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Novice clinicians and medical students are more likely to rely on analytical reasoning throughout the diagnostic process compared to experienced clinicians Croskerry, b; Elstein and Schwartz, ; Kassirer, ; Norman, Expert clinicians possess better developed mental models of diseases, which support more reliable pattern matching system 1 processes Croskerry, b. As a clinician. The ability to create and develop mental models through repetition explains why expert clinicians are more likely to rely on pattern recognition when making diagnoses than are novices—continuous engagement with disease conditions allows the expert to develop more reliable mental models of disease—by retaining more exemplars, creating more nuanced prototypes, or developing more detailed illness scripts.

Figure illustrates the concept of calibration, or the process of a clinician becoming aware of his or her diagnostic abilities and limitations through feedback. Feedback mechanisms—both in educational settings see Chapter 4 and in learning health care systems see Chapter 6 —allow. Academic Emergency Medicine 7 11 —, Calibration enables clinicians to assess their diagnostic accuracy and improve their future performance. Work system factors influence diagnostic reasoning, including diagnostic team members and tasks, technologies and tools, organizational characteristics, the physical environment, and the external environment.

For example, Chapter 6 describes how the physical environment, including lighting, noise, and layout, can influence clinical reasoning.

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Chapter 5 discusses how health IT can improve or degrade clinical reasoning, depending on the usability of health IT including clinical decision support , its integration into clinical workflow, and other factors. Box describes how certain individual characteristics of diagnostic team members can affect clinical reasoning.

As described above, the diagnostic process involves initial information gathering that leads to a working diagnosis. The process of ruling in or ruling out a diagnosis involves probabilistic reasoning as findings are integrated and interpreted. Probabilistic or Bayesian reasoning provides a formal method to avoid some cognitive biases when integrating and interpreting information. For instance, when patients present with typical symptoms but the disease is rare e. Using Bayesian reasoning and formally revising probabilities of the various diseases under consideration helps clinicians avoid these errors.

Clinicians can then decide whether to pursue additional information gathering or treatment based on an accurate estimate of the likelihood of disease, the harms and benefits of treatment, and patient preferences Kassirer et al. Probabilistic reasoning is most often considered in the context of diagnostic testing, but the presence or absence of specific signs and symptoms can also help to rule in or rule out diseases.

The likelihood of a positive finding the presence of signs or symptoms or a positive test when disease is present is referred to as sensitivity. The likelihood of a negative finding the absence of symptoms, signs, or a negative test when a disease is absent is referred to as specificity. If a sign, symptom, or test is always positive in the presence of a particular disease percent sensitivity , then the absence of that symptom, sign, or test rules out disease e.

There are a number of individual characteristics that can affect clinical reasoning, including intelligence and knowledge, age, affect, experience, personality, physical state, and gender. High scores on intelligence tests indicate that an individual is adept at these cognitive tasks and is more likely to engage system 2 processes to monitor and, when necessary, override system 1 processing Croskerry and Musson, ; Eva, ; Evans and Stanovich, Although intelligence that allows one to monitor and override system 1 processing is important, it rarely suffices by itself for good clinical reasoning.

A sufficiently large knowledge base of both biological science and disease conditions is also important. It is likely that clinician age has an impact on clinical reasoning abilities Croskerry and Musson, ; Eva, ; Singer et al. For example, older and more experienced clinicians may be better able to employ system 1 processes in diagnosis, due to well-developed mental models of disease. However, as clinicians age, they tend to have more trouble considering alternatives and switching tasks during the diagnostic process Croskerry and Musson, ; Eva, Not all individuals experience cognitive or memory decline at the same rate or time though many people start to experience moderate declines in analytical reasoning capacity at some point in their 70s Croskerry and Musson, Affective factors such as mood and emotional state often play a role both positive and negative in clinical reasoning and decision making Blanchette and Richards, ; Croskerry, b; Croskerry et al.

When an obvious solution to a problem is not present, emotions may help direct people toward an outcome that is better than one that would be produced by random choice Johnson-Laird and Oatley, ; Stanovich, In cases where precision is important or when an emotional response is unlikely to be a reliable indicator, the affect heuristic can lead to negative consequences.

In these cases, the. Affective states such as irritation and stress due to environmental conditions can also affect reasoning, primarily through decreasing the ability of system 2 processes to monitor and override system 1 processes Croskerry et al. Novices and experts employ different decision-making practices Kahneman, Expert nurses, for instance, have been found to collect a wider range of cues than their novice counterparts during clinical decision making Hoffman et al. Expert clinicians are more likley to rely on system 1 processing during the diagnostic process, while novice practioners and medical students rely more on conscious, explicit, linear analytical reasoning.

Furthermore, expert clinicians are likely to be more accurate than novices when they employ system 1 processes because they have larger stores of developed mental models of disease conditions.

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While some have argued that experts are more susceptible to premature closure i. Individual personality influences clinical reasoning and decision making Croskerry and Musson, Arrogance, for instance, may lead to clinician overconfidence, a personality trait identified as a source of diagnostic error Berner and Graber, ; Croskerry and Norman, Other personality traits, such as openness to experiences and agreeableness, could improve decision making in some individuals if it increases their openness to divergent views and feedback.

Fatigue and sleep deprivation have been found to impede system 2 processing interventions on system 1 processes Croskerry and Musson, ; Zwaan et al.


Additionally, some research suggests that there are gender-specific effects associated with reasoning, including a male tendency toward risk-taking Byrnes et al. Other studies have failed to replicate this proposed gender effect Croskerry and Musson, If a sign, symptom, or test is always negative in the absence of a particular disease percent specificity , then the presence of that symptom, sign, or test rules in disease e. However, nearly all signs, symptoms, or test results are neither percent sensitive or specific.

