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Medical Problem Solving: An Analysis of Clinical Reasoning

ISBN-10 0674561252
ISBN-13 978-0674561250
Publisher Harvard University Press
Publication date April 20, 1978
Language English
Dimensions 6.75 x 1 x 9.75 inches
Print length 352 pages
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Publisher ‏ : ‎ Harvard University Press (April 20, 1978)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 352 pages
ISBN-10 ‏ : ‎ 0674561252
ISBN-13 ‏ : ‎ 978-0674561250
Item Weight ‏ : ‎ 1.57 pounds
Dimensions ‏ : ‎ 6.75 x 1 x 9.75 inches
#2,541 in General (Books)
#33,443 in Professional
#205,853 in Medical Books (Books)

About the authors

Arthur s. elstein.

Discover more of the author’s books, see similar authors, read author blogs and more

Sarah A. Sprafka

Lee S. Shulman

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Medical problem solving: an analysis of clinical reasoning

... Medical problem solving: An analysis of clinical reasoning. Post a Comment. CONTRIBUTORS: ... VOLUME/EDITION: PAGES (INTRO/BODY): xvi, 330 p. SUBJECT(S): Medical logic; Medicine; Problem solving; Diagnosis; Decision making. DISCIPLINE: No discipline assigned. ...

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What constitutes clinical reasoning is a disputed subject regarding the processes underlying accurate diagnosis, the importance of patient‐specific versus population‐based data, and the relation between virtue and expertise in clinical practice. In this paper, I present a model of clinical reasoning that identifies and integrates the processes of diagnosis, prognosis, and therapeutic decision making. The model is based on the generalized empirical method of Bernard Lonergan, which approaches inquiry with equal attention to the subject who investigates and the object under investigation. After identifying the structured operations of knowing and doing and relating these to a self‐correcting cycle of learning, I correlate levels of inquiry regarding what‐is‐going‐on and what‐to‐do to the practical and theoretical elements of clinical reasoning. I conclude that this model provides a methodical way to study questions regarding the operations of clinical reasoning as well as what constitute significant clinical data, clinical expertise, and virtuous health care practice. [Link to full text (read only): https://rdcu.be/TQW1]

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Clinical diagnostic medicine is an experimental science based on observation, hypothesis making, and testing. It is an use dynamic process that involves observation and summary, diagnostic conjectures, testing, review, observation and summary, new or revised conjectures, i.e. it is an iterative process. It can then be said that diagnostic hypotheses are also 'observation-laden'. My aim is to enlarge on the strategies of medical diagnosis as these are meshed in training and clinical experience—that is, to describe the patterns of reasoning used by experienced clinicians under different diagnostic circumstances and how these patterns of inquiry allow further insight into the evaluation and treatment of patients. I do not aim to present a theory and illustrate it with examples; I wish rather am to let a realistic example, similar to actual clinical scenarios, direct the exposition. To this end, I introduce an account of medical diagnosis—briefly comparing and contrasting it to other accounts—in order to focus on discussing the process of diagnosis through a detailed clinical case.

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Introduction: Developing the skills of clinical reasoning is a tedious process, especially for the novice learner and requires practice. The clinical reasoning skill is a cognitive process of systematic clinical decision making needed to reduce diagnostic errors. A clinical reasoning tool for diagnosis using the Bloom's tax-onomy of critical thinking has been in use in the Paediatrics Department of the University of Port Harcourt. However, little is known about the difficulties encountered by trainees (medical students and early career doctors) while using the tool during daily clinical clerkship. We aimed to determine aspects of the clinical reasoning process trainees find difficult and ways to make this easier. Methods: A well-structured, pre-tested questionnaire was administered to 67 medical undergraduates and 99 early career medical doctors which assessed responses to the definition of clinical reasoning , matching Bloom's taxon-omy hierarchy with steps in clinical reasoning, functional and structural abnormalities and attitudes towards the use of the clinical reasoning tool. The Likert 5 point scale tool was used to assess attitudes and practice difficulties during the use of the tool. The differences in responses were tested for significance using Stu-dent's T test, and Chi squared test, with p values <0.05 as significant. Results: Of the 166 respondents analysed, 103 (62%) got the correct definition of clinical reasoning with early career doctors having a higher proportion of correct respondents , χ2 = 4.59, p = 0.032. Specific areas of difficulties identified were with making clinical diagnosis in 50 (30.1%) and patho-logic diagnosis (es) in 38 (22.9%). Ninety-nine (59.6%) responded that clinical reasoning was time consuming and 42 (25.3%) thought it was difficult to practice in a busy clinic. One hundred and six (64.1%) respondents suggested a view of basic clinical studies before starting clinical practice in order to improve clinical reasoning. Conclusion/Recommendation: Making clinical diagnosis is difficult for the clinical trainee while using the clinical reasoning tool, therefore the clinical teacher should help trainees move from one cognitive level to the next until the trainee can create logical conclusions from information gathered following clerking.

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Medical Problem Solving: An Analysis of Clinical Reasoning.

Elstein AS, ed. Cambridge, MA: Harvard University Press; 1978. ISBN: 9780674561250.

Clinical reasoning lies at the heart of formulating diagnoses and selecting treatments. The results of these medical decisions determine a substantial portion of the dollars spent on health care. Considering the fundamental importance of clinical reasoning, the topic has received surprisingly little systematic study. Even with the widespread interest in medical error and patient safety in recent years, diagnostic errors and other errors in clinical reasoning have received little attention. This classic collection of empiric studies on clinical reasoning in action thus remains highly relevant more than 25 years after its original publication. One finding of particular relevance for those interested in patient safety and quality improvement is that competence may be problem specific; thus, there is no generic approach to clinical problem solving that, when followed, ensures excellent, or even competent, performance in a variety of domains within a field. The authors also provide an excellent overview of theoretic models relevant to the study of clinical reasoning.

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Medical Problem Solving

An analysis of clinical reasoning.

