Accurate, timely, and patient-centered diagnosis relies on proficiency in clinical reasoning, which is often regarded as the clinician's quintessential competency. Understanding the clinical reasoning process and the factors that can impact it are important to improving diagnosis, given that clinical reasoning processes contribute to diagnostic errors Croskerry, a ; Graber, Clinical reasoning occurs within clinicians' minds facilitated or impeded by the work system and involves judgment under uncertainty, with a consideration of possible diagnoses that might explain symptoms and signs, the harms and benefits of diagnostic testing and treatment for each of those diagnoses, and patient preferences and values.
The current understanding of clinical reasoning is based on the dual process theory, a widely accepted paradigm of decision making. The dual process theory integrates analytical and non-analytical models of decision making see Box Analytical models slow system 2 involve a conscious, deliberate process guided by critical thinking Kahneman, Nonanalytical models fast system 1 involve unconscious, intuitive, and automatic pattern recognition Kahneman, Fast system 1 nonanalytical, intuitive automatic processes require very little working memory capacity.
They are often triggered by stimuli or result from overlearned associations or implicitly learned activities. In contrast, slow system 2 reflective, analytical processing places a heavy load on working memory and involves hypothetical and counterfactual reasoning Evans and Stanovich, ; Stanovich and Toplak, System 2 processing requires individuals to generate mental models of what should or should not happen in particular situations, in order to test possible actions or to explore alternative causes of events Stanovich, Counterfactual reasoning occurs when one thinks about what should occur if the situation differed from how it actually is.
The deliberate, conscious, and reflective nature of both hypothetical and counterfactual reasoning illustrates the analytical nature of system 2.
Heuristics—mental shortcuts or cognitive strategies that are automatically and unconsciously employed—are particularly important for decision making Gigerenzer and Goldstein, Heuristics can facilitate decision making but can also lead to errors, especially when patients present with atypical symptoms Cosmides and Tooby, ; Gigerenzer, ; Kahneman, ; Klein, ; Lipshitz et al.
When a heuristic fails, it is referred to as a cognitive bias. Cognitive biases, or predispositions to think in a way that leads to failures in judgment, can also be caused by affect and motivation Kahneman, Prolonged learning in a regular and predictable environment increases the success-fulness of heuristics, whereas uncertain and unpredictable environments are a chief cause of heuristic failure Kahneman, ; Kahneman and Klein, There are many heuristics and biases that affect clinical reasoning and decision making see Table for medical and nonmedical examples.
Additional examples of heuristics and biases that affect decision making and the potential for diagnostic errors are described below Croskerry, b :. In addition to cognitive biases, research suggests that fallacies in reasoning, ethical violations, and financial and nonfinancial conflicts of interest can influence medical decision making Seshia et al.
The interaction between fast system 1 and slow system 2 remains controversial. Some hold that these processes are constantly occurring in parallel and that any conflicts are resolved as they arise. Others have argued that system 1 processes generate an individual's default response and that system 2 processes may or may not intervene and override system 1 processing Evans and Stanovich, ; Kahneman, When system 2 overrides system 1, this can lead to improved decision making, because engaging in analytical reasoning may correct for inaccuracies.
It is important to note that slow system 2 processing does not guarantee correct decision making. For instance, clinicians with an inadequate knowledge base may not have the information necessary to make a correct decision. There are some instances when system 1 processing is correct, and the override from system 2 can contribute to incorrect decision making. However, when system 1 overrides system 2 processing, this can also result in irrational decision making.
Intervention by system 2 is likely to occur in novel situations when the task at hand is difficult; when an individual has minimal knowledge or experience Evans and Stanovich, ; Kahneman, ; or when an individual deliberately employs strategies to overcome known biases Croskerry et al.
Monitoring and intervention by system 2 on system 1 is unlikely to catch every failure because it is inefficient and would require sustained vigilance, given that system 1 processing often leads to correct solutions Kahneman, Factors that affect working memory can impede the ability of system 2 to monitor and, when necessary, intervene on system 1 processes Croskerry, b.
