Background: Overtreatment of catheter-associated bacteriuria is a quality and safety problem, despite the availability of evidence-based guidelines. Little is known about how guidelines-based knowledge is integrated into clinicians' mental models for diagnosing catheter-associated urinary tract infection (CA-UTI). The objectives of this research were to better understand clinicians' mental models for CA-UTI, and to develop and validate an algorithm to improve diagnostic accuracy for CA-UTI.
Methods: We conducted two phases of this research project. In phase one, 10 clinicians assessed and diagnosed four patient cases of catheter associated bacteriuria (n= 40 total cases). We assessed the clinical cues used when diagnosing these cases to determine if the mental models were IDSA guideline compliant. In phase two, we developed a diagnostic algorithm derived from the IDSA guidelines. IDSA guideline authors and non-expert clinicians evaluated the algorithm for content and face validity. In order to determine if diagnostic accuracy improved using the algorithm, we had experts and non-experts diagnose 71 cases of bacteriuria.
Results: Only 21 (53%) diagnoses made by clinicians without the algorithm were guidelines-concordant with fair inter-rater reliability between clinicians (Fleiss' kappa = 0.35, 95% Confidence Intervals (CIs) = 0.21 and 0.50). Evidence suggests that clinicians' mental models are inappropriately constructed in that clinicians endorsed guidelines-discordant cues as influential in their decision-making: pyuria, systemic leukocytosis, organism type and number, weakness, and elderly or frail patient. Using the algorithm, inter-rater reliability between the expert and each non-expert was substantial (Cohen's kappa = 0.72, 95% CIs = 0.52 and 0.93 between the expert and non-expert #1 and 0.80, 95% CIs = 0.61 and 0.99 between the expert and non-expert #2).
Conclusions: Diagnostic errors occur when clinicians' mental models for catheter-associated bacteriuria include cues that are guidelines-discordant for CA-UTI. The understanding we gained of clinicians' mental models, especially diagnostic errors, and the algorithm developed to address these errors will inform interventions to improve the accuracy and reliability of CA-UTI diagnoses.