Ten misconceptions regarding decision-making in critical care

Table 3 Ten misconceptions and realities for understanding and improving critical care decision-making

Misconception	Reality
Diagnostic errors resulting in adverse events are infrequent and of little impact on critically ill patients	Diagnostic errors are prevalent and associated with significant patient harm and cost
Useful models for understanding clinical decision-making are lacking	Cognitive science has provided insight into clinical decision-making that can be used to reduce error
Most diagnostic errors are due to infrequent conditions and clinician inexperience	Diagnostic errors occur most frequently with atypical presentations of commonly occurring conditions
Advanced laboratory diagnostics have reduced the value of a thorough history and comprehensive physical exam	History taking and physical examination remain central to the process of diagnostic reasoning
Decision-making errors are most effectively avoided by slowing down and trying harder	General-purpose directives to 'try harder' or "slow down and be thorough" are often suggested to allow time for analytical reasoning, but multiple studies of this technique have shown little benefit in improving cognitive performance
Debiasing strategies are most effective for novice practitioners	Studies suggest limited benefits of debiasing training for novice practitioners since they often do not have enough experience to utilize heuristics, leading them to fall victim to cognitive biases. As novice practitioners acquire additional knowledge and clinical experience, they are more likely to use heuristics and will more likely benefit from debiasing strategies
A robust body of evidence exists for the effectiveness of debiasing strategies in clinical decision-making	Recent reviews of debiasing interventions show promise for improving diagnostic accuracy but demonstrated benefit in clinical practice is currently lacking
Valid methods for assessment of decision-making are lacking	Many clinical reasoning assessments have been each with their strengths and limitations. Utilizing a variety of assessment tools for decision-making together with developmental milestones is essential to support learning
Clinical decision support systems based on artificial intelligence obviate the need for diagnostic reasoning	AI algorithms hold promise for improving decision-making, but understanding the potential biases in such systems is essential. An integrated approach combining the unique advantages of AI pattern recognition and human contextual interpretation will likely result in the best patient outcomes
Peer review is best used to identify medical errors and assign responsibility	Maximizing the value of the PRC requires both recognizing the decisions and errors involved and reflecting on them. Evaluation of clinical cases should move away from the single-dimensional approach of assigning individual fault and toward recognizing the multiplicity of factors that contribute to diagnostic error and the ultimate outcome

AI: Artificial intelligence; PRC: Peer review conferences.