Arthur S. Elstein , Lee S. Shulman and Sarah A. Sprafka
In collaboration with: Linda Allal , Michael Gordon , Jason Hilliard , Norman Kagan , Michael J. Loupe and Ronald D. Jordan
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Language: English
Publisher: Harvard University Press
Copyright year: 1978
Edition: Reprint 2014
Audience: Professional and scholarly;
Front matter: 16
Main content: 330
Other: 15 illustrations and tables
Keywords: Medical logic. ; Medicine -- Decision making. ; Diagnosis. ; Problem Solving.
Published: October 1, 2013
ISBN: 9780674189089
Published: February 5, 1978
ISBN: 9780674189072

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Medical Problem Solving: An Analysis of Clinical Reasoning Hardcover – 1 July 1978

Print length 352 pages
Language English
Publisher Harvard University Press
Publication date 1 July 1978
Dimensions 17.15 x 2.54 x 24.77 cm
ISBN-10 0674561252
ISBN-13 978-0674561250
See all details

Product details

Publisher ‏ : ‎ Harvard University Press (1 July 1978)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 352 pages
ISBN-10 ‏ : ‎ 0674561252
ISBN-13 ‏ : ‎ 978-0674561250
Dimensions ‏ : ‎ 17.15 x 2.54 x 24.77 cm
26,585 in General Medical Issues Guides

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Sarah a. sprafka.

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Arthur S. Elstein

Lee S. Shulman

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Clinical Problem Solving and Decision Psychology: Comment on "The Epistemology of Clinical Reasoning"

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Clinical problem...

Clinical problem solving and diagnostic decision making: selective review of the cognitive literature

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This article has a correction. Please see:

Clinical problem solving and diagnostic decision making: selective review of the cognitive literature - November 02, 2006
Arthur S Elstein , professor ( aelstein{at}uic.edu ) ,
Alan Schwarz , assistant professor of clinical decision making.
Department of Medical Education, University of Illinois College of Medicine, Chicago, IL 60612-7309, USA
Correspondence to: A S Elstein

This is the fourth in a series of five articles

This article reviews our current understanding of the cognitive processes involved in diagnostic reasoning in clinical medicine. It describes and analyses the psychological processes employed in identifying and solving diagnostic problems and reviews errors and pitfalls in diagnostic reasoning in the light of two particularly influential approaches: problem solving 1 , 2 , 3 and decision making. 4 , 5 , 6 , 7 , 8 Problem solving research was initially aimed at describing reasoning by expert physicians, to improve instruction of medical students and house officers. Psychological decision research has been influenced from the start by statistical models of reasoning under uncertainty, and has concentrated on identifying departures from these standards.

Summary points

Problem solving and decision making are two paradigms for psychological research on clinical reasoning, each with its own assumptions and methods

The choice of strategy for diagnostic problem solving depends on the perceived difficulty of the case and on knowledge of content as well as strategy

Final conclusions should depend both on prior belief and strength of the evidence

Conclusions reached by Bayes's theorem and clinical intuition may conflict

Because of cognitive limitations, systematic biases and errors result from employing simpler rather than more complex cognitive strategies

Evidence based medicine applies decision theory to clinical diagnosis

Problem solving

Diagnosis as selecting a hypothesis.

The earliest psychological formulation viewed diagnostic reasoning as a process of testing hypotheses. Solutions to difficult diagnostic problems were found by generating a limited number of hypotheses early in the diagnostic process and using them to guide subsequent collection of data. 1 Each hypothesis can be used to predict what additional findings ought to be present if it were true, and the diagnostic process is a guided search for these findings. Experienced physicians form hypotheses and their diagnostic plan rapidly, and the quality of their hypotheses is higher than that of novices. Novices struggle to develop a plan and some have difficulty moving beyond collection of data to considering possibilities.

It is possible to collect data thoroughly but nevertheless to ignore, to misunderstand, or to misinterpret some findings, but also possible for a clinician to be too economical in collecting data and yet to interpret accurately what is available. Accuracy and thoroughness are analytically separable.

Pattern recognition or categorisation

Expertise in problem solving varies greatly between individual clinicians and is highly dependent on the clinician's mastery of the particular domain. 9 This finding challenges the hypothetico-deductive model of clinical reasoning, since both successful and unsuccessful diagnosticians use hypothesis testing. It appears that diagnostic accuracy does not depend as much on strategy as on mastery of content. Further, the clinical reasoning of experts in familiar situations frequently does not involve explicit testing of hypotheses. 3 10 , 11 , 12 Their speed, efficiency, and accuracy suggest that they may not even use the same reasoning processes as novices. 11 It is likely that experienced physicians use a hypothetico-deductive strategy only with difficult cases and that clinical reasoning is more a matter of pattern recognition or direct automatic retrieval. What are the patterns? What is retrieved? These questions signal a shift from the study of judgment to the study of the organisation and retrieval of memories.

Problem solving strategies

Hypothesis testing

Pattern recognition (categorisation)

By specific instances

By general prototypes

Viewing the process of diagnosis assigning a case to a category brings some other issues into clearer view. How is a new case categorised? Two competing answers to this question have been put forward and research evidence supports both. Category assignment can be based on matching the case to a specific instance (“instance based” or “exemplar based” recognition) or to a more abstract prototype. In the former, a new case is categorised by its resemblance to memories of instances previously seen. 3 11 This model is supported by the fact that clinical diagnosis is strongly affected by context—for example, the location of a skin rash on the body—even when the context ought to be irrelevant. 12

The prototype model holds that clinical experience facilitates the construction of mental models, abstractions, or prototypes. 2 13 Several characteristics of experts support this view—for instance, they can better identify the additional findings needed to complete a clinical picture and relate the findings to an overall concept of the case. These features suggest that better diagnosticians have constructed more diversified and abstract sets of semantic relations, a network of links between clinical features and diagnostic categories. 14

The controversy about the methods used in diagnostic reasoning can be resolved by recognising that clinicians approach problems flexibly; the method they select depends upon the perceived characteristics of the problem. Easy cases can be solved by pattern recognition: difficult cases need systematic generation and testing of hypotheses. Whether a diagnostic problem is easy or difficult is a function of the knowledge and experience of the clinician.

The strategies reviewed are neither proof against error nor always consistent with statistical rules of inference. Errors that can occur in difficult cases in internal medicine include failure to generate the correct hypothesis; misperception or misreading the evidence, especially visual cues; and misinterpretations of the evidence. 15 16 Many diagnostic problems are so complex that the correct solution is not contained in the initial set of hypotheses. Restructuring and reformulating should occur as data are obtained and the clinical picture evolves. However, a clinician may quickly become psychologically committed to a particular hypothesis, making it more difficult to restructure the problem.

