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Medical problem solving : an analysis of clinical reasoning

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Medical problem solving: An analysis of clinical reasoning

Related Papers

Arthur S Elstein

Jan Kiesewetter , Ralf Schmidmaier

Problem-solving in terms of clinical reasoning is regarded as a key competence of medical doctors. Little is known about the general cognitive actions underlying the strategies of problem-solving among medical students. In this study, a theory-based model was used and adapted in order to investigate the cognitive actions in which medical students are engaged when dealing with a case and how patterns of these actions are related to the correct solution. Twenty-three medical students worked on three cases on clinical nephrology using the think-aloud method. The transcribed recordings were coded using a theory-based model consisting of eight different cognitive actions. The coded data was analysed using time sequences in a graphical representation software. Furthermore the relationship between the coded data and accuracy of diagnosis was investigated with inferential statistical methods. The observation of all main actions in a case elaboration, including evaluation, representation and...

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Luis Humberto Orozco Pulido

This paper presents a brief analysis of most known problem solving theoretical models realized using epistemological categories such as observer position, object of study, methods and procedures, and descriptive or explicative scope. The review showed linear and cyclical models, the need to recognize method's limitations to generalizing, the relevance of expliciting observer position, and a diffuse delimitation of the object problem solving as a cognitive process. An integrative and molar theoretical model of problem solving as a dependent variable is proposed whose variations go with critical cognitive processes (information processing, comprehension, reasoning, cognitive styles, and attitudes). Its molar feature refers to that it integrates basic and high order processes in a general cognitive activity; this proposal has to be extensively tested.

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This paper has five objectives: (a) to review the scientific background of, and major findings reported in, Medical Problem Solving, now widely recognized as a classic in the field; (b) to compare these results with some of the findings in a recent best-selling collection of case studies; (c) to summarize criticisms of the hypothesis-testing model and to show how these led to greater emphasis on the role of clinical experience and prior knowledge in diagnostic reasoning; (d) to review some common errors in diagnostic reasoning; (e) to examine strategies to reduce the rate of diagnostic errors, including evidence-based medicine and systematic reviews to augment personal knowledge, guidelines and clinical algorithms, computer-based diagnostic decision support systems and second opinions to facilitate deliberation, and better feedback.

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

• Related content
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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/ )

• Elstein AS ,
• Shulman LS ,
• Bordage G ,
• Schmidt HG ,
• Norman GR ,
• Boshuizen HPA
• Kahneman D ,
• Sox HC Jr . ,
• Higgins MC ,
• Mellers BA ,
• Schwartz A ,
• Chapman GB ,
• Sonnenberg F
• Glasziou P ,
• Pliskin J ,
• Brooks LR ,
• Coblentz CL ,
• Lemieux M ,
• Kassirer JP ,
• Kopelman RI
• Sackett DL ,
• Haynes RB ,
• Guyatt GH ,
• Richardson WS ,
• Rosenberg W ,
• Tversky A ,
• Fischhoff B ,
• Bostrom A ,
• Quadrell M J
• Redelmeier DA ,
• Koehler DJ ,
• Liberman V ,
• Wallsten TS
• Bergus GR ,

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Rethinking clinical decision-making to improve clinical reasoning

Salvatore corrao.

1 Department of Internal Medicine, National Relevance and High Specialization Hospital Trust ARNAS Civico, Palermo, Italy

2 Dipartimento di Promozione della Salute Materno Infantile, Medicina Interna e Specialistica di Eccellenza “G. D’Alessandro” (PROMISE), University of Palermo, Palermo, Italy

Christiano Argano

Associated data.

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Improving clinical reasoning techniques is the right way to facilitate decision-making from prognostic, diagnostic, and therapeutic points of view. However, the process to do that is to fill knowledge gaps by studying and growing experience and knowing some cognitive aspects to raise the awareness of thinking mechanisms to avoid cognitive errors through correct educational training. This article examines clinical approaches and educational gaps in training medical students and young doctors. The authors explore the core elements of clinical reasoning, including metacognition, reasoning errors and cognitive biases, reasoning strategies, and ways to improve decision-making. The article addresses the dual-process theory of thought and the new Default Mode Network (DMN) theory. The reader may consider the article a first-level guide to deepen how to think and not what to think, knowing that this synthesis results from years of study and reasoning in clinical practice and educational settings.

Introduction

Clinical reasoning is based on complex and multifaceted cognitive processes, and the level of cognition is perhaps the most relevant factor that impacts the physician’s clinical reasoning. These topics have inspired considerable interest in the last years ( 1 , 2 ). According to Croskerry ( 3 ) and Croskerry and Norman ( 4 ), over 40 affective and cognitive biases may impact clinical reasoning. In addition, it should not be forgotten that both the processes and the subject matter are complex.

In medicine, there are thousands of known diagnoses, each with different complexity. Moreover, in line with Hammond’s view, a fundamental uncertainty will inevitably fail ( 5 ). Any mistake or failure in the diagnostic process leads to a delayed diagnosis, a misdiagnosis, or a missed diagnosis. The particular context in which a medical decision is made is highly relevant to the reasoning process and outcome ( 6 ).

More recently, there has been renewed interest in diagnostic reasoning, primarily diagnostic errors. Many researchers deepen inside the processes underpinning cognition, developing new universal reasoning and decision-making model: The Dual Process Theory.

This theory has a prompt implementation in medical decision-making and provides a comprehensive framework for understanding the gamma of theoretical approaches taken into consideration previously. This model has critical practical applications for medical decision-making and may be used as a model for teaching decision reasoning. Given this background, this manuscript must be considered a first-level guide to understanding how to think and not what to think, deepening clinical decision-making and providing tools for improving clinical reasoning.