For example, if clinicians are tired or distracted by elements in the work system, they may fail to recognize when a decison provided by system 1 processing needs to be reconsidered Croskerry, b. System 1 and system 2 perform optimally in different types of clinical practice settings. System 1 performs best in highly reliable and predictable environments but falls short in uncertain and irregular settings Kahneman and Klein, ; Stanovich, System 2 performs best in relaxed and unhurried environments.
This section applies the dual process theory of clinical reasoning to the diagnostic process Croskerry, a , b ; Norman and Eva, ; Pelaccia et al. Croskerry and colleagues provide a framework for understanding the cognitive activities that occur in clinicians as they iterate through information gathering, information integration and interpretation, and determining a working diagnosis Croskerry et al. The dual process model of diagnostic decision making. When a patient presents to a clinician, the initial data include symptoms and signs of disease, which can range from single characteristics of disease to illness scripts.
If the symptoms and signs more When patients present, clinicians gather information and compare that information with their knowledge about various diseases. This can include comparing a patient's signs and symptoms with clinicians' mental models of diseases or information about diseases that is stored in memory as exemplars, prototypes, or illness scripts; see Box This initial pattern matching is an instance of fast system 1 processing.
If a sufficiently unique match occurs, then a diagnosis may be made without involvement of slow system 2. However, some symptoms or signs may not be recognized or they may trigger mental models for several diseases at once. When this happens, slow system 2 processing may be engaged, and the clinician will continue to gather, integrate, and interpret potentially relevant information until a working diagnosis is generated and communicated to the patient.
When this process triggers pattern matches for several mental models of disease, a differential diagnosis is developed. At this point, the diagnostic process shifts to slow system 2 analytical reasoning.
Based on their knowledge base, clinicians then use deductive reasoning: If this patient has disease A, what clinical history and physical examination findings might be expected, and does the patient have them? This process is repeated for each condition in the differential diagnosis and may be augmented by additional sources of information, such as diagnostic testing, further history gathering or physical examination, or referral or consultation.
The cognitive process of reassessing the probability assigned to each potential diagnosis involves inductive reasoning, 5 or going from observed signs and symptoms to the likelihood of each disease to determine which hypothesis is most likely Goodman, This can help refine and narrow the differential diagnosis.
Further information gathering activities or treatment could provide greater certainty regarding a working diagnosis or suggest that alternative diagnoses be considered. Throughout this process, clinicians need to communicate with patients about the working diagnosis and the degree of certainty involved. Task complexity and expertise affect which cognitive system is dominantly employed in the diagnostic process. System 1 processing is more likely to be used when patients present with typical signs and symptoms of disease.
However, system 2 processing is likely to intervene in situations marked by novelty and difficulty, when patients present with atypical signs and symptoms, or when clinicians lack expertise Croskerry, b ; Evans and Stanovich, 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 accumulates experience, the repetition of system 2 processing can expand pattern matching possibilities by building and storing in memory mental models for additional diseases that can be triggered by patient signs and symptoms. 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.
The way in which information is processed through system 1 and system 2 informs a clinician's subsequent diagnostic performance. 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 clinicians to compare their patients' ultimate diagnoses with the diagnoses that they provided to those patients.
Calibration enables clinicians to assess their diagnostic accuracy and improve their future performance. Calibration in the diagnostic process. Favorable or unfavorable information about a clinician's diagnostic performance provides good feedback and improves clinician calibration. When a patient's diagnostic outcome is unknown, it will be treated as favorable more 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. 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.
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. Bayes' theorem provides a framework for clinicians to revise the probability of disease, given disease prevalence, as well as the presence or absence of clinical findings or positive or negative test results Grimes and Schulz, ; Griner 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. The committee's recommendation on improving diagnostic competencies includes a focus on diagnostic test ordering and subsequent decision making, which relies on the principles of probabilistic reasoning.
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. The sheer number of potential diagnoses illustrates this complexity: There are thousands of diseases and related health conditions categorized in the National Library of Medicine's medical subjects headings system and around 13, in International Classification of Diseases , 9th Edition , with new conditions and diseases added every year Medicaid.