Decision making

Diagnosis as opinion revision.

From the point of view of decision theory, reaching a diagnosis means updating opinion with imperfect information (the clinical evidence). 8 17 The standard rule for this task is Bayes's theorem. The pretest probability is either the known prevalence of the disease or the clinician's subjective impression of the probability of disease before new information is acquired. The post-test probability, the probability of disease given new information, is a function of two variables, pretest probability and the strength of the evidence, measured by a “likelihood ratio.”

Bayes's theorem tells us how we should reason, but it does not claim to describe how opinions are revised. In our experience, clinicians trained in methods of evidence based medicine are more likely than untrained clinicians to use a Bayesian approach to interpreting findings. 18 Nevertheless, probably only a minority of clinicians use it in daily practice and informal methods of opinion revision still predominate. Bayes's theorem directs attention to two major classes of errors in clinical reasoning: in the assessment of either pretest probability or the strength of the evidence. The psychological study of diagnostic reasoning from this viewpoint has focused on errors in both components, and on the simplifying rules or heuristics that replace more complex procedures. Consequently, this approach has become widely known as “heuristics and biases.” 4 19

Errors in estimation of probability

Availability —People are apt to overestimate the frequency of vivid or easily recalled events and to underestimate the frequency of events that are either very ordinary or difficult to recall. Diseases or injuries that receive considerable media attention are often thought of as occurring more commonly than they actually do. This psychological principle is exemplified clinically in the overemphasis of rare conditions, because unusual cases are more memorable than routine problems.

Representativeness —Representativeness refers to estimating the probability of disease by judging how similar a case is to a diagnostic category or prototype. It can lead to overestimation of probability either by causing confusion of post-test probability with test sensitivity or by leading to neglect of base rates and implicitly considering all hypotheses equally likely. This is an error, because if a case resembles disease A and disease B equally, and A is much more common than B, then the case is more likely to be an instance of A. Representativeness is associated with the “conjunction fallacy”—incorrectly concluding that the probability of a joint event (such as the combination of findings to form a typical clinical picture) is greater than the probability of any one of these events alone.

Heuristics and biases

Availability

Representativeness

Probability transformations

Effect of description detail

Conservatism

Anchoring and adjustment

Order effects

Decision theory assumes that in psychological processing of probabilities, they are not transformed from the ordinary probability scale. Prospect theory was formulated as a descriptive account of choices involving gambling on two outcomes, 20 and cumulative prospect theory extends the theory to cases with multiple outcomes. 21 Both prospect theory and cumulative prospect theory propose that, in decision making, small probabilities are overweighted and large probabilities underweighted, contrary to the assumption of standard decision theory. This “compression” of the probability scale explains why the difference between 99% and 100% is psychologically much greater than the difference between, say, 60% and 61%. 22

Support theory

Support theory proposes that the subjective probability of an event is inappropriately influenced by how detailed the description is. More explicit descriptions yield higher probability estimates than compact, condensed descriptions, even when the two refer to exactly the same events. Clinically, support theory predicts that a longer, more detailed case description will be assigned a higher subjective probability of the index disease than a brief abstract of the same case, even if they contain the same information about that disease. Thus, subjective assessments of events, while often necessary in clinical practice, can be affected by factors unrelated to true prevalence. 23

Errors in revision of probability

In clinical case discussions, data are presented sequentially, and diagnostic probabilities are not revised as much as is implied by Bayes's theorem 8 ; this phenomenon is called conservatism. One explanation is that diagnostic opinions are revised up or down from an initial anchor, which is either given in the problem or subjectively formed. Final opinions are sensitive to the starting point (the “anchor”), and the shift (“adjustment”) from it is typically insufficient. 4 Both biases will lead to collecting more information than is necessary to reach a desired level of diagnostic certainty.

It is difficult for everyday judgment to keep separate accounts of the probability of a disease and the benefits that accrue from detecting it. Probability revision errors that are systematically linked to the perceived cost of mistakes show the difficulties experienced in separating assessments of probability from values, as required by standard decision theory. There is a tendency to overestimate the probability of more serious but treatable diseases, because a clinician would hate to miss one. 24

Bayes's theorem implies that clinicians given identical information should reach the same diagnostic opinion, regardless of the order in which information is presented. However, final opinions are also affected by the order of presentation of information. Information presented later in a case is given more weight than information presented earlier. 25

Other errors identified in data interpretation include simplifying a diagnostic problem by interpreting findings as consistent with a single hypothesis, forgetting facts inconsistent with a favoured hypothesis, overemphasising positive findings, and discounting negative findings. From a Bayesian standpoint, these are all errors in assessing the diagnostic value of clinical evidence—that is, errors in implicit likelihood ratios.

Educational implications

Two recent innovations in medical education, problem based learning and evidence based medicine, are consistent with the educational implications of this research. Problem based learning can be understood as an effort to introduce the formulation and testing of clinical hypotheses into the preclinical curriculum. 26 The theory of cognition and instruction underlying this reform is that since experienced physicians use this strategy with difficult problems, and since practically any clinical situation selected for instructional purposes will be difficult for students, it makes sense to provide opportunities for students to practise problem solving with cases graded in difficulty. The finding of case specificity showed the limits of teaching a general problem solving strategy. Expertise in problem solving can be separated from content analytically, but not in practice. This realisation shifted the emphasis towards helping students acquire a functional organisation of content with clinically usable schemas. This goal became the new rationale for problem based learning. 27

Evidence based medicine is the most recent, and by most standards the most successful, effort to date to apply statistical decision theory in clinical medicine. 18 It teaches Bayes's theorem, and residents and medical students quickly learn how to interpret diagnostic studies and how to use a computer based nomogram to compute post-test probabilities and to understand the output. 28

We have selectively reviewed 30 years of psychological research on clinical diagnostic reasoning. The problem solving approach has focused on diagnosis as hypothesis testing, pattern matching, or categorisation. The errors in reasoning identified from this perspective include failure to generate the correct hypothesis; misperceiving or misreading the evidence, especially visual cues; and misinterpreting the evidence. The decision making approach views diagnosis as opinion revision with imperfect information. Heuristics and biases in estimation and revision of probability have been the subject of intense scrutiny within this research tradition. Both research paradigms understand judgment errors as a natural consequence of limitations in our cognitive capacities and of the human tendency to adopt short cuts in reasoning.