Too much attention to the tip of the iceberg

The New England Journal of Medicine has recently published a fascinating article ( 7 ) in the “Perspective” section, whereon we must all reflect on it. The title is “At baseline” (the basic condition). Dr. Bergl, from the Department of Medicine of the Medical College of Wisconsin (Milwaukee), raised that his trainees no longer wonder about the underlying pathology but are focused solely on solving the acute problem. He wrote that, for many internal medicine teams, the question is not whether but to what extent we should juggle the treatment of critical health problems of patients with care for their coexisting chronic conditions. Doctors are under high pressure to discharge, and then they move patients to the next stage of treatment without questioning the reason that decompensated the clinical condition. Suppose the chronic condition or baseline was not the fundamental goal of our performance. In that case, our juggling is highly inconsistent because we are working on an intermediate outcome curing only the decompensation phase of a disease. Dr. Bergl raises another essential matter. Perhaps equally disturbing, by adopting a collective “base” mentality, we unintentionally create a group of doctors who prioritize productivity rather than developing critical skills and curiosity. We agree that empathy and patience are two other crucial elements in the training process of future internists. Nevertheless, how much do we stimulate all these qualities? Perhaps are not all part of cultural backgrounds necessary for a correct patient approach, the proper clinical reasoning, and balanced communication skills?

On the other hand, a chronic baseline condition is not always the real reason that justifies acute hospitalization. The lack of a careful approach to the baseline and clinical reasoning focused on the patient leads to this superficiality. We are focusing too much on our students’ practical skills and the amount of knowledge to learn. On the other hand, we do not teach how to think and the cognitive mechanisms of clinical reasoning.

Time to rethink the way of thinking and teaching courses

Back in 1910, John Dewey wrote in his book “How We Think” ( 8 ), “The aim of education should be to teach us rather how to think than what to think—rather improve our minds to enable us to think for ourselves than to load the memory with the thoughts of other men.”

Clinical reasoning concerns how to think and make the best decision-making process associated with the clinical practice ( 9 ). The core elements of clinical reasoning ( 10 ) can be summarized in:

• 1. Evidence-based skills,
• 2. Interpretation and use of diagnostic tests,
• 3. Understanding cognitive biases,
• 4. Human factors,
• 5. Metacognition (thinking about thinking), and
• 6. Patient-centered evidence-based medicine.

All these core elements are crucial for the best way of clinical reasoning. Each of them needs a correct learning path to be used in combination with developing the best thinking strategies ( Table 1 ). Reasoning strategies allow us to combine and synthesize diverse data into one or more diagnostic hypotheses, make the complex trade-off between the benefits and risks of tests and treatments, and formulate plans for patient management ( 10 ).

Set of some reasoning strategies (view the text for explanations).

However, among the abovementioned core element of clinical reasoning, two are often missing in the learning paths of students and trainees: metacognition and understanding cognitive biases.

Metacognition

We have to recall cognitive psychology, which investigates human thinking and describes how the human brain has two distinct mental processes that influence reasoning and decision-making. The first form of cognition is an ancient mechanism of thought shared with other animals where speed is more important than accuracy. In this case, thinking is characterized by a fast, intuitive way that uses pattern recognition and automated processes. The second one is a product of evolution, particularly in human beings, indicated by an analytical and hypothetical-deductive slow, controlled, but highly consuming way of thinking. Today, the psychology of thinking calls this idea “the dual-process theory of thought” ( 11 – 14 ). The Nobel Prize in Economic Sciences awardee Daniel Kahneman has extensively studied the dichotomy between the two modes of thought, calling them fast and slow thinking. “System 1” is fast, instinctive, and emotional; “System 2” is slower, more deliberative, and more logical ( 15 ). Different cerebral zones are involved: “System 1” includes the dorsomedial prefrontal cortex, the pregenual medial prefrontal cortex, and the ventromedial prefrontal cortex; “System 2” encompasses the dorsolateral prefrontal cortex. Glucose utilization is massive when System 2 is performing ( 16 ). System 1 is the leading way of thought used. None could live permanently in a deliberate, slow, effortful way. Driving a car, eating, and performing many activities over time become automatic and subconscious.

A recent brilliant review of Gronchi and Giovannelli ( 17 ) explores those things. Typically, when a mental effort is required for tasks requiring attention, every individual is subject to a phenomenon called “ego-depletion.” When forced to do something, each one has fewer cognitive resources available to activate slow thinking and thus is less able to exert self-control ( 18 , 19 ). In the same way, much clinical decision-making becomes intuitive rather than analytical, a phenomenon strongly affected by individual differences ( 20 , 21 ). Experimental evidence by functional magnetic resonance imaging and positron emission tomography studies supports that the “resting state” is spontaneously active during periods of “passivity” ( 22 – 25 ). The brain regions involved include the medial prefrontal cortex, the posterior cingulate cortex, the inferior parietal lobule, the lateral temporal cortex, the dorsal medial prefrontal cortex, and the hippocampal formation ( 26 ). Findings reporting high-metabolic activity in these regions at rest ( 27 ) constituted the first clear evidence of a cohesive default mode in the brain ( 28 ), leading to the widely acknowledged introduction of the Default Mode Network (DMN) concept. The DMN contains the medial prefrontal cortex, the posterior cingulate cortex, the inferior parietal lobule, the lateral temporal cortex, the dorsal medial prefrontal cortex, and the hippocampal formation. Lower activity levels characterize the DMN during goal-directed cognition and higher activity levels when an individual is awake and involved in the mental processes requiring low externally directed attention. All that is the neural basis of spontaneous cognition ( 26 ) that is responsible for thinking using internal representations. This paradigm is growing the idea of stimulus-independent thoughts (SITs), defined by Buckner et al. ( 26 ) as “thoughts about something other than events originating from the environment” that is covert and not directed toward the performance of a specific task. Very recently, the role of the DMN was highlighted in automatic behavior (the rapid selection of a response to a particular and predictable context) ( 29 ), as opposed to controlled decision making, suggesting that the DMN plays a role in the autopilot mode of brain functioning.

