While research has focused on improving data through statistical risk-adjustment, whether clinicians understand these data is unknown. Therefore, we assessed clinician comprehension of central line-associated blood stream infection (CLABSI) quality metric data” Govindan et al (2017).
OBJECTIVE: Despite significant efforts and cost, quality metrics do not consistently influence practice. While research has focused on improving data through statistical risk-adjustment, whether clinicians understand these data is unknown. Therefore, we assessed clinician comprehension of central line-associated blood stream infection (CLABSI) quality metric data.
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DESIGN: Cross-sectional survey with an 11-item test of CLABSI data comprehension. Each question assessed 1 of 3 concepts concerning CLABSI understanding: basic numeracy, risk-adjustment numeracy, and risk-adjustment interpretation. Hypothetical data were used and presented in a validated format.
PARTICIPANTS: Clinicians were recruited from 6 nations via Twitter to take an online survey. Clinician eligibility was confirmed by assessing responses to a question regarding CLABSI.
MAIN MEASURES: The primary outcome was percent correct of attempted questions pertaining to the presented CLABSI data.
RESULTS: Ninety-seven clinicians answered at least 1 item, providing 939 responses; 72 answered all 11 items. The mean percentage of correct answers was 61% (95% confidence interval [CI], 57%-65%). Overall, doctor performance was better than performance by nurses and other respondents (68% [95% CI, 63%-73%] vs. 57% [95% CI, 52%-62%], P = 0.003). In basic numeracy, mean percent correct was 82% (95% CI, 77%-87%). For risk-adjustment numeracy, the mean percent correct was 70% (95% CI, 64%-76%). Risk-adjustment interpretation had the lowest average percent correct, 43% (95% CI, 37%-49%). All pairwise differences between concepts were statistically significant at P <0.05.
CONCLUSIONS: CLABSI quality metric comprehension appears low and varies substantially among clinicians. These findings may contribute to the limited impact of quality metric reporting programs, and further research is needed.
Govindan, S., Chopra, V. and Iwashyna, T.J. (2017) Do Clinicians Understand Quality Metric Data? An Evaluation in a Twitter-Derived Sample. Journal of Hospital Medicine. 12(1), p.18-22.
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