Both approaches have focused more on the mistakes made by both experts and novices than on what they get right, possibly leading to overestimation of the frequency of the mistakes catalogued in this article. The reason for this focus seems clear enough: from the standpoint of basic research, errors tell us a great deal about fundamental cognitive processes, just as optical illusions teach us about the functioning of the visual system. From the educational standpoint, clinical instruction and training should focus more on what needs improvement than on what learners do correctly; to improve performance requires identifying errors. But, in conclusion, we emphasise, firstly, that the prevalence of these errors has not been established; secondly, we believe that expert clinical reasoning is very likely to be right in the majority of cases; and, thirdly, despite the expansion of statistically grounded decision supports, expert judgment will still be needed to apply general principles to specific cases.

Series editor J A Knottnerus

Preparation of this review was supported in part by grant RO1 LM5630 from the National Library of Medicine.

Competing interests None declared.

“The Evidence Base of Clinical Diagnosis,” edited by J A Knottnerus, can be purchased through the BMJ Bookshop ( http://www.bmjbookshop.com/ )

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Schwartz A ,
Chapman GB ,
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Coblentz CL ,
Lemieux M ,
Kassirer JP ,
Kopelman RI
Sackett DL ,
Haynes RB ,
Guyatt GH ,
Richardson WS ,
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ten Cate O, Custers EJFM, Durning SJ, editors. Principles and Practice of Case-based Clinical Reasoning Education: A Method for Preclinical Students [Internet]. Cham (CH): Springer; 2018. doi: 10.1007/978-3-319-64828-6_3

Cover of Principles and Practice of Case-based Clinical Reasoning Education

Principles and Practice of Case-based Clinical Reasoning Education: A Method for Preclinical Students [Internet].

Chapter 3 understanding clinical reasoning from multiple perspectives: a conceptual and theoretical overview.

Olle ten Cate and Steven J. Durning .

Affiliations

Published online: November 7, 2017.

Rather than a historical overview as in Chap. 2, this chapter provides the reader with insight into the various approaches that have been used to understand clinical reasoning. We review concepts and major scholars who have been involved in such investigations. Cognitive psychologists Newel and Simon theorized about problem-solving skills and artificial intelligence and initiated the use of computers as metaphors of thinking. Elstein and colleagues found that there is no such thing as a general problem-solving skill, independent of medical knowledge, and thus clinical reasoning is case specific. Reasoning then became analyzed in approaches, including forward reasoning from data to diagnosis; hypothetico-deductive reasoning with backward nature, from hypothesis to diagnosis; and abductive reasoning to understand early hypothesis generation that is so characteristic in clinical reasoning, elaborated by Patel and colleagues. Bordage introduced prototypes to characterize how physicians may remember illness presentations and semantic qualifiers to denote the shortened conceptual language and labels physicians use to store medical information systematically in memory. Illness scripts represent how encounters with diseases are remembered by physicians and were introduced by Feltovich and Barrows. Schmidt and Boshuizen elaborated the concept further and propose encapsulation of knowledge as a hypothetical process that happens when physicians regularly and routinely apply shortcuts in thinking typically ellaborated as pathophysiology. Reasoning ability appears not only to be case specific-- it is also situation or context specific . Clinicians with broad reasoning ability have extensive experience. Deliberate practice with many cases and in varying contexts is recommended by Ericsson to acquire reasoning expertise. To improve reasoning, some authors have focused on cognitive biases and error prevention. Norman, however, concludes that bias reduction strategies are unlikely to be successful but correcting knowledge deficiencies is likely to lead to reasoning success. Kahnemann promoted System 1 and System 2 thinking for instant pattern recognition (nonanalytic reasoning) and analytic reasoning, respectively . What actually happens in the brain during clinical reasoning is the domain of neuroscience, which may provide insights from research in the near future.

Concepts and Definitions

This chapter is devoted to clarifying terminology and concepts that have been regularly cited and used in the last decades around clinical reasoning. Thus, this chapter represents a conceptual overview.

Success in clinical reasoning is essential to a physician’s performance. Clinical reasoning is both a process and an outcome (with the latter often being referred to as decision-making). While these decisions must be evidence based as much as possible, clearly decisions also involve patient perspectives, the relationship between the physician and the patient, and the system or environment where care is rendered. Definitions of clinical reasoning therefore must include these aspects. While definitions of clinical reasoning vary, they typically share the features that clinical reasoning entails: (i) the cognitive operations allowing physicians to observe, collect, and analyze information and (ii) the resulting decisions for actions that take into account a patient’s specific circumstances and preferences (Eva et al. 2007 ; Durning and Artino 2011 ).

The variety of definitions of clinical reasoning and the heterogeneity in research is likely in part due to the number of fields that have informed our understanding of clinical reasoning. In this chapter, a number of concepts from a broad spectrum of fields is presented to help the reader understand clinical reasoning and to assist the instruction of preclinical medical students. Many of these concepts reflect difficulties inherent to understanding how doctors think and how this type of thinking can be acquired by learners over time. Some provide hypotheses with more or less firm theoretical grounding, but a broad understanding of clinical reasoning requires an ongoing process of investigation.

Learning to Solve Problems in New Areas: Expanding the Learner Domain Space

Klahr and Dunbar proposed a model for scientific discovery (Klahr and Dunbar 1988 ) that may be helpful to understand how learners solve problems in unknown territory, such as what happens when a medical student starts learning to solve medical problems. The student has a learner domain space of knowledge that only partly overlaps, or not at all, with the expert domain space of knowledge, which is the space that contains all possible hypotheses a learner can generate about a problem. Knowledge building during inquiry learning can be considered as expanding the learner domain space to increase that overlap (Lazonder et al. 2008 ).