In light of these premises, everyone can pause to analyze what he is doing, improving self-control to avoid “ego-depletion.” Thus, one can actively switch between one type of thinking and the other. The ability to make this switch makes the physician more performing. In addition, a physician can be trained to understand the ways of thinking and which type of thinking is engaged in various situations. This way, experience and methodology knowledge can energize Systems 1 and 2 and how they interact, avoiding cognitive errors. Figure 1 summarizes all the concepts abovementioned about the Dual Mode Network and its relationship with the DMN.

Graphical representation of the characteristics of Dual Mode Network, including the relationship between the two systems by Default Mode Network (view the text for explanations).

Emotional intelligence is another crucial factor in boosting clinical reasoning for the best decision-making applied to a single patient. Emotional intelligence recognizes one’s emotions. Those others label different feelings appropriately and use emotional information to guide thinking and behavior, adjust emotions, and create empathy, adapt to environments, and achieve goals ( 30 ). According to the phenomenological account of Fuchs, bodily perception (proprioception) has a crucial role in understanding others ( 31 ). In this sense, the proprioceptive skills of a physician can help his empathic understanding become elementary for empathy and communication with the patient. In line with Fuchs’ view, empathic understanding encompasses a bodily resonance and mediates contextual knowledge about the patient. For medical education, empathy should help to relativize the singular experience, helping to prevent that own position becomes exclusive, bringing oneself out of the center of one’s own perspective.

Reasoning errors and cognitive biases

Errors in reasoning play a significant role in diagnostic errors and may compromise patient safety and quality of care. A recently published review by Norman et al. ( 32 ) examined clinical reasoning errors and how to avoid them. To simplify this complex issue, almost five types of diagnostic errors can be recognized: no-fault errors, system errors, errors due to the knowledge gap, errors due to misinterpretation, and cognitive biases ( 9 ). Apart from the first type of error, which is due to unavoidable errors due to various factors, we want to mention cognitive biases. They may occur at any stage of the reasoning process and may be linked to intuition and analytical systems. The most frequent cognitive biases in medicine are anchoring, confirmation bias, premature closure, search satisficing, posterior probability error, outcome bias, and commission bias ( 33 ). Anchoring is characterized by latching onto a particular aspect at the initial consultation, and then one refuses to change one’s mind about the importance of the later stages of reasoning. Confirmation bias ignores the evidence against an initial diagnosis. Premature closure leads to a misleading diagnosis by stopping the diagnostic process before all the information has been gathered or verified. Search satisficing blinds other additional diagnoses once the first diagnosis is made posterior probability error shortcuts to the usual patient diagnosis for previously recognized clinical presentations. Outcome bias impinges on our desire for a particular outcome that alters our judgment (e.g., a surgeon blaming sepsis on pneumonia rather than an anastomotic leak). Finally, commission bias is the tendency toward action rather than inaction, assuming that only good can come from doing something (rather than “watching and waiting”). These biases are only representative of the other types, and biases often work together. For example, in overconfidence bias (the tendency to believe we know more than we do), too much faith is placed in opinion instead of gathered evidence. This bias can be augmented by the anchoring effect or availability bias (when things are at the forefront of your mind because you have seen several cases recently or have been studying that condition in particular), and finally by commission bias—with disastrous results.

Novice vs. expert approaches

The reasoning strategies used by novices are different from those used by experts ( 34 ). Experts can usually gather beneficial information with highly effective problem-solving strategies. Heuristics are commonly, and most often successfully, used. The expert has a saved bank of illness scripts to compare and contrast the current case using more often type 1 thinking with much better results than the novice. Novices have little experience with their problems, do not have time to build a bank of illness scripts, and have no memories of previous similar cases and actions in such cases. Therefore, their mind search strategies will be weak, slow, and ponderous. Heuristics are poor and more often unsuccessful. They will consider a more comprehensive range of diagnostic possibilities and take longer to select approaches to discriminate among them. A novice needs specific knowledge and specific experience to become an expert. In our opinion, he also needs special training in the different ways of thinking. It is possible to study patterns, per se as well. It is, therefore, likely to guide the growth of knowledge for both fast thinking and slow one.

Moreover, learning by osmosis has traditionally been the method to move the novice toward expert capabilities by gradually gaining experience while observing experts’ reasoning. However, it seems likely that explicit teaching of clinical reasoning could make this process quicker and more effective. In this sense, an increased need for training and clinical knowledge along with the skill to apply the acquired knowledge is necessary. Students should learn disease pathophysiology, treatment concepts, and interdisciplinary team communication developing clinical decision-making through case-series-derived knowledge combining associative and procedural learning processes such as “Vienna Summer School on Oncology” ( 35 ).

Moreover, a refinement of the training of communicative skills is needed. Improving communication skills training for medical students and physicians should be the university’s primary goal. In fact, adequate communication leads to a correct diagnosis with 76% accuracy ( 36 ). The main challenge for students and physicians is the ability to respond to patients’ individual needs in an empathic and appreciated way. In this regard, it should be helpful to apply qualitative studies through the adoption of a semi-structured or structured interview using face-to-face in-depth interviews and e-learning platforms which can foster interdisciplinary learning by developing expertise for the clinical reasoning and decision-making in each area and integrating them. They could be effective tools to develop clinical reasoning and decision-making competencies and acquire effective communication skills to manage the relationship with patient ( 37 – 40 ).

