual antibiotic prescribing rates for non-antibiotic-appropriate ARIs and as a benchmark, the antibiotic prescribing rate for clinicians who were at the 10th percentile within the clinic (i.e. the lowest rates of inappropriate antibiotic prescribing). If clinicians were among the 10% of their peers with the lowest prescribing rates the emailed reports told clinicians "You are a top performer.” If clinicians were not among the 10% best, the emailed report told clinicians “You are not a top performer. You are prescribing too many unnecessary antibiotics”. The proportion of “top performers” could be greater than 10 % of clinicians if more than 10 % of clinicians had an inappropriate antibiotic prescribing rate of 0. (2.2 Feedback on behaviour, 6.2 Social comparison, 10.4 Social reward)