For example, studies suggest exceptions for findings such as Kayser—Fleischer rings with other causes of liver disease Frommer et al. Bayesian calculators are available to facilitate these probability revision analyses Simel and Rennie, Box works through two examples of probabilistic reasoning. While most clinicians will not formally calculate probabilities, the logical principles behind Bayesian reasoning can help clinicians consider the trade-offs involved in further information gathering, decisions about treatment, or evaluating clinically ambiguous cases Kassirer et al.

Bayesian reasoning then calculates the likelihood of GABHS among those without nasal congestion to be The presence of three additional distinguishing symptoms tonsillar exudates, no cough, and swollen, tender anterior cervical nodes would raise the likelihood of GABHS to 70 percent, and if those three additional distinguishing symptoms were absent, the likelihood of GABHS would fall to 3 percent Centor et al. To provide a second example, suppose a woman has a 0. Among women with breast cancer, a mammogram will be positive in 90 percent sensitivity. Among women without breast cancer, a mammogram will be positive in 7 percent false positive rate or 1 minus a specificity of 93 percent.

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If the mammogram is positive, what is the likelihood of this woman having breast cancer? Among 1, women, 8 0.

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Among the without breast cancer, 69 7 percent of will have a false positive mammogram. Thus, among the 76 women with a positive mammogram, 7—or 9 percent—will have breast cancer. When a very similar question was presented to practicing physicians with an average of 14 years of experience, their answers ranged from 1 percent to 90 percent, and very few answered correctly Gigerenzer and Edwards, Thus, a better understanding of probabilistic reasoning can help clinicians apply signs, symptoms, and test results to subsequent decision making such as refining or expanding a differential diagnosis, determining the likelihood that a patient has a specific diagnosis on the basis of a positive or negative test result, deciding whether retesting or ordering new tests is appropriate, or beginning treatment see Chapter 4.

Advances in biology and medicine have led to improvements in prevention, diagnosis, and treatment, with a deluge of innovations in diagnostic testing IOM, , a; Korf and Rehm, ; Lee and Levy, The rising complexity and volume of these advances, coupled with clinician time constraints and cognitive limitations, have outstripped human capacity to apply this new knowledge IOM, a, a; Marois and Ivanoff, ; Miller, ; Ostbye et al. With the rapidly increasing number of published scientific articles on health see Figure , health care professionals have difficulty keeping up with the breadth and depth of knowledge in their specialties.

For example, to remain up to date, primary care clinicians would need to read for an estimated McGlynn and colleagues found that Americans receive only about half of recommended care, including recommended diagnostic processes. Thus, clinicians need approaches to ensure they know the evidence base and are well-equipped to deliver care that reflects the most up-to-date information.

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One of the ways that this is accomplished is through team-based. Publications have increased steadily over 40 years. In addition, systematic reviews and clinical practice guidelines CPGs help synthesize available information in order to inform clinical practice decision making IOM, a,b. CPGs came into prominence partly in response to studies that found excessive variation in diagnostic and treatment-related care practices, indicating that inappropriate care was occurring Chassin et al. CPGs can include diagnostic criteria for specific conditions as well as approaches to information gathering, such as conducting a clinical history and interview, the physical exam, diagnostic testing, and consultations.

CPGs translate knowledge into clinical care decisions, and adherence to evidence-based guideline recommendations can improve health care quality and patient outcomes Bhatt et al. However, there have been a number of challenges to the development and use of CPGs in clinical practice IOM, a, a,b; Kahn et al. Two of the primary challenges are the inadequacy of the evidence base supporting CPGs and determining the applicability of guidelines for individual patients IOM, a, b. For example, individual patient preferences for possible health outcomes may vary, and with the growing prevalence of chronic disease, patients often have comorbidities or competing causes of mortality that need to be considered.

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CPGs may not factor in these patient-specific variables Boyd et al. In addition, the majority of scientific evidence about any diagnostic test typically is focused on test accuracy and not on the impact of the test on patient outcomes Brozek et al. This makes it difficult to develop guidelines that inform clinicians about the role of diagnostic tests within the diagnostic process and about how these tests can influence the path of care and health outcomes for a patient Gopalakrishna et al.

Furthermore, diagnosis is generally not a primary focus of CPGs; diagnostic testing guidelines typically account for a minority of recommendations and often have lower levels of evidence supporting them than treatment-related CPGs Tricoci et al.

The adoption of available clinical practice guideline recommendations into practice remains suboptimal due to concerns about the trustworthiness of the guidelines as well as the existence of varying and conflicting guide-. Health care professional societies have also begun to develop appropriate use or appropriateness criteria as a way of synthesizing the available scientific literature and expert opinion to inform patient-specific decision making Fitch et al.

With the growth of diagnostic testing and substantial geographic variation in the utilization of these tools due in part to the limitations in the evidence base supporting their use , health care professional societies have developed appropriate use criteria aimed at better matching patients to specific health care interventions Allen and Thorwarth, ; Patel et al.

Checklists are another approach that has been implemented to improve the safety of care by, for example, preventing health care—acquired infections or errors in surgical care. Checklists have also been proposed to improve the diagnostic process Ely et al. Developing checklists for the diagnostic process may be a significant undertaking; thus far, checklists have been developed for discrete, observable tasks, but the complexity of the diagnostic process, including the associated cognitive tasks, may represent a fundamentally different type of challenge Henriksen and Brady, About the AAFP proficiency testing program. Points to consider in the clinical application in genomic sequencing.

Genetics in Medicine 14 8 Allen, B.