Clinical reasoning ways

Clinical reasoning is complex: it often requires different mental processes operating simultaneously during the same clinical encounter and other procedures for different situations. The dual-process theory describes how humans have two distinct approaches to decision-making ( 41 ). When one uses heuristics, fast-thinking (system 1) is used ( 42 ). However, complex cases need slow analytical thinking or both systems involved ( 15 , 43 , 44 ). Slow thinking can use different ways of reasoning: deductive, hypothetic-deductive, inductive, abductive, probabilistic, rule-based/categorical/deterministic, and causal reasoning ( 9 ). We think that abductive and causal reasoning need further explanation. Abductive reasoning is necessary when no deductive argument (from general assumption to particular conclusion) nor inductive (the opposite of deduction) may be claimed.

In the real world, we often face a situation where we have information and move backward to the likely cause. We ask ourselves, what is the most plausible answer? What theory best explains this information? Abduction is just a process of choosing the hypothesis that would best explain the available evidence. On the other hand, causal reasoning uses knowledge of medical sciences to provide additional diagnostic information. For example, in a patient with dyspnea, if considering heart failure as a casual diagnosis, a raised BNP would be expected, and a dilated vena cava yet. Other diagnostic possibilities must be considered in the absence of these confirmatory findings (e.g., pneumonia). Causal reasoning does not produce hypotheses but is typically used to confirm or refute theories generated using other reasoning strategies.

Hypothesis generation and modification using deduction, induction/abduction, rule-based, causal reasoning, or mental shortcuts (heuristics and rule of thumbs) is the cognitive process for making a diagnosis ( 9 ). Clinicians develop a hypothesis, which may be specific or general, relating a particular situation to knowledge and experience. This process is referred to as generating a differential diagnosis. The process we use to produce a differential diagnosis from memory is unclear. The hypotheses chosen may be based on likelihood but might also reflect the need to rule out the worst-case scenario, even if the probability should always be considered.

Given the complexity of the involved process, there are numerous causes for failure in clinical reasoning. These can occur in any reasoning and at any stage in the process ( 33 ). We must be aware of subconscious errors in our thinking processes. Cognitive biases are subconscious deviations in judgment leading to perceptual distortion, inaccurate assessment, and misleading interpretation. From an evolutionary point of view, they have developed because, often, speed is more important than accuracy. Biases occur due to information processing heuristics, the brain’s limited capacity to process information, social influence, and emotional and moral motivations.

Heuristics are mind shortcuts and are not all bad. They refer to experience-based techniques for decision-making. Sometimes they may lead to cognitive biases (see above). They are also essential for mental processes, expressed by expert intuition that plays a vital role in clinical practice. Intuition is a heuristic that derives from a natural and direct outgrowth of experiences that are unconsciously linked to form patterns. Pattern recognition is just a quick shortcut commonly used by experts. Alternatively, we can create patterns by studying differently and adequately in a notional way that accumulates information. The heuristic that rules out the worst-case scenario is a forcing mind function that commits the clinician to consider the worst possible illness that might explain a particular clinical presentation and take steps to ensure it has been effectively excluded. The heuristic that considers the least probable diagnoses is a helpful approach to uncommon clinical pictures and thinking about and searching for a rare unrecognized condition. Clinical guidelines, scores, and decision rules function as externally constructed heuristics, usually to ensure the best evidence for the diagnosis and treatment of patients.

Hence, heuristics are helpful mind shortcuts, but the exact mechanisms may lead to errors. Fast-and-frugal tree and take-the-best heuristic are two formal models for deciding on the uncertainty domain ( 45 ).

In the recent times, clinicians have faced dramatic changes in the pattern of patients acutely admitted to hospital wards. Patients become older and older with comorbidities, rare diseases are frequent as a whole ( 46 ), new technologies are growing in a logarithmic way, and sustainability of the healthcare system is an increasingly important problem. In addition, uncommon clinical pictures represent a challenge for clinicians ( 47 – 50 ). In our opinion, it is time to claim clinical reasoning as a crucial way to deal with all complex matters. At first, we must ask ourselves if we have lost the teachings of ancient masters. Second, we have to rethink medical school courses and training ones. In this way, cognitive debiasing is needed to become a well-calibrated clinician. Fundamental tools are the comprehensive knowledge of nature and the extent of biases other than studying cognitive processes, including the interaction between fast and slow thinking. Cognitive debiasing requires the development of good mindware and the awareness that one debiasing strategy will not work for all biases. Finally, debiasing is generally a complicated process and requires lifelong maintenance.

We must remember that medicine is an art that operates in the field of science and must be able to cope with uncertainty. Managing uncertainty is the skill we have to develop against an excess of confidence that can lead to error. Sound clinical reasoning is directly linked to patient safety and quality of care.

Data availability statement

Author contributions.

SC and CA drafted the work and revised it critically. Both authors have approved the submission of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Open access
• Published: 24 May 2024

Integration of case-based learning and three-dimensional printing for tetralogy of fallot instruction in clinical medical undergraduates: a randomized controlled trial

• Jian Zhao 1   na1 ,
• Xin Gong 1   na1 ,
• Jian Ding 1 ,
• Kepin Xiong 2 ,
• Kangle Zhuang 3 ,
• Rui Huang 1 ,
• Shu Li 4 &
• Huachun Miao 1  

BMC Medical Education volume  24 , Article number:  571 ( 2024 ) Cite this article

Metrics details

Case-based learning (CBL) methods have gained prominence in medical education, proving especially effective for preclinical training in undergraduate medical education. Tetralogy of Fallot (TOF) is a congenital heart disease characterized by four malformations, presenting a challenge in medical education due to the complexity of its anatomical pathology. Three-dimensional printing (3DP), generating physical replicas from data, offers a valuable tool for illustrating intricate anatomical structures and spatial relationships in the classroom. This study explores the integration of 3DP with CBL teaching for clinical medical undergraduates.