Early Thinking of Clinical Reasoning: The Computer Analogy

Building on the cognitive psychology work of Newell and Simon about problem-solving in the 1970s (Newell and Simon 1972 ), artificial intelligence (AI) computer models were created to resemble the clinical reasoning process, with programs like MYCIN and INTERNIST (Pauker et al. 1976 ). Analogies between cognitive functioning and the emerging computer capacities led to the assumption that both use algorithmic processes in the working memory, viewed as the central processing unit of the brain. Many predicted that like in chess, computer programs for medical diagnosis would quickly be developed and would perform superiorly to the practicing professional, outperforming the diagnostic accuracy of the best physician’s thinking. Four decades later, however, this has not yet happened and may be impossible. The emergence of self-driving cars as an analogy shows how humans can build highly complex machines, but at least this development in clinical reasoning has been much slower than many had thought it would (Wachter 2015 ; Clancey 1983 ). Robert Wachter, in a recent book about technology in health care, argues that, still better than computers, experienced physicians can distinguish between patients with similar signs and symptoms to determine that “that guy is sick, and the other is okay,” with the “the eyeball test” or intuition, which computers have not been able to capture so far (page 95), just as a computer cannot currently analyze nonverbal information that is so critical to communication in health care. Clinical decision support systems (CDSS, containing a large knowledge base and if-then rules for inferences) have been used with some success at the point of care to support clinicians in decision-making, particularly in medication decisions, but, integrated with electronic health records, they have not been shown to improve clinical outcome parameters as of yet (Moja et al. 2014 ).

Abandoning Clinical Reasoning as a General Problem-Solving Ability

Expertise in clinical reasoning was initially viewed as being synonymous with acquiring general problem-solving procedures (Newell and Simon 1972 ). However, in a groundbreaking study, published as a book in 1978 ( Medical Problem Solving ), Elstein and colleagues found few differences between expert (attending physicians) and novice diagnosticians (medical students) in the way they solve diagnostic problems (Elstein et al. 1978 ). The primary difference appeared to be in their knowledge and in particular the way it is structured as a consequence of experience. Thus while medical students and practicing physicians generated a similar number of diagnostic hypotheses differential diagnosis of similar length, practicing physicians were far more likely to list the correct diagnosis. This insight replaced the era that was marked by the belief that clinical reasoning could be measured as a distinct skill that would result in superior performance regardless of the specifics of a patient’s presentation. Content knowledge was shown to be very important but still does not guarantee success in clinical reasoning. Variation in clinical performance is a product of the expert’s integration of his or her knowledge of the signs and symptoms of disease with contextual factors in order to arrive at an adaptive solution.

Deconstructing the Reasoning Process

Abstraction can be viewed as generalization from a finding to a conclusion (hemoglobin <12 gm/dl in an adult male is labeled as “anemia”).
Abduction is a backward reasoning process to explain why this adult male should have anemia. “Abductive reasoning” was first coined as a term by logician C.S. Peirce in the nineteenth century to signify a common process when a surprising observation takes place that leads to a hypothesis (“The lawn is wet! Ergo, it has probably rained.”) and is based on knowledge of possible causations and must be tested (“but it could also be the neighbor’s sprinkler”). Abduction is considered to be a primary means of acquiring new ideas in clinical reasoning (Bolton 2015 ).
Deduction is the process of testing the hypothesis (e.g., of anemia) through confirmation by expected other diagnostic findings: if conditions X and Y are met, inference Z must be true.
Induction is the process of generalization from multiple cases and more applicable in research than in individual patient care: if multiple patients show similar signs and symptoms, general rules may be created to explain new cases.

Part of this process is forward-driven reasoning (hypothesis generation through data), and another part is backward-driven reasoning (hypothesis testing) (Patel et al. 2005 ).

Knowledge Representations to Support Reasoning

In a 1996 review, Custers and colleagues categorized the thinking about the way physician’s cognition is organized around clinical knowledge in three alternative frameworks and provided critical notes (Custers et al. 1996 ). These mental representations could have the form of prototypes , instances , or semantic networks . All three of these models have assets and drawbacks in their explanatory power for clinical reasoning. The prototype framework or prototype theory assumes that multiple encounters with related diseases lead physicians to remember the common denominators, resulting in single prototypes in long-term memory. The instances framework assumes that physicians actually remember the individual instances of patient encounters without abstraction, and context-specific (situation specific) information may be part of these instances. The semantic network theory posits the existence of nodes of information units, connected with other nodes in the network. The strength of the network and its nodes depends on the intensity of its use. Schemas and illness scripts are medically meaningful interconnected nodes that can be strengthened and adapted based on clinical experience.

Prototyping and Semantic Qualifiers

Georges Bordage introduced the term semantic qualifiers referring to the use of abstract, often binary, terms to help sort through and organize (e.g., chunk) patient information. They are “useful adjectives” that represent an abstraction of the situational clinical findings (Chang et al. 1998 ). A commonly cited example of the use of semantic qualifiers is translating a patient who is presenting with knee swelling and pain into a presentation of acute monarticular arthritis. Note three semantic qualifiers – “acute,” “monoarticular,” and “arthritis.” The reason why these qualifiers are important is that the structure of clinical knowledge in the clinician’s mind is organized with such qualifiers, as claimed by Bordage. To enable recognition and linkage, the clinician must first translate what she hears and sees into such terminology (Bordage 1994 ). An assumption is that the clinician’s memory contains prototypes of diseases (Bordage and Zacks 1984 ), generalizable representations that enable recognition. Bordage stresses how semantically rich discourses about patients are associated with greater diagnostic accuracy (Bordage 2007 ).

Illness Script Theory

Custers recently summarized scripts as high-level conceptual knowledge structures in long-term memory, representing general event sequences, in which the individual events are interconnected by temporal and often causal or hierarchical relationships (“usually diabetes type II occurs a older age, a overweight is associated; late symptoms might include vascular problems in the retina, in the lower limbs and in other places”). Scripts are activated as integral wholes in appropriate contexts that should contain relevant variables, including clinical findings in the patient. “Slots” in the reasoning process can be filled with information present in the actual situation, retrieved from memory, or inferred from the context (Custers 2015 ). Illness scripts, first introduced by Barrows and Feltovich, are believed to be chunks in long-term memory that contain three components, enabling conditions (past history and causes) , fault (pathophysiology), and consequences (signs and symptoms) (Feltovics and Barrows 1984 ), and are elaborated further by Schmidt and Boshuizen ( 1993 ). Illness scripts are stored in long-term memory as units with temporal (i.e., sequential) components, as a film script of unfolding events, and patients are remembered as instances of a script. With experience, physicians build a larger repertoire of illness scripts and more elaborated scripts.

