scholarly journals Diagnostic reasoning in cardiovascular medicine

BMJ ◽  
2022 ◽  
pp. e064389
Author(s):  
John E Brush ◽  
Jonathan Sherbino ◽  
Geoffrey R Norman
Keyword(s):  
Cognitive Psychology ◽  
Medical Diagnosis ◽  
Diagnostic Reasoning ◽  
Diagnostic Process ◽  
Educational Strategies ◽  
Cardiovascular Medicine ◽  
Clinical Teachers ◽  
Base Rates ◽  
Novices And Experts ◽  
Intuitive Sense

ABSTRACT Research in cognitive psychology shows that expert clinicians make a medical diagnosis through a two step process of hypothesis generation and hypothesis testing. Experts generate a list of possible diagnoses quickly and intuitively, drawing on previous experience. Experts remember specific examples of various disease categories as exemplars, which enables rapid access to diagnostic possibilities and gives them an intuitive sense of the base rates of various diagnoses. After generating diagnostic hypotheses, clinicians then test the hypotheses and subjectively estimate the probability of each diagnostic possibility by using a heuristic called anchoring and adjusting. Although both novices and experts use this two step diagnostic process, experts distinguish themselves as better diagnosticians through their ability to mobilize experiential knowledge in a manner that is content specific. Experience is clearly the best teacher, but some educational strategies have been shown to modestly improve diagnostic accuracy. Increased knowledge about the cognitive psychology of the diagnostic process and the pitfalls inherent in the process may inform clinical teachers and help learners and clinicians to improve the accuracy of diagnostic reasoning. This article reviews the literature on the cognitive psychology of diagnostic reasoning in the context of cardiovascular disease.

Risk and Reason in Clinical Diagnosis

2019 ◽  
Author(s):  
Cym Anthony Ryle
Keyword(s):  
Medical Diagnosis ◽  
Process Model ◽  
Medical Training ◽  
Diagnostic Reasoning ◽  
Dual Process ◽  
Diagnostic Process ◽  
Near Misses ◽  
Diagnostic Thresholds ◽  
Enhance Patient Safety

This book provides, without the use of specialist language, a description of diagnostic reasoning and error and a discussion of steps that could improve diagnostic accuracy. Drawing on work in cognitive psychology, it presents the key characteristics of human reasoning. It notes that complex cognitive tasks such as medical diagnosis require a synergy of intuition and analytical thinking and introduces the concept of bias. The book considers the value of current classifications of disease, the meaning of diagnostic thresholds, and the potential for overdiagnosis. It examines the role of the patient-centred approach in this context. It develops a description of the diagnostic process, provides illustrative examples and metaphors, and refers to the dual-process model. It suggests that medical training does not consistently provide a coherent account of diagnostic thinking and the associated risks of error. It considers the role of probability in diagnostic reasoning, noting the contribution and the limitations of both informal and mathematical estimates. It refers to clear evidence that error in medical diagnosis is a prevalent and potent cause of harm and may result from systems factors or cognitive glitches such as bias and logical fallacy. It presents cases with commentaries, highlighting the cognitive processes in diagnostic successes, near misses, and disasters. It concludes with proposals for change, notably in institutional culture; in professional culture, education, and training; and in the structure of medical records. The book advocates the development and deployment of computerized diagnostic decision support. It argues that these changes could significantly enhance patient safety.

Becoming a teacher of clinical reasoning

Diagnosis ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 11-14 ◽  
Author(s):  
Robert L. Trowbridge ◽  
Andrew P.J. Olson
Keyword(s):  
Clinical Reasoning ◽  
Cognitive Biases ◽  
Diagnostic Reasoning ◽  
Dual Process ◽  
Diagnostic Process ◽  
Clinical Experiences ◽  
Context Specificity ◽  
Clinician Educators ◽  
Medical Educators

AbstractDiagnostic reasoning is one of the most challenging and rewarding aspects of clinical practice. As a result, facility in teaching diagnostic reasoning is a core necessity for all medical educators. Clinician educators’ limited understanding of the diagnostic process and how expertise is developed may result in lost opportunities in nurturing the diagnostic abilities of themselves and their learners. In this perspective, the authors describe their journeys as clinician educators searching for a coherent means of teaching diagnostic reasoning. They discuss the initial appeal and immediate applicability of dual process theory and cognitive biases to their own clinical experiences and those of their trainees, followed by the eventual and somewhat belated recognition of the importance of context specificity. They conclude that there are no quick fixes in guiding learners to expertise of diagnostic reasoning, but rather the development of these abilities is best viewed as a long, somewhat frustrating, but always interesting journey. The role of the teacher of clinical reasoning is to guide the learners on this journey, recognizing true mastery may not be attained, but should remain a goal for teacher and learner alike.

scholarly journals What does research on clinical reasoning have to say to clinical teachers?

