Cognitive biases and collapse of prioritization accuracy under incongruent clinical data: a mixed-methods study of nursing diagnostic reasoning.

Abstract

Objectives: To characterize clinical reasoning in prioritization and test whether errors are linked to experience or are universal, by examining how information congruence and informativeness influence nurses' prioritization and diagnostic reasoning, and by identifying cognitive mechanisms underlying systematic errors under clinical uncertainty.

Methods: A concurrent embedded mixed-methods study was conducted with 130 nurses from two university hospitals. Using a think-aloud protocol, participants reasoned through four experimentally controlled clinical scenarios in which information congruence (data aligned vs. misaligned with the most common diagnosis) and informativeness (amount of data) were manipulated. Prioritization accuracy (correct vs. incorrect priority) was the primary outcome. Qualitative analysis identified cognitive biases, which were entered into a logistic regression model to quantify their association with accuracy.

Results: Accuracy collapsed when nurses faced incongruent clinical data, falling from 49.3 % in congruent scenarios to 18.4 % in incongruent ones (31-point drop; 95 % CI 20-42 %; p<0.001). This decrement was independent of age, experience, educational level, and ward type. Qualitative analysis showed that most nurses (71.4 %) actively dismissed critical conflicting cues. Confirmation bias (OR=0.048, p=0.015) and information bias (OR=0.082, p=0.010), were strong significant predictors of incorrect prioritization.

Conclusions: Nursing prioritization errors are systematic cognitive failures rather than random mistakes or simple knowledge deficits. The core vulnerability appears to be metacognitive: an impaired ability to detect and resolve conflict between an activated mental model and new, incongruent information. Interventions to reduce diagnostic and prioritization errors should explicitly train cognitive and metacognitive skills for managing incongruence and flexibly updating clinical representations.