Illness scripts are shaped by experience and continually refined throughout one’s clinical practice. When an experienced physician initially sees a patient, his or her verbal and nonverbal information is thought to immediately activate relevant illness scripts. This effortless, fast thinking, or nonanalytic process is referred to as script activation . In some cases, only one script is activated, and in these cases, one may arrive at the correct diagnosis (e.g., “type II diabetes mellitus”). In other cases, multiple scripts are activated, and then theory holds that we choose the most likely diagnosis by comparing and contrasting alternative illness scripts that were activated (through analytic or slow thinking). Early learners may not activate any scripts when they initially see a patient, and experts may activate one or several illness scripts.

Encapsulation of Knowledge and the Intermediate Effect

With increasing clinical information stored as illness scripts in the long-term memory of the physician, diagnostic reasoning should steadily become more accurate. However, studies have shown that more novice clinicians (e.g., those just out of training such as recent graduates from residency education) sometimes outperform physicians who have been in practice for some time (e.g., “experts”) on the recall of details from clinical cases seen. This finding was coined by Schmidt and Boshuizen as the intermediate effect (Schmidt and Boshuizen 1993 ). While inexperienced clinicians may consciously use pathophysiological thinking when solving clinical problems, the frequent use of similar thinking pathways leads to efficient shortcuts, and after a while it may no longer be possible to unfold these pathways. The pathophysiological knowledge about the disease becomes encapsulated into diagnostic labels or high-level simplified causal models that explain signs and symptoms (Schmidt and Mamede 2015 ).

System 1 and 2 Thinking as Dual Processes

Dual process theory refers to two processes that are thought to apply during reasoning (Croskerry et al. 2014 ). Briefly, dual process theory argues that we have two general thought processes. Fast thinking (sometimes called System I thinking or “nonanalytic” reasoning) is believed to be quick, subconscious, and typically effortless. An example of a fast thinking strategy is pattern recognition (Eva 2005 ). An example of pattern recognition in medicine would happen when a physician examines a patient with palpitations and immediately recognizes the cardinal features or “pattern” of Graves’ disease, when also observing exophthalmia, fine resting tremor, and thyromegaly. Slow or analytic thinking (System 2 thinking) on the other hand is effortful and conscious. An example of System 2 thinking would be working through a patient’s acid base status (e.g., calculating an anion gap, using Winter’s formula, and calculating a delta-delta gap). Dual process theory has recently been popularized in the book Thinking, Fast and Slow by Daniel Kahneman ( 2011 ). More recent work with dual process theory argues that both of these processes are used simultaneously, e.g., it’s not one or the other but rather one uses a combination of both fast and slow thinking in practice. In other words, fast and slow thinking can be viewed as a continuum (Custers 2013 ). Efficient clinical work requires fast thinking. The capacity of the working memory would be overloaded if analytic reasoning were required for all decisions in patient care (Young et al. 2014 ).

Case Specificity and Context Specificity

In Elstein and colleagues ’ seminal work on medical problem-solving (Elstein et al. 1978 ), researchers noted that physician performance on one patient or case did not predict performance on a subsequent content area or case, giving rise to the phenomenon of case specificity . These findings would be quite surprising if medical problem-solving were a general skill.

A second vexing problem in practice is the more recently highlighted phenomenon of context specificity . Context specificity refers to the finding that a physician can see two patients with the same chief complaint and the same (or nearly identical) symptoms and physical findings and have the same diagnosis, yet, in different contexts, arrive at different diagnoses (Durning et al. 2011 ). The context can be helpful to arrive at the correct diagnosis (Hobus et al. 1987 ) or harmful and lead to error (Eva 2005 ). In other words, something other than the “essential content” is driving the physician’s clinical reasoning. Durning and Artino hold that the outcome of clinical reasoning is driven by the context, which includes the physician, the patient, the system, and their interactions (Durning and Artino 2011 ). The notion of system includes appointment length, appointment location, support systems, and clinic staffing (Durning and Artino 2011 ) and stresses the importance of the situation. One example of “situativity” is situated cognition , which breaks down an activity like clinical reasoning into physician, patient, and environment as well as interactions between these components. Clinical reasoning is believed to emerge from these factors and their interactions. Another example of situativity, situated learning , stresses participation in an activity and identity formation as learning versus the acquisition of generalized facts.

Clinical Reasoning and the Development of Expert Performance

Despite the finding that clinical reasoning is content -dependent and context -dependent, expertise in diagnostic and therapeutic reasoning in general varies among physicians even with similar experience. Some internists are considered better diagnosticians and some surgeons better operators that others. It remains useful to think of what leads to superb performance, as education can be a part of it (Asch et al. 2014 ). Indeed, many scholars prefer the term expert performance as opposed to expertise when referring to clinical reasoning as the former acknowledges the many nuances to this ability that we have outlined in this chapter.

For procedural performance, repetitive practice is key. Competence in colonoscopy requires experience with 150–200 colonoscopies under supervision (Ekkelenkamp et al. 2016 ). That competence improves with practice is not surprising and known from, for instance, in chess (De Groot 1978 ). Anecdotally, in the 1960s the Hungarian educational psychologist László Polgár was determined to raise his yet unborn children to become highly skilled in a specific domain and chose chess. All three daughters received careful, highly intensive training, from very young age on, and have become world-top chess players, two of which are currently considered the world’s best female chess players. Psychologist Ericsson has generalized the idea that, rather than innate talent, deliberate practice is key to expert performance (Ericsson et al. 1993 ). He distinguishes three subsequent mental representations: a planning phase with clear performance goals, a translation to execution, and a representation for monitoring how well one does. Applications in medical training have been described (Ericsson 2015 ) but have mainly focused on procedures. Clinical reasoning may benefit from deliberate practice, and the work of Mamede et al., using deliberate practice, shows how reasoning can benefit as well (Mamede et al. 2014 ).

Reflection During Diagnostic Thinking

Donald Schön coined the terminology of reflection in action and reflection on action , as a description of thinking of high-level professionals (Schön 1983 ). Knowing what to do when you do it may not require much effort if actions are routine, but professionals with nonroutine tasks may often face small problems or questions that require instant adaptive action. Schön maintains that reflection-in-action must be practiced by learners becoming professionals. Mamede and colleagues developed the method of “structured reflection” to improve students’ diagnostic reasoning (Mamede et al. 2010 , 2014 a, b). Structured reflection in the context of clinical reasoning means that problem-solvers explicitly match a patient’s presentation (case) against every diagnosis they consider for that case. Mamede et al. demonstrated a beneficial effect of this approach. Detailed comparison of a patient’s signs and symptoms with the already available and activated illness scripts and noticing similarities and discrepancies appears to be the mechanism behind this restructuring of knowledge as a consequence of structured reflection. The authors recommend deliberate reflection as a tool for learning clinical reasoning (Schmidt and Mamede 2015 ).

