Gut Feelings as a Third Track in General Practitioners’ Diagnostic Reasoning

BACKGROUND: General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. OBJECTIVE: This paper explains how gut feelings arise and function in GPs' diagnostic reasoning. APPROACH: The paper reviews literature from medical, psychological and neuroscientific perspectives. CONCLUSIONS: Gut feelings in general practice are based on the interaction between patient information and a GP's knowledge and experience. This is visualized in a knowledge-based model of GPs' diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician's knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed

Incorporating clinical guidelines through clinician decision-making

<p>Abstract</p> <p>Background</p> <p>It is generally acknowledged that a disparity between knowledge and its implementation is adversely affecting quality of care. An example commonly cited is the failure of clinicians to follow clinical guidelines. A guiding assumption of this view is that adherence should be gauged by a standard of conformance. At least some guideline developers dispute this assumption and claim that their efforts are intended to inform and assist clinical practice, not to function as standards of performance. However, their ability to assist and inform will remain limited until an alternative to the conformance criterion is proposed that gauges how evidence-based guidelines are incorporated into clinical decisions.</p> <p>Methods</p> <p>The proposed investigation has two specific aims to identify the processes that affect decisions about incorporating clinical guidelines, and then to develop ad test a strategy that promotes the utilization of evidence-based practices. This paper focuses on the first aim. It presents the rationale, introduces the clinical paradigm of treatment-resistant schizophrenia, and discusses an exemplar of clinician non-conformance to a clinical guideline. A modification of the original study is proposed that targets psychiatric trainees and draws on a cognitively rich theory of decision-making to formulate hypotheses about how the guideline is incorporated into treatment decisions. Twenty volunteer subjects recruited from an accredited psychiatry training program will respond to sixty-four vignettes that represent a fully crossed 2 × 2 × 2 × 4 within-subjects design. The variables consist of criteria contained in the clinical guideline and other relevant factors. Subjects will also respond to a subset of eight vignettes that assesses their overall impression of the guideline. Generalization estimating equation models will be used to test the study's principal hypothesis and perform secondary analyses.</p> <p>Implications</p> <p>The original design of phase two of the proposed investigation will be changed in recognition of newly published literature on the relative effectiveness of treatments for schizophrenia. It is suggested that this literature supports the notion that guidelines serve a valuable function as decision tools, and substantiates the importance of decision-making as the means by which general principles are incorporated into clinical practice.</p

Outcome Feedback Effects on Risk Propensity in an MCPLP Task

In this experimental analysis, the effects of outcome feedback on risk propensity were assessed within the multiple-cue-probability-learning-paradigm (MCPLP). The individual decision maker in this task received outcome feedback on a decision-by-decision basis. It was hypothesized that information on his/her success or lack of success (outcome feedback) on each decision would influence the decision to risk (commit) resources. Hierarchical regression results revealed that after all other performance effects had been partialled out, current outcome feedback explained much of the commitment decision.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Active symbols and internal models: Towards a cognitive connectionism

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist models are fundamentally associationist but that this is appropriate for building models of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45877/1/146_2005_Article_BF01889764.pd