Medical Expertise Research Paper

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Investigations of medical expertise endeavor to characterize the ways experienced clinical practitioners and medical scientists perform on a range of experimental and ‘real-world’ tasks. Expertise research employs a cross-sectional approach that contrasts subjects at various levels of training and experience in view to understand the mediators of skilled performance. Medicine is a complex, knowledge-rich, and illstructured domain. Ill-structured indicates that the initial states, the definite goal state, and the necessary constraints are unknown at the beginning of the problem-solving process.

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1. What is Medical Expertise?

The cognitive characteristics of expertise are a highly organized and differentiated knowledge base, refined technical skills, and perceptual capabilities that allow experts to see the world in distinctly different ways from nonexperts. These are some of the cognitive mediators of high-level performance as exemplified by experts. Experts are also well versed in disciplinary discourse and cultural practices, and can smoothly coordinate action in team settings. The development of expertise is a function of extensive domain-related practice and training, typically exceeding 10 years (Ericsson and Smith 1991).

Although exceptional performance is one of the distinguishing characteristics of expert, it is problematic to define expertise in terms of performance. In cognitive research, expertise is sometimes defined relative to other cohorts. For example, writers who have 5 years of experience are more expert than newcomers to the discipline. More commonly, expertise is defined according to socially-sanctioned criteria. For example, Grand Master chess players are awarded such status when they achieve a certain Elo rating on the basis of tournament play. The medical expert is typically a board-certified medical specialist in subdomains such as cardiology and endocrinology with some years of experience. Like other professional domains, medical practice is highly specialized and medical expertise is narrowly constituted. Research has documented gradations in performance as experts work in areas increasingly distal to their domain. For example, a typical cardiologist is a genuine expert in cardiology, has substantial aptitude in respiratory medicine, and would be rather challenged to treat dermatological patients. Medical knowledge has proliferated in recent years and by necessity, this has led to greater specialization and narrower bandwidths of expertise.

2. Medical Cognition

The term medical cognition refers to studies of cognitive processes, such as problem-solving, reasoning, decision making, comprehension, memory retrieval, and perception in medical practice or in experimental tasks representative of medical practice. These studies often focus on contrasting subjects of varying levels of expertise on sets of tasks. The subjects in these studies include medical students, physicians at varying levels of training and experience, clinicalspecialists, and biomedical scientists. There are two principle experimental traditions in the study of medical cognition: a decision-making and judgment approach in which a subjects’ decisions are contrasted with a normative model, indicating optimal choices under conditions of uncertainty and a problemsolving protocol analytic tradition in which the focus is on characterizing cognitive performance on reasoning and explanation tasks. This research paper is principally concerned with the latter approach. For a more detailed discussion, see Patel et al. (1994).

3. Diagnostic Reasoning

There are three classes of interdependent cognitive tasks that comprise clinical medicine: diagnoses, therapeutics, and patient monitoring and management. Diagnostic reasoning has been the focal point of much research in medical cognition. Diagnoses can be defined as the process of identifying and classifying malfunctions in a system. In the process of diagnosis, a physician makes a series of inferences derived from observations including patient’s history, physical examination findings, laboratory tests and responses to therapeutic interventions. The foundational studies of Elstein et al. (1978) on diagnostic reasoning employed an information-processing analysis of performance influenced by Newell and Simon’s (1972) seminal work on problem solving. They characterized the process of diagnostic reasoning as involving a hypothetico-deductive process in which sets of diagnostic hypotheses (typically 4 or 5) are weighed against pieces of available evidence. These investigations, as well as others that employed a similar approach, found no differences between students and clinicians or between physicians of varying levels of competency in their use of diagnostic strategies. The characterization of hypothetico-deductive reasoning as an expert strategy seemed anomalous as it was widely regarded as a weak method of problem-solving used in problems where little knowledge was available.

As the field of expertise research increasingly focussed on knowledge-rich domains such as medicine and physics, the focus of analysis shifted away from domain-general strategies to the organization of expert knowledge and its effect on reasoning. Subsequent investigations documented systematic differences in reasoning strategies between expert and nonexpert physicians. They found that when solving familiar problems, experts’ employed a form of reasoning characterized by the generation of inferences from the available patient data to the diagnostic hypotheses. This forward-directed reasoning strategy was in contrast to backward-directed reasoning, where the direction of inference is from a diagnosis to the explanation of given patient data. Highly skilled physicians, who are not experts in the problem domain, are more likely to use a mixture of forward and backward-directed reasoning. Backward reasoning, which is characterized by the generating and testing of hypotheses, is akin to the hypothetico-deductive method. Pure forward reasoning is only successful when working on familiar problems. Backward reasoning is considerably less efficient and makes heavy demands on working memory because one has to keep track of goals and hypotheses. This strategy is more likely to be used when domain knowledge is insufficient. Forward reasoning is disrupted under conditions of problem complexity and uncertainty. The differential use of strategies and skills is a function of better organized knowledge structures that emerge as a consequence of medical training and clinical experience.