Sixty senior clinical medical undergraduates were randomly assigned to the CBL group and the CBL-3DP group. Computed tomography imaging data from a typical TOF case were exported, processed, and utilized to create four TOF models with a color 3D printer. The CBL group employed CBL teaching methods, while the CBL-3DP group combined CBL with 3D-printed models. Post-class exams and questionnaires assessed the teaching effectiveness of both groups.

The CBL-3DP group exhibited improved performance in post-class examinations, particularly in pathological anatomy and TOF imaging data analysis ( P  < 0.05). Questionnaire responses from the CBL-3DP group indicated enhanced satisfaction with teaching mode, promotion of diagnostic skills, bolstering of self-assurance in managing TOF cases, and cultivation of critical thinking and clinical reasoning abilities ( P  < 0.05). These findings underscore the potential of 3D printed models to augment the effectiveness of CBL, aiding students in mastering instructional content and bolstering their interest and self-confidence in learning.

The fusion of CBL with 3D printing models is feasible and effective in TOF instruction to clinical medical undergraduates, and worthy of popularization and application in medical education, especially for courses involving intricate anatomical components.

Peer Review reports

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease(CHD) [ 1 ]. Characterized by four structural anomalies: ventricular septal defect (VSD), pulmonary stenosis (PS), right ventricular hypertrophy (RVH), and overriding aorta (OA), TOF is a focal point and challenge in medical education. Understanding anatomical spatial structures is pivotal for learning and mastering TOF [ 2 ]. Given the constraints of course duration, medical school educators aim to provide students with a comprehensive and intuitive understanding of the disease within a limited timeframe [ 3 ].

The case-based learning (CBL) teaching model incorporates a case-based instructional approach that emphasizes typical clinical cases as a guide in student-centered and teacher-facilitated group discussions [ 4 ]. The CBL instructional methods have garnered widespread attention in medical education as they are particularly appropriate for preclinical training in undergraduate medical education [ 5 , 6 ]. The collection of case data, including medical records and examination results, is essential for case construction [ 7 ]. The anatomical and hemodynamic consequences of TOF can be determined using ultrasonography, computed tomography (CT), and magnetic resonance imaging techniques. However, understanding the anatomical structures from imaging data is a slow and challenging psychological reconstruction process for undergraduate medical students [ 8 ]. Three-dimensional (3D) visualization is valuable for depicting anatomical structures [ 9 ]. 3D printing (3DP), which creates physical replicas based on data, facilitates the demonstration of complex anatomical structures and spatial relationships in the classroom [ 10 ].

During the classroom session, 3D-printed models offer a convenient means for hands-on demonstration and communication, similar to facing a patient, enhancing the efficiency and specificity of intra-team communication and discussion [ 11 ]. In this study, we printed TOF models based on case imaging data, integrated them into CBL teaching, and assessed the effectiveness of classroom instruction.

Research participants

The study employed a prospective, randomized controlled design which received approval from the institutional ethics committee. Senior undergraduate students majoring in clinical medicine at Wannan Medical College were recruited for participation based on predefined inclusion criteria. The researchers implemented recruitment according to the recruitment criteria by contacting the class leaders of the target classes they had previously taught. Notably, these students were in their third year of medical education, with anticipation of progressing to clinical courses in the fourth year, encompassing Internal Medicine, Surgery, Obstetrics, Gynecology, and Pediatrics. Inclusion criteria for participants encompassed the following: (1) proficient communication and comprehension abilities, (2) consistent attendance without absenteeism or truancy, (3) absence of failing grades in prior examinations, and (4) capability to conscientiously fulfill assigned learning tasks. Exclusion criteria were (1) absence from lectures, (2) failure to complete pre-and post-tests, and (3) inadequate completion of questionnaires. For their participation in the study, Students were provided access to the e-book “Localized Anatomy,” authored by the investigators, as an incentive for their participation. Voluntary and anonymous participation was emphasized, with participants retaining the right to withdraw from the study at any time without providing a reason.

The study was conducted between May 1st, 2023, and June 30, 2023, from recruitment to completion of data collection. Drawing upon insights gained from a previous analogous investigation which yielded an effect size of 0.95 [ 10 ]. Sample size was computed, guided by a statistical consultant, with the aim of 0.85 power value, predicated on an effect size of 0.8 and a margin of error set at 0.05. A minimum of 30 participants per group was calculated using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), resulting in the recruitment of a total of 60 undergraduate students. Each participant was assigned an identification number, with codes placed in boxes. Codes drawn from the boxes determined allocation to either the CBL group or the CBL-3DP group. Subsequently, participants were randomly assigned to either the CBL group, receiving instruction utilizing the CBL methodology, or the CBL-3DP group, which received instruction integrating both CBL and 3D Printed models.

Printing of TOF models

Figure  1 A shows the printing flowchart of the TOF models. A typical TOF case was collected from the Yijishan Hospital of Wannan Medical College. The CT angiography imaging data of the case was exported. Mimics Research 20.0 software (Mimics Innovation Suite version 20, Materialize, Belgium) was used for data processing. The cardiovascular module of the CT-Heart tool was employed to adjust the threshold range, independently obtain the cardiac chambers and vessels, post-process the chambers and vessels to generate a hollow blood pool, and merge it with the myocardial volume to construct a complete heart model. The file was imported into Magics 24.0 software (version 24.0; Materialize, Belgium) for correction using the Shell tool page. After repairs, the model entered the smoothing page, where tools such as triangular surface simplification, local smoothing, refinement and smoothing, subdivision of components, and mesh painting were utilized to achieve varying degrees of smoothness. Finally, optimized data were obtained and exported as stereolithography (STL) files. An experienced cardiothoracic surgeon validated the anatomical accuracy of the digital model.

