Development and validation of an algorithm to recalibrate mental models and reduce diagnostic errors associated with catheter-associated bacteriuria

• Published in: BMC medical informatics and decision making Vol. 13; no. 1; p. 48
• Main Authors: Trautner, Barbara W, Bhimani, Rupal D, Amspoker, Amber B, Hysong, Sylvia J, Garza, Armandina, Kelly, P Adam, Payne, Velma L, Naik, Aanand D
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.04.2013; BioMed Central Ltd; Springer Nature B.V
• Subjects: Adult; Algorithms; Bacteria; Bacteriuria - diagnosis; Catheter-Related Infections - diagnosis; Catheter-Related Infections - etiology; Clinical Competence; Decision making; Decision Support Techniques; Diagnostic errors; Diagnostic Errors - prevention & control; Female; Guideline Adherence; Health Informatics; Health Personnel - psychology; Health Personnel - standards; Humans; Infections; Information Systems and Communication Service; Male; Management of Computing and Information Systems; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Prevention; Reproducibility of Results; Research Article; Studies; Urinary Catheterization - adverse effects; Urinary tract infections; Urinary Tract Infections - diagnosis; United States; Diagnostic errors; Urinary tract infections; Catheter-associated bacteriuria; Evidence based guidelines
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-13-48

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.