92 Discussion
Fig. 3. Hand hygiene compliance rates by study implementation month. Study month 1 = first month a site began baseline observations.
Table 2. Generalized Linear Mixed Model for Hand Hygiene Compliance Rates for Baseline and Feedback Phases With a Random Effect for Facility
Hand Hygiene Compliance Rate Variable
Study phase Baseline Feedback
Audit method New audit method
Standard audit method
Study phase × audit method Calendar month
Bed size (every 10-bed increase) Note. OR, odds ratio; CI, confidence interval. OR (95% CI) Reference 0.92 (0.84–1.01) Reference
9.83 (8.82–10.95) 1.55 (1.36–1.77) 1.00 (0.99–1.01) 0.92 (0.89–0.96)
P Value
Reference .07
Reference <.001 <.001 0.42
<.001
The current study contributes to the growing body of research demonstrating that the Hawthorne effect introduces a significant amount of bias when HHC is measured via overt observation, the method almost universally utilized by hospitals.2–6 In our study, there was an ~30% absolute difference in estimated HHC rates between the standard and new audit methods. As the evidence mounts that hospitals and healthcare systems are significantly overestimating HHC, there should be increased demand that they change how they measure HHC to provide more accurate representations of their compliance rates. The standard audit method has value insofar as estimates of HHC using overt observation are negatively associated with rates of HAIs,27,28 revealing relative differences in HHC across facilities. However, because the standard method also seems to inflate estimates of HHC, using overt observations may undermine additional efforts to improve HHC by promoting the belief that one’s unit, hospital, or healthcare system is already near the top of what is possible for HHC and, as a result, put patients at risk. Our study also represents the first attempt to provide feed-
~30% absolute difference between the 2 audit methods for both study phases. Results from the generalized linear mixed model with a ran-
dom effect confirmed the observed differences in HHC rates between the 2 audit methods, with an almost 10-fold increase (odds ratio [OR], 9.83; 95% confidence interval [CI], 8.82–10.95) in the likelihood of reported compliance using the standard audit method compared to the new audit method (Table 2). This means that a healthcare worker would be 10 times more likely to be recorded as engaging in appropriate hand hygiene with the standard audit method compared to the new audit method. We found no observed association of study phase on HHC, but we detected a significant interaction between study phase and audit method. Specifically, the likelihood of reporting appropriate HHC was higher in the feedback phase compared to the baseline phase for the standard audit method, but we found no evidence of change in HHC across the 2 phases for the novel audit method (see Fig. 2). We also noted a significant decrease in the likelihood of reporting compliance as the bed size of the hospital increased (controlling for method).
back based on rapid, covert observations of HHC by pilot testing a feedback tool that we developed. While a more formal analysis of the potential barriers and facilitators to the implementation of the feedback tool is needed (and is currently underway in our research group), preliminary analyses indicate that the primary reason the feedback tool may nothaveproducedany meaningful change in HHC is that the tool was not widely disseminated beyond the leadership and nurse managers at each facility. We purposefully provided no guidance about how to use the feed- back tool, but simply sent it to the person responsible for HHC at a given site to see how it would be naturally used and dis- seminated. Although it is possible that the feedback was used to make top-down changesin policytopromote HHC, it is also possible that the tool needed to be shared more broadly with hospital staff to produce bottom-up changes in HHC behaviors. There are a number of potential explanations for why the feedback tool was not widely disseminated including, but not limited to, ambiguity aversion29,30 resulting from the absence of precise numeric HHC estimates; risk aversion31,32 over concerns about presenting information indicating substantially lower HHC rates than believed; and/or increased scrutiny or disbelief about the feedback information since it conflicted with expec- tationsordesires (a processcalled biased assimilation).33,34 Also, the feedback tool may have presented information in a way that was difficult to interpret. Identifying the specific barriers will be necessary to determine what changes are needed in future attempts to provide feedback about HHC based on rapid, covert observations. Additional research is also necessary to determine whether
the size of the Hawthorne effect varies across different locations (ie, unit, facility, healthcare system) and, if variability exists, whether the size of the effect is independently associated with HAIs. For example, would HAI risk be higher for a hospital that reports HHC of 90% but covert observations suggest HHC is 65% (difference of 25%) compared to a hospital that reports HHC of 80% when covert observations suggest HHC is 60% (difference of 20%). In other words, we do not know whether either method of measuring HHC, or the size of the discrepancy between the 2 methods, is more predictive of HAIs. Although more accurate measures of HHC would have a stronger asso- ciation with HAI rates, it is also possible that facilities may be
Aaron M. Scherer et al
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