Bias and Error in Clinical Reasoning

Availability bias . A differential diagnosis is influenced by what is easily recalled, creating a false sense of prevalence.
Representative bias (or judging by similarity ). Clinical suspicion is influenced solely by signs and symptoms and neglects prevalence of competing diagnoses.
Confirmation bias (or pseudodiagnosticity ). Additional testing confirms suspected diagnosis but fails to test competing hypotheses.
Anchoring bias. Inadequate adjustment of a differential diagnosis in light of new data resulting in a final diagnosis unduly influenced by the starting point.
Bounded rationality bias (or search satisficing ). Clinicians stop searching for additional diagnoses after the anticipated diagnosis is made leading to a premature closure of the reasoning process.
Outcome bias . A clinical decision is judged on the outcome rather than on the logic and evidence supporting the decision.

A limitation of this approach is that when the reasoning is believed to be successful, biases are not typically recognized, and when looking at a case in hindsight, many mistakes can easily be labeled as caused by “bias.” Indeed, so-called biases actually may serve as heuristics to guide successful behavior (Gigerenzer and Gaissmaier 2011 ; Gigerenzer 2007 ). In a recent overview, Norman and colleagues conclude that interventions directed at error reduction through the identification of heuristics and biases have no effect on diagnostic errors. Instead, most errors seem to originate from a limited knowledge based of the clinician (Norman et al. 2017 ).

Neuroscience and Visual Expertise in Clinical Reasoning

While neuroscience is quickly uncovering many cognitive processes, clinical reasoning has hardly been subject of such studies. More recently however a new line of research has evolved which seeks to explore the biologic underpinnings of clinical reasoning. Indeed, an Achilles heel of clinical reasoning is that it is less subject to introspection or visualization, and thus these new methods such as functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) are emerging and show particular promise for enhancing our understanding of System 1 thinking. One of the first publications in this domain is from Durning et al. who studied brain process with functional MRI techniques in novices and experts solving clinical problems through vignette-based multiple choice questions. Many parts of the brain were activated. The researchers observed activity in various regions of the prefrontal cortex (Durning et al. 2015 ). While preliminary, fMRI may be a promising route of future investigation.

A new and related avenue of investigation is that of visual expertise (Bezemer 2017 ; van der Gijp et al. 2016 ). Medicine is a highly visual profession, not only for specific disciplines such as radiology, pathology, dermatology, surgery, and cardiology but also in primary care (Kok and Jarodzka 2017 ). Visually observing a patient, human tissue, or a representation of it, and recognizing abnormality, may not easily be expressed in words but can instantly lead to a System 1 recognition.

The intention of this chapter was to provide an overview of theoretical concepts, frequently used terms, and a number of significant thinkers and authors in this domain, all of which underlie our current understanding of clinical reasoning to support the teaching of students about clinical reasoning in the preclinical period and beyond.

While much of the cited literature appeared after the model of case-based clinical reasoning was first created in 1992 (ten Cate 1994 ), and some aspects apply to clinical rather than preclinical education, none of the recommendations that could be drawn for this chapter would conflict the CBCR approach.

Although it is apparent that there are still numerous gaps in our collective understanding of clinical reasoning, it is also clear that progress into a more thorough understanding of clinical reasoning is advancing.