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. One of the ways that this is accomplished is through team-based care; by moving from individuals to teams of health care professionals, patients can benefit from a broader set of resources and expertise to support care Gittell et al. In addition, systematic reviews and clinical practice guidelines CPGs help synthesize available information in order to inform clinical practice decision making IOM, a , b.
Number of journal articles published on health care topics per year from to Publications have increased steadily over 40 years. 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. 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 guidelines Ferket et al. 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, Inductive reasoning involves probabilistic reasoning see the following section.
Turn recording back on. National Center for Biotechnology Information , U. Search term. Clinical History and Interview Acquiring a clinical history and interviewing a patient provides important information for determining a diagnosis and also establishes a solid foundation for the relationship between a clinician and the patient. Physical Exam The physical exam is a hands-on observational examination of the patient. Diagnostic Testing Over the past years, diagnostic testing has become a critical feature of standard medical practice Berger, ; European Society of Radiology, Medical Imaging Medical imaging plays a critical role in establishing the diagnoses for innumerable conditions and it is used routinely in nearly every branch of medicine.
Referral and Consultation Clinicians may refer to or consult with other clinicians formally or informally to seek additional expertise about a patient's health problem. Diagnostic Uncertainty One of the complexities in the diagnostic process is the inherent uncertainty in diagnosis. Kassirer concluded that: Absolute certainty in diagnosis is unattainable, no matter how much information we gather, how many observations we make, or how many tests we perform.
Time Of major importance in the diagnostic process is the element of time. Population Trends Population trends, such as the aging of the population, are adding significant complexity to the diagnostic process and require clinicians to consider such complicating factors in diagnosis as comorbidity, polypharmacy and attendant medication side effects, as well as disease and medication interactions IOM, , b.
Diverse Populations and Health Disparities Communicating with diverse populations can also contribute to the complexity of the diagnostic process. Mental Health Mental health diagnoses can be particularly challenging.
If the symptoms are highly typical e. The representativeness bias refers to the tendency to make decisions based on a typical case, even when this may lead to an incorrect judgment. The representativeness bias helps to explain why an incorrect diagnosis e. Base-rate neglect describes the tendency to ignore the prevalence of a disease in determining a diagnosis. For example, a clinician may think the diagnosis is acid reflux because it is a prevalent condition, even though it is actually an MI, which can present with similar symptoms e.
The overconfidence bias reflects the universal tendency to believe that we know more than we do. This bias encourages individuals to diagnose a disease based on incomplete information; too much faith is placed in one's opinion rather than on carefully gathering evidence. This bias is especially likely to develop if clinicians do not have feedback on their diagnostic performance.
Psych-out errors describe the increased susceptibility of people with mental illnesses to clinician biases and heuristics due to their mental health conditions. Patients with mental health issues may have new physical symptoms that are not considered seriously because their clinicians attribute them to their mental health issues. Patients with physical symptoms that mimic mental illnesses hypoxia, delirium, metabolic abnormalities, central nervous infections, and head injuries may also be susceptible to these errors.
Dual Process Theory and Diagnosis This section applies the dual process theory of clinical reasoning to the diagnostic process Croskerry, a , b ; Norman and Eva, ; Pelaccia et al.
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The now closed relay supplies voltage to the day time running lamps. If the headlamps are illuminated the DRL will not illuminate. Component Connector End Views. Exterior Lighting Systems Description and Operation. Control Module References for scan tool information.
Engine is running idle, supply sufficient light to the ambient light sensor to simulate daylight conditions. The daytime running lamps should be ON.
Perform the Diagnostic Repair Verification after completing the diagnostic procedure. Vauxhall Workshop Manuals. Ignition OFF, disconnect the appropriate daytime running lamp relay.
Ignition ON, verify that a test lamp does not illuminate between the control circuit terminal 87 and ground. Does not apply. RevivalFinds catotx Visit Store: RevivalFinds. Items On Sale. Postage and handling. This item can be sent to Germany , but the seller has not specified postage options. Contact the seller - opens in a new window or tab and request a postage method to your location.
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