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开发和验证一种算法,以重新校准心理模型并减少与导管相关性菌尿相关的诊断错误。

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

机构信息

Houston Health Services Research and Development Center of Excellence, Michael E, DeBakey VA Medical Center, Houston, TX, USA.

出版信息

BMC Med Inform Decis Mak. 2013 Apr 15;13:48. doi: 10.1186/1472-6947-13-48.

Abstract

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.

摘要

背景

尽管有循证指南,但过度治疗导管相关性菌尿仍是一个质量和安全问题。对于如何将基于指南的知识整合到临床医生诊断导管相关性尿路感染 (CA-UTI) 的思维模型中,我们知之甚少。本研究的目的是更好地了解临床医生对 CA-UTI 的思维模型,并开发和验证一种算法以提高 CA-UTI 的诊断准确性。

方法

我们进行了这个研究项目的两个阶段。在第一阶段,10 名临床医生评估和诊断了 4 例导管相关菌尿患者(共 40 例)。我们评估了用于诊断这些病例的临床线索,以确定思维模型是否符合 IDSA 指南。在第二阶段,我们根据 IDSA 指南开发了一种诊断算法。IDSA 指南作者和非专家临床医生评估了该算法的内容和表面有效性。为了确定使用该算法是否可以提高诊断准确性,我们让专家和非专家诊断了 71 例菌尿病例。

结果

没有使用算法的临床医生做出的诊断中,只有 21 例(53%)符合指南,临床医生之间的一致性一般(Fleiss' kappa = 0.35,95%置信区间 (CI) = 0.21 和 0.50)。有证据表明,临床医生的思维模型构建不当,因为他们认可与指南不一致的线索对其决策有影响:脓尿、全身性白细胞增多、病原体类型和数量、乏力以及老年或虚弱患者。使用该算法,专家与每位非专家之间的一致性较高(专家与非专家 #1 之间的 Cohen's kappa = 0.72,95%CI = 0.52 和 0.93,专家与非专家 #2 之间的 Cohen's kappa = 0.80,95%CI = 0.61 和 0.99)。

结论

当临床医生对导管相关性菌尿的思维模型包括与 CA-UTI 指南不一致的线索时,就会出现诊断错误。我们对临床医生思维模型的理解,特别是诊断错误,以及为解决这些错误而开发的算法,将为提高 CA-UTI 诊断的准确性和可靠性提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5da/3664217/3008ca194b38/1472-6947-13-48-1.jpg

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