The STL files were imported into a 3D printer (J401Pro; Sailner 3D Technology, China) for model printing. This printer can produce full-color medical models using different materials. The models were fabricated using two distinct materials: rigid and flexible. Both materials are suitable for the observational discussion of the teaching objectives outlined in our study. From the perspective of observing pathological changes in the TOF, there is no significant difference between the two materials.

Experimental flow chart of this study. A TOF model printing flow chart. B The instructional framework

Teaching implementation

Figure  1 B illustrates the instructional framework employed in this study. One week preceding the class session, all the students were tasked with a 30-minute self-study session, focusing on the theoretical content related to TOF as outlined in the Pediatrics and Surgery textbooks, along with a review of pertinent academic literature. Both groups received co-supervision from two basic medicine lecturers boasting over a decade of teaching experience, alongside a senior cardiothoracic surgeon. Teaching conditions remained consistent across groups, encompassing uniform assessment criteria and adherence to predefined teaching time frames, all conducted in a Project-Based Learning (PBL) classroom at Wannan Medical College. Additionally, a pre-course examination was administered to gauge students’ preparedness for self-study.

In adherence to the curriculum guidelines, the teaching objectives aimed to empower students to master TOF’s clinical manifestations, diagnostic modalities, and differential diagnoses, while acquainting them with treatment principles and surgical methodologies. Additionally, the objectives sought to cultivate students’ clinical reasoning abilities and problem-solving skills. the duration of instruction for the TOF theory session was standardized to 25 min. The didactic content was integrated with the TOF case study to construct a coherent pedagogical structure.

During the instructional session, both groups underwent teaching utilizing the CBL methodology. Clinical manifestations and case details of TOF cases were presented to stimulate students’ interest and curiosity. Subsequently, the theory of TOF, including its etiology, pathogenesis, pathologic anatomy, clinical manifestations, diagnostic methods, and therapeutic interventions, was briefly elucidated. Emphasis was then placed on the case, wherein selected typical TOF cases were explained, guiding students in analysis and discussion. Students were organized into four teams under the instructors’ supervision, fostering cooperative learning and communication, thereby deepening their understanding of the disease through continuous inquiry and exploration (Fig.  2 L). In the routinely equipped PBL classroom with standard heart models (Fig.  2 J, K), all students had prior exposure to human anatomy and were familiar with these models. Both groups were provided with four standard heart models for reference, while the CBL-3DP group received additional four 3D-printed models depicting TOF anomalies, enriching their learning experience (Fig.  2 D, G). After the lesson, summarization, and feedback sessions were conducted to consolidate group discussions’ outcomes, evaluate teaching effectiveness, and assess learning outcomes.

Heart models utilized in instructional sessions. A External perspective of 3D digital models. B, C Cross-sectional views following trans-septal sagittal dissection of the 3D digital model (PS: Pulmonary Stenosis; OA: Overriding Aorta; VSD: Ventricular Septal Defect; RVH: Right Ventricular Hypertrophy). D External depiction of rigid 3D printed model. E, F Sagittal sections of the rigid 3D printed model. G External portrayal of flexible 3D printed model. H, I Sagittal sections of the flexible 3D printed model. J, K The normal heart model employed in the instruction of the CBL group. L Ongoing classroom session

Teaching effectiveness assessment

Following the instructional session, participants from the two groups underwent a theoretical examination to assess their comprehension of the taught material. This assessment covered domains such as pathological anatomy, clinical manifestations, imaging data interpretation, diagnosis, and treatment relevant to TOF. Additionally, structured questionnaires were administered to evaluate the efficacy of the pedagogical approach employed. The questionnaire consisted of six questions designed to gauge participants’ understanding of the teaching content, enhancement of diagnostic skills, cultivation of critical thinking and clinical reasoning abilities, bolstering of confidence in managing TOF cases, satisfaction with the teaching mode, and satisfaction with the CBL methodology.

The questionnaire employed a 5-point Likert scale to gauge responses, with 5 indicating “strongly satisfied/agree,” 4 for “satisfied/agree,” 3 denoting “neutral,” 2 reflecting “dissatisfied/disagree,” and 1 indicating “strongly dissatisfied/disagree.” It comprised six questions, with the initial two probing participants’ knowledge acquisition, questions 3 and 4 exploring satisfaction regarding enhanced competence, and the final two assessing satisfaction with teaching methods and modes. Additionally, participants were encouraged to provide suggestions at the end of the questionnaire. To ensure the questionnaire’s validity, five esteemed lecturers in basic medical sciences with more than 10 years of experience verified its content and assessed its Content Validity Ratio and Content Validity Index to ensure alignment with the study’s objectives.

Statistical analysis

Statistical analyses were conducted utilizing GraphPad Prism 9.0 software. Aggregate score data for both groups were presented as mean ± standard deviation (x ± s). The gender comparisons were analyzed with the chi-square (χ2) test, while the other variables were compared using the Mann-Whitney U test. The threshold for determining statistical significance was set at P  < 0.05.

Three-dimensional printing models

After configuring the structural colors of each component (Fig.  2 A, B, C), we printed four color TOF models using both rigid and flexible materials, resulting in four life-sized TOF models. Two color TOF models were created using rigid materials (Fig.  2 D, E, F). These models, exhibiting resistance to deformation, and with a firm texture, smooth and glossy surface, and good transparency, allowing visibility of the internal structures, were deemed conducive to teaching and observation. We also fabricated two color TOF models using flexible materials (Fig.  2 G, H, I), characterized by soft texture, opacity, and deformability, allowing for easy manipulation and cutting. It has potential utility beyond observational purposes. It can serve as a valuable tool for simulating surgical interventions and may be employed to create tomographic anatomical specimens. In this study, both material models were suitable for observation in the classroom. The participants were able to discern the four pathological changes characteristic of TOF from surface examination or cross-sectional analysis.

