Significant variability in the interpretation of CLABSI data exists among experts

Govindan, S., Wallace, B., Iwashyna, T.J. and Chopra, V. (2017) Do Experts Understand Performance Measures? A Mixed-Methods Study of Infection Preventionists. Infection Control and Hospital Epidemiology. December 5th. [epub ahead of print].

doi: 10.1017/ice.2017.243.

OBJECTIVE: Central line-associated bloodstream infection (CLABSI) is associated with significant morbidity and mortality. Despite a nationwide decline in CLABSI rates, individual hospital success in preventing CLABSI is variable. Difficulty in interpreting and applying complex CLABSI metrics may explain this problem. Therefore, we assessed expert interpretation of CLABSI quality data.

DESIGN: Cross-sectional survey

PARTICIPANTS: Members of the Society for Healthcare Epidemiology of America (SHEA) Research Network (SRN)

METHODS: We administered a 10-item test of CLABSI data comprehension. The primary outcome was percent correct of attempted questions pertaining to the CLABSI data. We also assessed expert perceptions of CLABSI reporting.

RESULTS: The response rate was 51% (n=67).Among experts, the average proportion of correct responses was 73% (95% confidence interval [CI], 69%-77%). Expert performance on unadjusted data was significantly better than risk-adjusted data (86% [95% CI, 81%-90%] vs 65% [95% CI, 60%-70%]; P<.001). Using a scale of 1 to 100 (0, never reliable; 100, always reliable), experts rated the reliability of CLABSI data as 61. Perceived reliability showed a significant inverse relationship with performance (r=-0.28; P=.03), and as interpretation of data improved, perceptions regarding reliability of those data decreased. Experts identified concerns regarding understanding and applying CLABSI definitions as barriers to care.

CONCLUSIONS Significant variability in the interpretation of CLABSI data exists among experts. This finding is likely related to data complexity, particularly with respect to risk-adjusted data. Improvements appear necessary in data sharing and public policy efforts to account for this complexity.

By IV Team 12/12/17 link to pdf

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