dual-process theory and diagnostic error in clinical reasoning

Running head: DUAL-PROCESS THEORY AND DIAGNOSTIC ERROR IN CLINICAL… 1

Dual-Process Theory and Diagnostic Error in Clinical Reasoning

Matthew D. Moore

University of Louisville

DUAL-PROCESS THEORY AND DIAGNOSTIC ERROR IN CLINICAL REASONING

Dual-Process Theory and Diagnostic Error in Clinical Reasoning

Dual-process theory (DPT) describes two ways of information processing in which

humans engage everyday. Its first system is intuitive and responds quickly to the environment.

Its second system is deliberate and requires active attention. Some researchers have investigated

clinicians’ processes when making diagnoses and have found that each of the two systems of

DPT plays an important role in clinical reasoning (CR). Others criticize the use of system 1

processing for its potentially biased responses, claiming that this system is highly responsible for

diagnostic error. Some recent literature aims to dispel such criticisms regarding CR, claiming

that errors arise from a number of problems that aren’t related solely to system 1. Many argue

that the two systems are used in conjunction with one another, and that this way of processing

information has the best outcome. This review aims to synthesize the literature on such debates

and offer directions for further research. Additionally, I propose an adaptation to a current model

of CR that illustrates one of the common clinical errors that can arise.

Let’s begin with a review of DPT. As described by Barrett et al. (2004), DPT involves

automatic processing (system 1) and controlled processing (system 2). System 1 is the default

mode of processing. It’s primitive by nature and evolutionary inheritance, as its hallmark is a

lack of conscious thought, and it exists in much of the Animal kingdom. Other names for it are

nonconscious, non-analytic, implicit, and heuristic processing. System 1 processing is highly

dependent on prior knowledge—schemas in long-term memory—to guide associative pathways.

The associative aspect makes it much faster than system 2, which is hallmarked by slow and

careful consideration. System 2 works entirely within the boundaries of working memory, which

has a relatively low capacity for information handling and limited attentional resources. System 2

is also called conscious, analytic, explicit, and systematic processing. It can inhibit system 1

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processing if needed (such as when system 1 activates the wrong schema). This is system 2’s

main purpose in the clinical models examined in this review.

Previous models of CR include the hypothetico-deductive model and the pattern

recognition model (Marcum, 2012). The hypothetico-deductive model was first articulated in the

1970s, stemming from research on medical problem solving. In this model clinicians formulate a

few hypotheses and use them to obtain more evidence until a diagnosis is reached. This process

is highly analytical, most consistent with system 2 processing. An alternative to this model is the

pattern recognition model, which is most consistent with system 1 processing. In the pattern

recognition model clinicians match the current case with ones encountered in the past. Advocates

of this model value associative processing’s ability to view cases holistically, while critics say it

“…trivializes the complex cognitive activities involved in CR” (Marcum, 2012). Marcum

criticized the linear trajectory of these models and other like them, because they don’t feed back

onto previous processes. He proposed a cyclical model for CR that integrates aspects of these

previous models.

Marcum’s (2012) illustrations of CR include four different phases: non-analytic

processes, analytic processes, clinical decision, and metacognition (MC). Depending on its

position in the sequence, MC either monitors the reasoning processes or validates or rejects a

decision. Analytic processing includes traditional system 2 processes, as well as higher-order

processes (see Evans’ type 3 processes and Stanovich’s reflective mind). In Marcum’s first

model (Fig. 1), non-analytic (i.e., system 1) processes are used to obtain the differential

diagnosis. If a single pattern stands out that matches the case at hand, a clinical decision is made,

followed by metacognition. In Marcum’s second model (Fig. 2), if a clinical decision can’t be

made after non-analytic processes (e.g., a single pattern doesn’t stand out, there is inconclusive

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evidence, etc.), then analytic (i.e., system 2) processes are recruited. After analytic processing

comes MC, then a clinical decision, then MC again. When correct diagnoses are reached the two

models’ cyclical nature allows for associations to grow stronger, which fosters clinical expertise.

No feedback on correct processing can lead to having a lot of experience with very little

expertise. Marcum’s models account for the development of expertise.

