Efficient Selection: Diagnostic methods of personnel assessment and intuition

Broadly construed through a pattern-recognition framework, nonanalytical models attempt to understand clinical reasoning through human categorization and classification practices. These models suggest that clinicians make diagnoses and choose treatments by matching presenting patients to their mental models of diseases or information about diseases that is stored in memory.

Although the nature of these mental models remain under debate, most assume that they are either exemplars specific patients seen previously and stored in memory as concrete examples or prototypes an abstract disease conceptualization that weighs disease features according to their frequency Bordage and Zacks, ; Norman, ; Rosch and Mervis, ; Schmidt et al. Expert pattern matching by experienced clinicians may involve illness scripts, in which elaborated disease knowledge includes enabling conditions or risk factors e.

After encountering a patient, a clinician may activate a single illness script or multiple scripts. Illness scripts differ from exemplars and prototypes by having more extensive knowledge stored for each disease. As the diagnostic process evolves, the clinician matches the activated scripts against the presenting signs and symptoms, with the best matching script offered as the most likely diagnosis. While exemplars, prototypes, and illness scripts are assumed to encode different types of information about disease conditions—that is, actual instances versus typical presentation versus multidimensional information—pattern recognition models assume them to play the same role in diagnosis.

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 myth of the effectiveness of intuition in hiring and evaluating personnel - EPSI

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. 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.

When patients present, clinicians gather information and compare that information with their knowledge about various diseases. 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 of illness are recognized, system 1 processes are used. If they are not recognized, system 2 processes are used.

Repetition of data to system 2 processes may eventually be recognized as a new pattern and subsequently processed through system 1. Multiple arrows stem from system 1 processes to depict intuitive, fast, parallel decision making.

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Because system 2 processes are slow and serial, only one arrow stems from system 2 processes, depicting analytical decision making. The executive override pathway shows that system 2 surveillance has the potential to overrule system 1 decision making. The irrational override pathway shows the capability for system 1 processes to overrule system 2 analytical decision making. The toggle arrow T illustrates how the decision maker may employ both fast system 1 and slow system 2 processes throughout the decision-making process.

Origins of bias and theory of debiasing. 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.

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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.

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. Adapted with permission from The feedback sanction. Academic Emergency Medicine 7 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.

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

The sheer number of potential diagnoses illustrates this complexity: 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. 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. 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.

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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.

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Objectivity Although major differences of opinion persist concerning the nature, operationalization and objectives of talent management, nearly all authors agree that the selection and assessment of personnel is a decisive factor for success. Use easily-understood utility estimates and causal chain analysis. Utility formulas attempt to estimate the IBU dollar value of the gain in employee performance that results from using selection procedures that correlate with performance, or HR interventions that improve performance, such as performance appraisal and training O programs.

Presumably, such information should be convincing to managers because they take into account the dollar cost and dollar benefit of the HR practice and allow them, with relatively easy manipulation, to determine RO how much money can be gained or saved by implementing certain procedures. Utility formulas include as their components selection ratios, the dollar estimate of a standard deviation difference in P job performance, and the expected standardized difference in performance DIS between a group subjected to the HR practices and a no-treatment control group.

Not surprisingly, managerial reactions to these estimates have not been very favorable. In fact, Whyte and Latham found that adding utility analysis estimates using these types of formulas led to lower T acceptance of the HR practice. Causal chain analysis demonstrates the linkage of the implementation of HR practices to increases in profits through mediating variables such employee attitudes and customer satisfaction of course, these paths to profit are also likely to be indirect, and mediated by variables such as effort and work withdrawal for attitudes, and repeat business and positive word-of-mouth advertising for TF customer satisfaction.

The presentation to decision makers is similar to figures that researchers use to depict path models, except that the paths are depicted as percentages in the dependent variable that result from a set increase in the independent variable, as established by prior research. Such results are promising for the future of convincing practitioners of the monetary value of scientific selection.

RO 5 Present information to others in terms of narratives. As noted, managers 6 are more likely to rely on personnel selection aids—and therefore make 7 8 9 TR better decisions—if they can see the value of the method. One effective way in which a prediction method can gain acceptance is through the use of narratives Kuncel, People seldom use raw evidence e. In employee selection, for example, narratives could 4 allow individuals to imagine situations in which a candidate may behave 5 in certain ways. For example, one could present the information of Candidate A in a table, showing a score at the 90th percentile on a general mental ability test.

