When operating in a group, social psychologists suggest the feelings of responsibility for achieving outcomes can be diffused across group members. In other words, people can “hide in the crowd” (Latane, Williams & Harkins, 1979). When receiving group prescribing information, a GP may believe other GPs in the practice are responsible for the highprescribing levels because each individual’s contribution remains unclear. The GP may also be uncertain about how much to reduce their prescribing – if at all. As a result, the practice’s overall prescribing rate may not reduce as much as expected. To reduce the effect of hiding in a group it is recommended individual contributions are identified and measured against a standard (Karau & Williams, 1993). Targeting feedback at individual GPs rather than groups of GPs provides a potential way to increase individual accountability and the effectiveness of a peer comparison intervention. Overview of our approach We designed and tested the impact of four different letters (and associated materials) on the subsequent antibiotic prescribing rates of Australian GPs. There were five distinctive features of our letter designs. Peer comparison with individual and personalised feedback We personalised the GPs’ feedback by providing individual prescribing relative to other GPs in their region (e.g. “You prescribe more antibiotics than 85 per cent of prescribers in the ACT region”). We also gave their actual percentile (e.g. 74th, 91th or 89th percentile). Another recent study seeking to reduce overprescribing of antibiotics used individual peer comparisons, although it differed from our study in other respects (Meeker et al. 2016; see conclusion for further discussion). Targeting the top 30 per cent We targeted GPs across Australia whose antibiotic prescribing rates were in the top 30 per cent of their geographic region. We wanted to test whether the power of peer comparison information would vary depending on a person’s position relative to the average. For example, whether peer comparison would work less effectively for those at the 70th percentile compared to the 90th percentile. More than one letter We compared four different letters and associated materials to see if one approach had a greater impact on prescribing Education-only letter – contained standard information about AMR and two National Prescribing Service (NPS) posters. trial
Estimates are adjusted means. Bars represent the number of scripts per 1,000 consults and bar labels represent the percentage decreases compared to the no-letter group. See Appendix B for a table of estimates, p-values and confidence intervals. Effect of the letters by month Looking at prescription rates for each month (Figure 4 and Table 2), the impact of our three behaviourally-informed letters increased until September (three months after the letters were sent). At its peak, the peer comparison with graph letter reduced filled scripts by 14.6 per cent (or 17.3 scripts per 1,000 consults). After this, the difference between the no-letter group and the three peer comparison groups began to narrow. At the end of the six-month period, our three behaviourally-informed letters continued to have an impact, with statistically significant reductions ranging from 7.3 per cent to 8.8 per cent (or 6.5 to 7.8 scripts per 1,000 consults). In contrast, the impact of the education-only letter was small and was not always statistically significant at the conventional threshold. Figure 4: Monthly results of each letter (prescription rates) Table 2: Monthly percent change compared to no-letter control group Education-only Education with peer comparison Peer comparison with delayed prescribing Peer comparison with graph July (month 1) -1.2% -10.7% -9.8% -10.6% August 2) -3.4% -12.6% -13.1% -14.6% September 3) -3.4% -12.2% -12.4% -13.6% October 4) -2.4% -9.0% -10.9% -11.5% November (month 5) -3.3% -8.3% -7.4% -8.5% December (month 6) -1.8% -7.3% -7.4% -8.8% See Appendix B, Table 5 for a full table of estimates, confidence intervals and p-values Impact on the total number of scripts filled All up, we estimate our four letters reduced the number of scripts filled by 126,352 over the six-month period after letters were sent compared to the prescribing pattern of GPs who