An earlier study by Balla et al. (2009) supports Marcum’s model. Balla and colleagues

conducted a one-year study in which they interviewed general practitioners in the United

Kingdom. Practitioners were asked questions about their processing in cases they’d recently

seen. Interviewers asked participants about “…what went through their minds…” (Balla et al.,

2009) in those cases during a 20-minute semi-structured interview of open-ended questions. 35

practitioners participated and generated 72 cases to be analyzed. Two researchers reviewed

manuscripts of the cases for trends supporting or rejecting DPT. They found much evidence

supporting DPT, particularly for system 1 processing. In 79% of the cases a rapid judgment was

made. In over half the cases heuristics were used, which is a function of system 1. Likewise, in

nearly half the cases ‘ruling out’ or differentially diagnosing was explicitly stated as the focus,

while only a few cases stated ruling in possible conditions as the focus. This is consistent with

the first step in Marcum’s models, a process of elimination using system 1. Consistent with

system 2 processing, Balla and colleagues note that clinicians reframed problems and changed

their immediate responses in one third of the cases. In Marcum’s second model this occurs when

system 2 is recruited to settle discrepancies in system 1 processing. Balla et al. provide a model

for the strategies they saw in the practitioners (Fig 3) similar to Marcum’s model. Neither of

these models gives accounts for error, however, and the literature has a lot to say about it.

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Some of the criticisms of system 1 have already been discussed, but there are many

others. System 1 is often seen as a biased system, which in a clinical setting could mean favoring

a familiar or common diagnosis albeit wrong. The consensus of the literature on diagnostic error

is that errors are products mostly of cognitive bias, which is related to system 1 (Norman & Eva,

2010). Norman and Eva challenge this notion, finding that there isn’t much evidence linking

system 1 to diagnostic error. Their investigation begins with a review of error prevalence. An

extensive review by Berner and Graber (2008; as cited by Norman & Eva, 2010) concludes that

errors in fields such as radiology and pathology were usually less than 5%. An earlier study by

Graber (2005; as cited by Norman & Eva, 2010) reviewed 100 cases of diagnostic error.

Although nearly 75% of them involved some extent of cognitive error, less than half of those

cognitive errors were related to what Graber calls ‘Faulty Information Processing’, which may

relate to either processing system. The other three domains were ‘Faulty Knowledge’, ‘Faulty

Data Gathering’, and ‘Faulty Verification’, none of which are solely related to system 1. Graber

found ‘premature closure’ (i.e., stopping the consideration of other possibilities after reaching a

diagnosis) to be the most common error. System 1 bias does play a role in premature closure, but

it’s merely a correlate alongside other factors, such as incomplete history taking and failure to

consider the correct diagnosis.

While a mass of literature criticized the use of nonconscious processing, a few works find

it to be powerful in making complex decisions. Dijksterhuis et al. (2006) call it the ‘deliberation-

without-attention effect’. They found that in making simple decisions based on fewer criteria

conscious deliberation produces higher accuracy and greater decision satisfaction. More

importantly they found that in making decisions based on more criteria unconscious deliberation

produced higher accuracy and greater decision satisfaction. Considering overlap in diagnostic

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categories and abundance of existing conditions, clinical decision-making can be viewed as

highly complex with many criteria to juggle. Based on their findings, Dijksterhuis and colleagues

might suggest clinicians to use less conscious deliberation than the hypothetico-deductive model

or Marcum’s models prescribes. More importantly, their study emphasizes the importance and

reliability that system 1 unconscious processing can have in the right situation.

Croskerry (2009) compiled a number of possible cognitive failures that don’t rest alone

on system 1. He posits that sometimes system 2’s performance monitoring over system 1

processing can fail. For example, cognitive overload can negatively affect system 2’s ability to

monitor system 1 (Gilbert et al., 2003; as cited by Croskerry, 2009). Fatigue and sleep

deprivation can cause similar effects. This cognitive failure illustrates that, while system 1 can

make an error in judgment, system 2 can be equally responsible for not inhibiting or correcting

system 1’s response. Like Norman and Eva, Croskerry also cites Berner and Graber’s (2008)

study, noting that overconfidence appears to be involved in many diagnostic errors.

Overconfidence is mutually exclusive to neither processing system, favoring a de-emphasis of

error attribution to system 1.

This review leads to a few conclusions. First, there is a good deal of evidence suggesting

that clinicians use both associative and deliberate processing in the diagnostic process. Each

process has its own place in the CR process where it functions better than the other. For example,

since so many clinicians try first to eliminate possible conditions, system 1 is best used to obtain

a differential diagnosis. Its dependence on prior knowledge and independence of active attention

accelerate the differential diagnostic process in CR. On the other hand, system 2 is best used to

aid system 1 when a diagnosis can’t be reached. Its conscious and rationale components allow it

to direct attention to other foci in search of more evidence. Marcum applies these processes in

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his models. In regard to diagnostic error, there is a growing amount of evidence discordant with

the notion that biased cognition from system 1 processing accounts for most diagnostic errors.

Researchers have found a number of ways that cognitive errors arise that can’t be attributed to

system 1 alone. Most errors arise from an amalgamation of poor CR processes, such as faulty

knowledge and premature closure. This isn’t to say that system 1 is flawless. The potential for

bias is always there, but system 1 bias appears to be less responsible for error than many critics

currently hold it.