Baseline characteristics of the students

In total, 60 students were included in this study. The CBL group comprised 30 students (14 males and 16 females), with an average age of (21.20 ± 0.76) years. The CBL-3DP group consisted of 30 students (17 males and 13 females) with an average age of 20.96 years. All the students completed the study procedures. There were no significant differences in age, sex ratio, or pre-class exam scores between the two groups ( P  > 0.05), indicating that the baseline scores between the two groups were comparable (Table  1 ).

Theoretical examination results

All students completed the research procedures as planned. The post-class theoretical examination encompassed assessment of pathological anatomy, clinical presentations, imaging data interpretation, diagnosis, and treatment pertinent to TOF. Notably, no statistically significant disparities were observed in the scores on clinical manifestations, diagnosis and treatment components between the cohorts, as delineated in Table  2 . Conversely, discernible distinctions were evident whereby the CBL-3DP group outperformed the CBL group notably in pathological anatomy, imaging data interpretation, and overall aggregate scores ( P  < 0.05).

Results of the questionnaires

All the 60 participants submitted the questionnaire. Comparing the CBL and CBL-3DP groups, the scores from the CBL-3DP group showed significant improvements in many areas. This included satisfaction with the teaching mode, promotion of diagnostic skills, bolstering of self-assurance in managing TOF cases, and cultivation of critical thinking and clinical reasoning abilities (Fig.  3 B, C, D, E). All of which improved significantly ( P  < 0.05 for the first aspects and P  < 0.01 for the rest). However, the two groups were not comparable ( P  > 0.05) in terms of understanding of the teaching content and Satisfaction with the CBL methodology (Fig.  3 A, F).

Upon completion of the questionnaires, participants were invited to proffer recommendations. Notably, in the CBL group, seven students expressed challenges in comprehending TOF and indicated a need for additional time for consolidation to enhance understanding. Conversely, within the CBL-3DP group, twelve students advocated for the augmentation of model repertoire and the expansion of disease-related data collection to bolster pedagogical efficacy across other didactic domains.

Five-point Likert scores of students’ attitudes in CBL ( n  = 30) and CBL-3DP ( n  = 30) groups. A Understanding of teaching content. B Promotion of diagnostic skills. C Cultivation of critical thinking and clinical reasoning abilities. D Bolstering of self-assurance in managing TOF cases. E Satisfaction with the teaching mode. F Satisfaction with the CBL methodology. ns No significant difference, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

TOF presents a significant challenge in clinical practice, necessitating a comprehensive understanding for effective diagnosis and treatment [ 12 ]. Traditional teaching methods in medical schools have relied on conventional resources such as textbooks, 2D illustrations, cadaver dissections, and radiographic materials to impart knowledge about complex conditions like TOF [ 13 ]. However, the limitations of these methods in fully engaging students and bridging the gap between theoretical knowledge and practical application have prompted a need for innovative instructional approaches.

CBL has emerged as a valuable tool in medical education, offering students opportunities to engage with authentic clinical cases through group discussions and inquiry-based learning [ 14 ]. By actively involving students in problem-solving and decision-making processes, CBL facilitates the application of theoretical knowledge to real-world scenarios, thus better-preparing students for future clinical practice [ 15 ]. Our investigation revealed that both groups of students exhibited comparable levels of satisfaction with the CBL methodology, devoid of discernible disparities.

CHD presents a formidable challenge due to the intricate nature of anatomical anomalies, the diverse spectrum of conditions, and individual variations [ 16 ]. Utilizing 3D-printed physical models, derived from patient imaging data, can significantly enhance comprehension of complex anatomical structures [ 17 ]. These models have proven invaluable in guiding surgical planning, providing training for junior or inexperienced pediatric residents, and educating healthcare professionals and parents of patients [ 18 ]. Studies indicate that as much as 50% of pediatric surgical decisions can be influenced by the insights gained from 3D printed models [ 19 ]. By providing tangible, anatomically accurate models, 3D printing offers a unique opportunity for people to visualize complex structures and enhance their understanding of anatomical intricacies. Our study utilized full-color, to-scale 3D printed models to illustrate the structural abnormalities associated with TOF, thereby enriching classroom sessions and facilitating a deeper comprehension of the condition.

Comparative analysis between the CBL-3DP group and the CBL group revealed significant improvements in post-test performance, particularly in pathological anatomy and imaging data interpretation. Additionally, questionnaire responses indicated higher levels of satisfaction and confidence among students in the CBL-3DP group, highlighting the positive impact of incorporating 3D printed models into the learning environment, improving the effectiveness of CBL classroom instruction. Despite the merits, our study has limitations. Primarily, participants were exclusively drawn from the same grade level within a single college, possibly engendering bias owing to shared learning backgrounds. Future research could further strengthen these findings by expanding the sample size and including long-term follow-up to assess the retention of knowledge and skills. Additionally, the influence of the 3D models depicting a normal heart on the learning process and its potential to introduce bias into the results warrants consideration, highlighting a need for scrutiny in subsequent studies.

As medical science continues to advance, the need for effective teaching methods becomes increasingly paramount. Our study underscores the potential of combining active learning approaches like CBL with innovative technologies such as 3D printing to enhance teaching effectiveness, improve knowledge acquisition, and foster students’ confidence and enthusiasm in pursuing clinical careers. Moving forward, further research and integration of such methodologies are essential for meeting the evolving demands of medical education, especially in areas involving complex anatomical understanding.

Conclusions

Integrating 3D-printed models with the CBL method is feasible and effective in TOF instruction. The demonstrated success of this method warrants broad implementation in medical education, particularly for complex anatomical topics.