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Cite this Page ten Cate O, Durning SJ. Understanding Clinical Reasoning from Multiple Perspectives: A Conceptual and Theoretical Overview. 2017 Nov 7. In: ten Cate O, Custers EJFM, Durning SJ, editors. Principles and Practice of Case-based Clinical Reasoning Education: A Method for Preclinical Students [Internet]. Cham (CH): Springer; 2018. Chapter 3. doi: 10.1007/978-3-319-64828-6_3
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Medical Problem Solving: An Analysis of Clinical Reasoning. By Arthur S
Medical Problem Solving studies from the perspective of cognitive psychol-ogy the processes by which physicians make clinical diagnosis decisions. The book reports on a series of studies begun in the early 1970s. It finds little support for the notion that clinical knowledge represents a systematic
Medical problem solving : an analysis of clinical reasoning
Medical problem solving : an analysis of clinical reasoning ... Medical problem solving : an analysis of clinical reasoning by Elstein, Arthur S. (Arthur Shirle), 1935-Publication date 1978 Topics Diagnostics, Résolution de problème, 44.51 medical diagnostic, Geneeskunde, Diagnose, Probleemoplossing, ...
Medical Problem Solving: An Analysis of Clinical Reasoning
Medical Problem Solving: An Analysis of Clinical Reasoning by Arthur S. Elstein (Author), Lee S. Shulman (Author), Sarah A. Sprafka (Author) & 0 more 4.0 4.0 out of 5 stars 3 ratings
Medical Problem Solving: An Analysis of Clinical Reasoning
Full text. Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (169K), or click on a page image below to browse page by page.
Medical problem solving: an analysis of clinical reasoning
Results: Of the 166 respondents analysed, 103 (62%) got the correct definition of clinical reasoning with early career doctors having a higher proportion of correct respondents , χ2 = 4.59, p = 0.032. Specific areas of difficulties identified were with making clinical diagnosis in 50 (30.1%) and patho-logic diagnosis (es) in 38 (22.9%).
Medical Problem Solving: An Analysis of Clinical Reasoning
Medical Problem Solving: An Analysis of Clinical Reasoning. Elstein AS, ed. Cambridge, MA: Harvard University Press; 1978. ISBN: 9780674561250. Elstein AS, ed. Cambridge, MA: Harvard University Press; 1978. ISBN: 9780674561250. View more articles from the same authors. Clinical reasoning lies at the heart of formulating diagnoses and selecting ...
(PDF) Medical Problem Solving, an Analysis of Clinical Reasoning by
PDF | On Jan 1, 1980, Wellesley R. Foshay published Medical Problem Solving, an Analysis of Clinical Reasoning by Arthur S. Elstein; Lee S. Shulman; Sarah A. Sprafka | Find, read and cite all the ...
Medical Problem Solving
Elstein, Arthur S., Shulman, Lee S. and Sprafka, Sarah A.. Medical Problem Solving: An Analysis of Clinical Reasoning.Cambridge, MA and London, England: Harvard ...
Elstein, Arthur S., Lee S. Shulman, and Sarah A. Sprafka, et al
Elstein, Arthur S., Lee S. Shulman, and Sarah A. Sprafka, et al. Medical Problem Solving: An Analysis of Clinical Reasoning. Cambridge, Massachusetts: Harvard University Press, 1978. Volume 3, Issue 3. ... Influences of early diagnostic suggestions on clinical reasoning. Go to citation Crossref Google Scholar. Learning Analytics Applied to ...
Medical Problem Solving: An Analysis of Clinical Reasoning.Arthur S
The authors posit a framework to teach diagnostic reasoning in the clinical setting that targets specific deficiencies in the students' reasoning process and recommends more comparative studies with standardized assessment and evaluation of long-term effectiveness of these methods.
Medical Problem Solving: An Analysis of Clinical Reasoning
Medical Problem Solving: An Analysis of Clinical Reasoning. Arthur S. Elstein , Lee S. Shulman , Sarah A. Sprafka
Medical Problem Solving: A Ten-Year Retrospective
This essay reviews the origins, findings and influence of the monograph Medical Problem Solving: An Analysis of Clinical Reasoning. Majorfindings of the monograph are reviewed in the light of subsequent work and the results of selected studies of clinical cognition are related to the book's conclusions, thus sketching the growth of this field of research in the decade since publication.
Medical Problem Solving: An Analysis of Clinical Reasoning
The authors describe how scripts are used in diagnostic tasks, how the script concept fits within the clinical reasoning literature, how it contrasts with competing theories of clinical reasoning, how educators can help students build and refine scripts, and how scripts can be used to assess clinical competence. Expand
Medical Problem Solving: An Analysis of Clinical Reasoning
Buy Medical Problem Solving: An Analysis of Clinical Reasoning by Elstein, As (ISBN: 9780674561250) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders.
Five decades of research and theorization on clinical reasoning: a
Introduction. Clinical reasoning is a complex cognitive process that is essential to evaluate and manage a patient's medical problem. 1 It includes the diagnosis of the patient problem, making a therapeutic decision and estimating the prognosis for the patient. 2 In describing the importance of clinical reasoning, it has been acknowledged that clinical reasoning is the central part of ...
Clinical Reasoning: Defining It, Teaching It, Assessing It, Studying It
From the earliest studies of medical problem solving 4, 5 to the present, the most reproducible result is that clinical reasoning performance is highly content (and context) specific. Solving a clinical problem in one discipline holds little predictive value for how one will do with a problem in another area.
Medical Problem Solving: An Analysis of Clinical Reasoning
Clinical Problem Solving and Decision Psychology: Comment on "The Epistemology of Clinical Reasoning" Elstein, Arthur S. (2000-10) Related Items in Google Scholar ©2009—2024 Bioethics Research Library ... Medical Problem Solving: An Analysis of Clinical Reasoning. Creator. Elstein, Arthur S. Shulman, Lee S. and Sprafka, Sarah A.
Medical Problem Solving: An Analysis of Clinical Reasoning
The authors describe and discuss clinical problem analysis (CPA), an approach to solving complex clinical problems, and discusses the value of CPA's content-independent (methodical) approach and argues that teaching students to use it will enable them to avoid some common diagnostic reasoning errors and pitfalls. Expand.
Clinical problem solving and diagnostic decision making: selective
This is the fourth in a series of five articles This article reviews our current understanding of the cognitive processes involved in diagnostic reasoning in clinical medicine. It describes and analyses the psychological processes employed in identifying and solving diagnostic problems and reviews errors and pitfalls in diagnostic reasoning in the light of two particularly influential ...
Medical problem solving: A ten-year retrospective.
Reviews the origins, findings, and influence of the monograph Medical problem solving: An analysis of clinical reasoning by A. S. Elstein et al (1978). Methodological problems and scholarly issues in the field of cognition are discussed, including (1) sampling cases and Ss, (2) the definition of medical expertise, (3) the role of verbal report in analyzing thinking, (4) the level of clinical ...
Introduction
What Is Clinical Reasoning? Clinical reasoning is usually defined in a very general sense as "The thinking and decision -making processes associated with clinical practice" (Higgs and Jones 2000) or simply "diagnostic problem solving" (Elstein 1995).. For the purpose of this book, we define clinical reasoning as the mental process that happens when a doctor encounters a patient and is ...
Clinical Problem Analysis (CPA): A Systematic Approach to Te ...
This approach, called clinical problem analysis (CPA), has three major goals: To encourage adoption of a problem-solving method that maximizes the probability of solving complex clinical problems by optimally exploiting the benefits of both a systematic approach and the mobilization of the relevant knowledge the practitioner * possesses.
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Critical thinking, problem solving and deductive reasoning skills. Demonstrated familiarity and adept use with software and technical applications including but not limited to: Microsoft Office products (Outlook, Excel, Word, PowerPoint) and Epic (Electronic Health Records).
Understanding Clinical Reasoning from Multiple Perspectives: A
Rather than a historical overview as in Chap. 2, this chapter provides the reader with insight into the various approaches that have been used to understand clinical reasoning. We review concepts and major scholars who have been involved in such investigations. Cognitive psychologists Newel and Simon theorized about problem-solving skills and artificial intelligence and initiated the use of ...

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Principles and Practice of Case-based Clinical Reasoning Education: A Method for Preclinical Students [Internet].

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Learning to Solve Problems in New Areas: Expanding the Learner Domain Space

Early Thinking of Clinical Reasoning: The Computer Analogy

Abandoning Clinical Reasoning as a General Problem-Solving Ability

Deconstructing the Reasoning Process

Knowledge Representations to Support Reasoning

Prototyping and Semantic Qualifiers

Illness Script Theory

Encapsulation of Knowledge and the Intermediate Effect

System 1 and 2 Thinking as Dual Processes

Case Specificity and Context Specificity

Clinical Reasoning and the Development of Expert Performance

Reflection During Diagnostic Thinking

Bias and Error in Clinical Reasoning

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