Alternatively, this information could be presented in a narrative format: Underwood has high general P mental abilities, and thus she is likely to process information in an efficient DIS way. This may help her prepare accurate financial reports. Decisions and predictions are more likely to be accepted if there is input from human OR judgment. Furthermore, people are unwilling to use decision aids when they perceive their own autonomy to be violated Goldsmith, This suggests that TF people may react unfavorably if told to pick the option that the statistical method recommends. For example, decision makers may be advised on how TIO 2 to combine the information they face, as suggested by a weighed additive 3 model.

Dalal and Bonaccio found that decision support was well perceived 4 in terms of both accuracy and autonomy , Study 2. There may be cases in which FS 6 statistical methods such as regression analysis are so strongly opposed that 7 they cannot be used at all. Even so, fortunately, there is substantial research 8 on the best predictors of job performance e. IBU 9 We recommend using a small set of valid predictors or cues such as scores from general mental ability tests and assessment centers. Using too much 1 information, especially when information is vivid and irrelevant e.

RO 5 Ask decision makers to make precise estimates, and provide unambiguous 6 feedback. Together with the decision of hiring or rejecting a candidate, 7 8 9 TR decision makers should make precise, numerical predictions. Note that there are two important issues DIS 2 that make these estimates precise: Of course, this only applies to accepted candidates, 1S 8 as it is impossible to receive feedback from rejected candidates.

Using 9 precise estimates makes individuals less likely to be vulnerable to hindsight bias Fischhoff, This allows decision makers to realize the limitations OR 1 of their predictive skills, and to learn from feedback Arkes, As a 2 result, they may be more willing to rely on selection decision aids, which 3 should improve their hiring decisions. This works as a debiasing strategy, 8 because the individual focuses on contrary evidence that would not 9 otherwise be considered Larrick, Thinking and listing these reasons can help make more accurate judgments.

Kausel Summary TIO In this section, we have discussed a number of ways to try to convince managers of the value of evidence-based decision making for employee selection, including simplifying the presentation of validity and utility information. We have also discussed ways to improve decision making FS when managers are resistant to using mechanical predictor information and mechanical combination of predictors.

We do need to sound a note of caution here. Many of the suggestions we have made are based on what IBU is known from laboratory-based decision-making research. Thus, whether they will be effective in the field, in terms of improving decision making O and improving user reactions, is an empirical question. We urge researchers to continue to experiment with different methods for improving the quality of selection decisions. Given the close P relationship between these two processes, we are hopeful that the DIS information we have presented leads to more research at the intersection of these topics and creative ideas about how to improve selection decision making in organizations.

As a result of the brevity of the chapter, we want T to leave with the warning that there are many influences on decision making not discussed here. If the reader is so inclined, we encourage a closer look at other works in decision making that will provide more complete 1S information e. We want to leave the reader with two thoughts. One is directed toward practitioners and managers, and the other is directed toward researchers. Many practitioners believe in the power of their intuitive judgments because they do not dare to act against their intuition. As a result, they are never able to test the hypothesis that their intuitions might be wrong.

Managers should test their intuition every so often, TF by ignoring their gut feelings and choosing on the basis of objective information. They may well find that acting TIO 2 against intuition and relying on validated methods is a pretty good method 3 after all.

Many of the FS 6 assumptions we have made here, in terms of how often intuition is used 7 or our ideas about exactly why managers cling to intuition, have not been 8 fully substantiated empirically. We encourage researchers to collect IBU 9 information from managers on how they make selection decisions, as well as the reasons for the methods they use. Qualitative research— 1 paradoxically, in the form of interviews—can be especially helpful in this 2 4 O regard.

There are certainly other reasons beyond complexity that may be causing negative reactions to utility estimates. First, utility estimates might seem absurdly high in terms of raw dollars gained. Second, utility estimates are often computed without input from P 1 other organizational decision makers. These issues are also important to 3 consider when using causal chain analysis.

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