Data availability

All data supporting the conclusions of this research are available upon reasonable request from the corresponding author.

Apitz C, Webb GD, Redington AN. Tetralogy of Fallot. Lancet. 2009;374:1462–71.

Article   Google Scholar  

Ghosh RM, Jolley MA, Mascio CE, Chen JM, Fuller S, Rome JJ, et al. Clinical 3D modeling to guide pediatric cardiothoracic surgery and intervention using 3D printed anatomic models, computer aided design and virtual reality. 3D Print Med. 2022;8:11.

Chakrabarti R, Wardle K, Wright T, Bennie T, Gishen F. Approaching an undergraduate medical curriculum map: challenges and expectations. BMC Med Educ. 2021;21:341.

Donkin R, Yule H, Fyfe T. Online case-based learning in medical education: a scoping review. BMC Med Educ. 2023;23:564.

Novack JP. Designing cases for case-based immunology teaching in large medical school classes. Front Immunol. 2020;11:995.

Chen HC, Van Den Broek WES, Ten Cate O. The case for use of entrustable professional activities in undergraduate medical education. Acad Med. 2015;90:431–6.

Wang M, Sun Z, Jia M, Wang Y, Wang H, Zhu X, et al. Intelligent virtual case learning system based on real medical records and natural language processing. BMC Med Inf Decis Mak. 2022;22:60.

Yoo S-J, Thabit O, Kim EK, Ide H, Yim D, Dragulescu A, et al. 3D printing in medicine of congenital heart diseases. 3D Print Med. 2015;2:3.

Yammine K, Violato C. A meta-analysis of the educational effectiveness of three-dimensional visualization technologies in teaching anatomy. Anat Sci Educ. 2015;8:525–38.

Miao H, Ding J, Gong X, Zhao J, Li H, Xiong K, et al. Application of 3D-printed pulmonary segment specimens in experimental teaching of sectional anatomy. BMC Surg. 2023;23:109.

Sun Z, Wong YH, Yeong CH. Patient-specific 3D-printed low-cost models in medical education and clinical practice. Micromachines (Basel). 2023;14:464.

Downing TE, Kim YY. Tetralogy of Fallot: general principles of management. Cardiol Clin. 2015;33:531–41. vii–viii.

Jia X, Zeng W, Zhang Q. Combined administration of problem- and lecture-based learning teaching models in medical education in China: a meta-analysis of randomized controlled trials. Med (Baltim). 2018;97:e11366.

McLean SF. Case-based learning and its application in medical and health-care fields: a review of worldwide literature. J Med Educ Curric Dev. 2016;3:JMECD.S20377.

Zeng N, Lu H, Li S, Yang Q, Liu F, Pan H, et al. Application of the combination of CBL teaching method and SEGUE framework to improve the doctor-patient communication skills of resident physicians in otolaryngology department. Bmc Med Educ. 2024;24:201.

Sun Z. Patient-specific 3D-printed models in pediatric congenital heart disease. Children. 2023;10:319.

Meyer-Szary J, Luis MS, Mikulski S, Patel A, Schulz F, Tretiakow D, et al. The role of 3D printing in planning complex medical procedures and training of medical professionals—cross-sectional multispecialty review. IJERPH. 2022;19:3331.

Sun Z, Wee C. 3D printed models in cardiovascular disease: an exciting future to deliver personalized medicine. Micromachines-basel. 2022;13:1575.

Valverde I, Gomez-Ciriza G, Hussain T, Suarez-Mejias C, Velasco-Forte MN, Byrne N, et al. Three-dimensional printed models for surgical planning of complex congenital heart defects: an international multicentre study. Eur J Cardio-thorac. 2017;52:1139–48.

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Acknowledgements

We extend our sincere appreciation to the instructors and students whose invaluable participated in this study.

This paper received support from the Education Department of Anhui Province, China (Grant Numbers 2022jyxm1693, 2022jyxm1694, 2022xskc103, 2018jyxm1280).

Author information

Jian Zhao and Xin Gong are joint first authors.

Authors and Affiliations

Department of Human Anatomy, Wannan Medical College, No.22 West Wenchang Road, Wuhu, 241002, China

Jian Zhao, Xin Gong, Jian Ding, Rui Huang & Huachun Miao

Department of Cardio-Thoracic Surgery, Yijishan Hospital of Wannan Medical College, Wuhu, China

Kepin Xiong

Zhuhai Sailner 3D Technology Co., Ltd., Zhuhai, China

Kangle Zhuang

School of Basic Medical Sciences, Wannan Medical College, Wuhu, China

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Contributions

Jian Zhao and Huachun Miao designed the research. Jian Zhao, Xin Gong, Jian Ding, Kepin Xiong designed the tests and questionnaires. Kangle Zhuang processed the imaging data and printed the models. Xing Gong and Kepin Xiong implemented the teaching. Jian Zhao and Rui Huang collected the data and performed the statistical analysis. Jian Zhao and Huachun Miao prepared the manuscript. Shu Li and Huachun Miao revised the manuscript. Shu Li provided the Funding acquisition. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Shu Li or Huachun Miao .

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This investigation received ethical approval from the Ethical Committee of School of Basic Medical Sciences, Wannan Medical College (ECBMSWMC2022-1-12). All methodologies adhered strictly to established protocols and guidelines. Written informed consent was obtained from the study participants to take part in the study.

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Zhao, J., Gong, X., Ding, J. et al. Integration of case-based learning and three-dimensional printing for tetralogy of fallot instruction in clinical medical undergraduates: a randomized controlled trial. BMC Med Educ 24 , 571 (2024). https://doi.org/10.1186/s12909-024-05583-z

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Received : 03 March 2024

Accepted : 21 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1186/s12909-024-05583-z

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