ArticleBMC medical informatics and decision making2013
Development and validation of an algorithm to recalibrate mental models and reduce diagnostic errors associated with catheter-associated bacteriuria.
Article in BMC medical informatics and decision making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01052545 (Guideline Implementation to Decrease Inappropriate Bacteriuria Treatment), which is not on this map. Cited by 28 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Guideline Implementation to Decrease Inappropriate Bacteriuria Treatment
Who cites it
28 citing papers in PubMed, 60 citations in OpenAlex.
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- Analysis of an Antibiotic Stewardship Program for Asymptomatic Bacteriuria in the Veterans Affairs Health Care System.JAMA network open · 2022Article
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- The Five Ds of Outpatient Antibiotic Stewardship for Urinary Tract Infections.Clinical microbiology reviews · 2021Review
- An Algorithm (LaD) for Monitoring Childbirth in Settings Where Tracking All Parameters in the World Health Organization Partograph Is Not Feasible: Design and Expert Validation.JMIR medical informatics · 2021Article
- Predictive Value of Urinalysis and Recent Antibiotic Exposure to Distinguish Between Bacteriuria, Candiduria, and No-Growth Urine.Infection and drug resistance · 2021Article
- A Primary Care Provider's Guide to Management of Neurogenic Lower Urinary Tract Dysfunction and Urinary Tract Infection After Spinal Cord Injury.Topics in spinal cord injury rehabilitation · 2020Review
- Teamwork and safety climate affect antimicrobial stewardship for asymptomatic bacteriuria.Infection control and hospital epidemiology · 2019Article
- Genitourinary Complications Are a Leading and Expensive Cause of Emergency Department and Inpatient Encounters for Persons With Spinal Cord Injury.Archives of physical medicine and rehabilitation · 2019Article
- Effect of changing urine testing orderables and clinician order sets on inpatient urine culture testing: Analysis from a large academic medical center.Infection control and hospital epidemiology · 2019Article
- Determining Best Practices for Management of Bacteriuria in Spinal Cord Injury: Protocol for a Mixed-Methods Study.JMIR research protocols · 2019Article
- Spinal Cord Injury Creates Unique Challenges in Diagnosis and Management of Catheter-Associated Urinary Tract Infection.Topics in spinal cord injury rehabilitation · 2019Review
- No need for a urine culture in elderly hospitalized patients with a negative dipstick test result.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018Article
- Routine Urine Testing at the Spinal Cord Injury Annual Evaluation Leads to Unnecessary Antibiotic Use: A Pilot Study and Future Directions.Archives of physical medicine and rehabilitation · 2018Article
- Protocol to disseminate a hospital-site controlled intervention using audit and feedback to implement guidelines concerning inappropriate treatment of asymptomatic bacteriuria.Implementation science : IS · 2018Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvertreatment 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.
methodsWe 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.
resultsOnly 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).
conclusionsDiagnostic 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.