2020 ◽  
Vol 30 (1) ◽  
pp. e37350
Author(s):  
Silvia Mamede
Keyword(s):  
Clinical Reasoning ◽  
Correct Diagnosis ◽  
Diagnostic Process ◽  
Instructional Approaches ◽  
Clinical Teachers ◽  
Reasoning Strategy ◽  
Therapeutic Decisions ◽  
Illness Scripts ◽  
Underlying Mechanisms ◽  
Clinical Problems

Clinical reasoning is a crucial determinant of physicians’ performance. It is key to arrive at a correct diagnosis, which substantially increases the chance of appropriate therapeutic decisions. Clinical teachers face the daily challenge of helping their students to develop clinical reasoning. To select appropriate teaching strategies, it may be useful to become acquainted with the results of the research on clinical reasoning that has been conducted over the last decades. This article synthesizes the findings of this research that help in particular to understand the cognitive processes involved in clinical reasoning, the trajectory that leads the student from novice to expert, and instructional approaches that have been shown to be useful to facilitating this trajectory. The focus of the article is the diagnostic process, because it is about it that most research has been conducted. This research indicates that there is not a particular reasoning strategy that is specific to expert physicians and could be taught to students. It is the availability of a large knowledge base organized in memory in illness scripts of different formats that explains the expert’s better performance. The more, the richer, and the more well-structured are the illness scripts a physician has stored in memory, the more he/she would be able to make accurate diagnoses. These scripts are formed gradually over the years of education. To help develop them, students should be exposed to a wide variety of clinical problems, with which they must interact actively. Instructional approaches that require students to systematically reflect on problems, analyzing differences and similarities between them, explaining underlying mechanisms, comparing and contrasting alternative diagnoses, have proved useful to help refine disease scripts. These approaches are valuable tools for teachers concerned with the development of their students clinical reasoning.

Diagnostic reasoning

2018 ◽  
Author(s):  
David Sprigings
Keyword(s):  
Cognitive Psychology ◽  
Clinical Problem ◽  
Computer Programs ◽  
Diagnostic Reasoning ◽  
Mental Process

Diagnostic reasoning is the mental process by which physicians turn information about the patient into the name of a disease. To do this, physicians must gather and evaluate evidence relevant to the clinical problem, and then choose a diagnosis or make a decision about management. As in detective work, with which diagnostic reasoning has many similarities, physicians have to reason from observed effects to their possible causes. This chapter explores the thinking behind diagnostic reasoning, drawing on insights from cognitive psychology, philosophy, and the design of computer programs. If physicians have a deeper understanding of the reasoning that underpins making a diagnosis, they may be more astute diagnosticians, and better able to teach the skill to novices. And, however physicians arrive at a diagnosis, they need to be able to articulate their reasoning to the patient and their colleagues.

The Diagnostic Process with Special Reference to Errors

1971 ◽  
Vol 10 (03) ◽  
pp. 176-188 ◽  
Author(s):  
J. GOOD ◽  
I. W. CARD
Keyword(s):  
Special Reference ◽  
Expected Utility ◽  
Medical Diagnosis ◽  
Information Transfer ◽  
Basic Assumption ◽  
Search Tree ◽  
Weight Of Evidence ◽  
Diagnostic Process ◽  
Large Tree

An analysis is made of the losses due to errors in the diagnostic process. The basic assumption is that the doctor should try to maximize expected utility, where the utility allows both for the health of the patient and for »costs« of various kinds. This assumption leads to the view that in general the doctor should make use of a diagnostic search tree. Owing to the difficulty of estimating utilities and of back-tracking in a large tree it is convenient for him to use substitutes for utility, called quasi-utilities, such as mean information transfer or expected weight of evidence. After listing a number of such quasi-utilities, the effect on their expectations due to error is considered. The losses can be larger than might have been supposed. Much of the analysis could also be applied to scientific problems other than to medical diagnosis.

The Cognitive Psychology of Diagnostic Errors

2015 ◽  
Author(s):  
Lauren E. Benishek ◽  
Sallie J. Weaver ◽  
David E. Newman-Toker
Keyword(s):  
Decision Making ◽  
Cognitive Psychology ◽  
Scientific Evidence ◽  
Characteristic Curve ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Diagnostic Reasoning ◽  
Diagnostic Decision ◽  
Context Specific ◽  
Diagnostic Decision Making

