Ten misconceptions regarding decision-making in critical care

Table 1 Comparison of system 1 and system 2 thinking

	System 1	System 2
Basis for decisions	Heuristics, Pattern Recognition	Logical, analytical
Activation	Default system	Activated when needed (e.g., atypical or complex presentation)
Speed	Efficient, time sparing	Rigorous, time-consuming
Optimal use	Familiar situations	Unfamiliar or uncertain situations
Role of information	Limited information required	Information required to minimize uncertainty
Role of experience	Relies on prior training and experience	Relies on the pursuit of new knowledge/information
	Efficiency and accuracy improve with experience	Provides useable tools for novices
Limitations	Susceptible to cognitive biases	Accuracy is dependent on effort and time
		Increases cognitive load
	Not reliable for novices	Less useful in stressful events

The table was adapted from Hartigan et al[36] and Lighthall et al[75].

Table 2 Common cognitive biases encountered in critical care decision making

Cognitive bias	Description
Anchoring bias	Relying too heavily on the first information received when making decisions
Availability bias	Judging a diagnosis as more likely if it quickly and readily comes to mind
Confirmation bias	Selectively seeking information to support a diagnosis rather than information to refute it
Diagnostic momentum	Attaching diagnostic labels and not reevaluating them
Dunning-Kruger effect	The tendency for a novice to overestimate their skills
Framing effect	Arriving at different conclusions depending on how the information is presented
Hindsight bias	Interpreting past events as more predictable than they actually are
Premature closure	Finalizing a diagnosis without full confirmation
Sunk cost bias	Difficulty considering alternatives when time, effort, and energy are invested in a particular diagnosis

The table was adapted from Lighthall et al[75] and Webster et al[76].

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.