Marcum’s model of CR is particularly unique because of its cyclical nature and ability to

feed back on prior processes. It accounts for the process of developing expertise, unlike any

other model in this review. Still, this model needs more research to be determined a good tool for

teaching rising clinicians. In the meantime, I propose two adaptations to Marcum’s second model

from Figure 2, illustrating Graber’s (2005) most commonly found error: premature closure.

These adapted models are intended to demonstrate two ways this particular error might arise,

either before analytic processing (Fig. 2a) or after analytic processing (Fig. 2b). In Fig. 2a, non-

analytic processes can’t reach a diagnosis, signaling analytic processes to intervene. However,

analytic processes don’t intervene due to premature closure, and a decision is made by system 1

based on inconclusive evidence (noted as ‘premature closure, type I’). In Fig. 2b, a conflict arises

between non-analytic processes and lower-order analytic processes. Higher-order analytical

processes (i.e. type 3 or the reflective mind) aren’t allowed to resolve the conflict due to

premature closure, and a decision is made by an automatic system 2 override (noted as

‘premature closure, type II’). In both adaptations, MC isn’t allowed time to monitor operations or

validate or reject the decision. These adaptations visually represent that while one system

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generates the incorrect diagnosis, other system failures and external stressors perpetuate the

incorrect diagnosis. In such cases, error can be attributed to multiple systems, not just system 1.

The direction of the research on CR looks promising. Evidence is building in support of

models such as Marcum’s. With continued research these models may be concluded as reliable

in accurately portraying clinicians’ processes. Such models can be very helpful in teaching CR

and diagnosis, providing the framework for new clinicians to integrate their processing systems

and foster expertise. The proposed adaptations to Marcum’s model may also be helpful in

teaching how and where errors can occur, providing insight to new clinicians so that they can

avoid such errors. There is no light way to say that clinicians make errors too. Diagnostic error

could mean a client’s death. Researchers of CR should take every step forward to come up with

reliable ways to help clinicians avoid such errors.

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Figure 1. Continuous cyclical or spiral model of non-analytic processes, along with metacognition, for clinical reasoning and decision making. Adapted from “An integrated model of clinical reasoning: dual-process theory of cognition and metacognition,” by J. A. Marcum, 2010, Practice, 18, p. 957. Copyright 2012 by Blackwell Publishing Ltd.

Figure 2a. An adaptation of Marcum’s Fig. 2 illustrating premature closure, type I. Adapted from “An integrated model of clinical reasoning: dual-process theory of cognition and metacognition,” by J. A. Marcum, 2010, Journal of Evaluation in Clinical Practice, 18, p. 957. Copyright 2012 by Blackwell Publishing Ltd.

Figure 2b. An adaptation of Marcum’s Fig. 2 illustrating premature closure, type II. Adapted from “An integrated model of clinical reasoning: dual-process theory of cognition and metacognition,” by J. A. Marcum, 2010, Journal of Evaluation in Clinical Practice, 18, p. 957. Copyright 2012 by Blackwell Publishing Ltd.

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References

Balla, J. I., Heneghan, C., Glasziou, P., Thompson, M., & Balla, M. E. (2009). A model for

reflection for good clinical practice. Journal of Evaluation in Clinical Practice, 15, 964-

969. doi:10.1111/j.1365-2753.2009.01243.x

Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual Differences in Working

Memory Capacity and Dual-Process Theories of the Mind. Psycho Bull., 130(4), 553-

573.

Figure 3. Model for reflection on clinical practice. Adapted from “A model for reflection for good clinical practice,” by J. I. Balla, C. Heneghan, P. Glasziou, M. Thompson, and M. E. Balla, 2009, Journal of Evaluation in Clinical Practice, 15, p. 967. Copyright 2009 by Blackwell Publishing Ltd.j

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Croskerry, P. (2009). Clinical cognition and diagnostic error: applications of a dual process

model of reasoning. Adv in Health Sci Educ, 14, 27-35. doi:10.1007/s10459-009-9182-2

Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006). On Making the Right

Choice: The Deliberation-Without-Attention Effect. Science, 311, 1005-1007. doi:

10.1126/science.1121629

Marcum, J. A. (2012). An integrated model of clinical reasoning: dual-process theory of

cognition and metacognition. Journal of Evaluation in Clinical Practice, 18, 954-961.

doi:10.1111/j.1365-2753.2012.01900.x

Norman, G. R., & Eva, K. W. (2010). Diagnostic error and clinical reasoning. Medical

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dual-process theory and diagnostic error in clinical reasoning

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