Health care involves complex decision making, often under uncertain, ambiguous, and time-sensitive conditions. Clinicians typically face the greatest uncertainty when making diagnostic decisions; common, undifferentiated symptoms paired with increasing prevalence of complex comorbidities, continuously and rapidly evolving scientific evidence, and often fragmented information systems are just a few of the hurdles clinicians must navigate as part of daily diagnostic decision making. In this review, the current state of the science concerning the cognitive psychology of diagnostic errors is discussed, including models of diagnostic reasoning, common errors: heuristics and biases, and practical implications and interventions. Figures show a conceptual model for diagnostic errors; diagnostic and therapeutic cycles; relationships among heuristics, biases, premature closure, and diagnostic errors; Reason’s (2000) Swiss cheese model; and tradeoffs versus improvements in diagnostic performance as illustrated by the receiver operating characteristic curve. Tables list important reasons for understanding the foundational cognitive models of diagnostic reasoning; a glossary of key diagnostic error–related definitions; three models of cognitive decision making; a summary of clinical reasoning models; steps of diagnostic decision making; examples of diagnostic errors resulting from representativeness, availability, and anchoring and adjustment; categories of countermeasures for error reduction interventions; examples of cognitively, systems-, and patient-focused countermeasures for selected biases; a summary of cognitively focused countermeasures to cognitive bias; key problem “classes” where problem- or context-specific solutions might be applied; types of system-focused countermeasures; and patient-focused countermeasures to avoid diagnostic error.   This review contains 5 highly rendered figures, 12 tables, and 120 references.

scholarly journals Karl Popper 120th anniversary; Some reflections on the decision-making process in medicine

2021 ◽  
Author(s):  
Elisha Krasin
Keyword(s):  
Decision Making ◽  
Medical Diagnosis ◽  
Scientific Method ◽  
Scientific Discovery ◽  
Diagnostic Process ◽  
Decision Making Process ◽  
Karl Popper ◽  
Relative Truth ◽  
Basic Concepts ◽  
120Th Anniversary

Rereading Popper’s “The Logic of scientific discovery”, at his 120th anniversary, brings some thoughts regarding the diagnostic process and decision making in medicine from the viewpoint of the classical scientific method. In recent years physicians are increasingly becoming technical experts who base their decision-making on uniform criteria, guidelines and classifications but unfortunately have moved away from understanding the basic concepts in the philosophy of science. This raises an ethically and philosophically important issue; what does a medical diagnosis mean? Is this an absolute or a relative truth? The implications of this question are enormous in terms of prognosis and treatment. Both patients and physicians should be educated about the nature of the diagnostic process.

Learner Analytics Applied to the Clinical Diagnostic Reasoning with Hepius Cognitive Tutor and Simulator (Preprint)

2020 ◽  
Author(s):  
Raffaello Furlan ◽  
Mauro Gatti ◽  
Roberto Menè ◽  
Dana Shiffer ◽  
Chiara Marchiori ◽  
...  
Keyword(s):  
Language Processing ◽  
Medical Knowledge ◽  
Final Diagnosis ◽  
Relevant Information ◽  
Diagnostic Reasoning ◽  
Diagnostic Process ◽  
Patient Simulator ◽  
Cognitive Tutor ◽  
Clinical Diagnostic ◽  
Maximal Sensitivity

BACKGROUND Virtual Patient Simulator is a tool that may generate a multi-dimensional representation of the student’s medical knowledge by analyzing the recordings of the user’s actions during a clinical simulation. Adequate metrics may provide teachers with valuable learning information. OBJECTIVE To describe the analytic metrics we used to analyze the clinical diagnostic reasoning of medical students obtained by a novel Cognitive Tutor and Simulator named Hepius embedding Natural Language Processing (NLP) techniques. METHODS Two clinical case simulations (Tests) were created to tune our metrics. During each simulation, students’ actions were logged into the program data base for off-line analysis. Twenty-six students, attending the 5th year of the School of Medicine at Humanitas University, underwent Test 1 (April 12th 2019) which simulated a patient suffering from dyspnea. Test 2 (May 21st 2019) dealt with abdominal pain and was attended by 36 students. Overall students’ performance was split into 7 issues: 1) the identification of relevant information in the given clinical scenario (SC); 2) history taking (AN); 3) physical exam (PE); 4) medical tests (MT) ordering; 5) diagnostic hypotheses (HY) setting; 6) binary analysis fulfillment (BA); 7) final diagnosis (RS) setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic (F1), thereby obtaining a single score (1= maximal sensitivity and precision) evaluating the student’s performances. The seven F1-metric scores were further combined to obtained a convenient index assessing the student’s overall performances.The seven metrics were further grouped to reflect the student’s capability to collect (SC, AN, PE and MT) and to analyze (HY, BA and RS) information. A methodological score was computed on the basis of the discordance between the diagnostic pathway followed by the student and a reference one, previously defined by the teacher. RESULTS Mean overall scores were consistent between the two tests (0.6.±0.05 for Test 1 and 0.5±0.05 for Test 2). For each student, overall performance was achieved by a different contribution in collecting and analyzing information. Methodological scores highlighted some discordance between the reference diagnostic pattern previously set by the teachear and the one pursued by the student. CONCLUSIONS Different components of the student’s diagnostic process may be disentangled and quantified by appropriate metrics applied on students’ actions recorded while addressing a virtual case. Such an approach may help teachers in giving students individualized feedbacks aimed at filling up knowledge drawbacks and methodological inconsistencies.

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