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).
Abstract:
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.
Reference:
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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