4. Conceptual Understanding in Biomedicine

This section addresses studies that have investigated the role of basic science concepts in clinical explanations and research that has focused on subjects’ understanding of physiological systems. The first area of research has examined the ways in which biomedical knowledge is used by subjects at different levels of expertise on problems of varying complexity. Patel and coworkers have demonstrated that basic science concepts are used sparingly by experts on routine problems in their own domain of expertise. Intermediate subjects as well as novices tend to introduce more basic science concepts into their explanations of clinical problems. These concepts appear to be integral to the development of explanatory coherence. However, at certain stages in the acquisition of expertise, the use of biomedical concepts can actually impede diagnostic reasoning, resulting in incoherent explanations and misleading diagnostic hypotheses. In more complex clinical problems, mastery of biomedical knowledge is correlated with discriminating between relevant and less relevant problem features and selecting among competing diagnostic hypotheses.

The biomedical sciences are prototypical of domains of advanced knowledge acquisition. These domains are characterized by complex subject matter necessitating substantial prior knowledge as well as standards of coherence that exceed earlier forms of knowledge acquisition. Several studies have documented that medical students exhibit significant misconceptions in areas as diverse as cellular respiration, genetics, and cardiovascular physiology. Misconceptions emerge as a function of both formal and informal learning. These misunderstandings are not the mere result of a single piece of wrong knowledge. They reflect networks of knowledge that consist of elements that may be correct, partially correct, or substantially flawed. The study of misconceptions is oriented towards the detailed analysis of knowledge structures in order to identify the multiple sources of knowledge that comprise them. Instructional interventions have been developed to target specific misconceptions with the goal of developing more generative and robust understanding of biomedical systems and concepts (Feltovich et al. 1989).

There are competing theories on how best to characterize the acquisition of biomedical expertise. The knowledge encapsulation theory suggests that as a function of clinical experience, biomedical concepts become increasingly subsumed under clinical concepts at higher levels of abstraction with the same explanatory power (Boshuizen and Schmidt 1992). For example, explaining symptoms of chest pain and shortness of breath on exertion can invoke a more detailed explanation of circulatory function or can be more succinctly expressed in terms of symptoms leading to a particular pathophysiological condition. With increasing levels of expertise, physicians generate explanations at higher levels of generality using fewer biomedical concepts. An alternative theory expresses growth of understanding in terms of progressions of mental models. A mental model is a dynamic knowledge structure that is composed to make sense of experience and to reason across space and time. Reasoning with mental models involves a process of mental simulation and can be used to generate predictions about future states or derive causal explanations to account for the development of a particular problem (Patel et al. 1994).

5. Dynamic Decision-making in Medical Settings

Early medical decision-making research typically contrasted physician performance with normative statistical models. These studies documented several weaknesses in physicians’ decisions, most notably insensitivity to probabilities and the lack of a rational approach for weighing evidence. Subsequent research examined factors such as how problems are framed, and how uncertainty and risk affect decision outcomes. This research was shaped by the highly influential work of Tversky and Kahneman (1974). Human decision makers are constrained by limitations of information processing including selective perceptual capabilities, limited attentional resources, and errors associated with memory retrieval. These limitations guide the decision maker to construct simplified decision models and to use heuristics. Heuristics are rules of thumb that direct decision-making and sometimes result in biases that cause particular patterns of errors. Research in medical decision making has similarly found that physicians exhibit a range of biases that lead to suboptimal decisions. In general, this research portrays the expert decision maker as a fallible reasoner with marked deficiencies in decision analysis. This is at variance with other perspectives on expertise. Conventional decision-making research has been critiqued for the kinds of artificial tasks employed and the limited perspective on characteristics of domain competence such as domain knowledge and cognitive skills involved in making difficult decisions.

An emerging area of research concerns investigations of cognition in dynamic real-world environments. Naturalistic decision-making research differs from conventional decision research that typically focuses on a single decision event among a fixed set of alternatives in a stable environment. In realistic settings, decisions are embedded in a broader social context involving multiple participants and are part of an extended complex decision action process. Stress, time pressure, and communication patterns among different individuals critically affect decision processes. Naturalistic medical decision-making research within this tradition has been carried out in areas such as anesthesiology, intensive care medicine, critical care nursing, and emergency telephone consultation (e.g., Gaba 1992, Patel et al. 1996). These complex social settings all involve situations of varying levels of urgency and the smooth coordination of decisions among individuals with different kinds of expertise (e.g., cardiologists, nurses, and pharmacists). For example, three principle objectives in caring for patients are first to stabilize the patient, then to identify and treat the underlying disorder, and finally to plan a longerterm course of action. The information gathering process as well as knowledge is distributed among several team members. There are some commonalties across these investigations including how level of urgency affects decision-making strategies. Under conditions of high urgency, decisions and actions are taken with minimal justification and deliberation and follow a pattern of satisficing. In addition, senior team members such as the attending physician are essentially responsible for the course of action taken. Under less urgent conditions, multiple options may be considered and deliberated, and the decision process is somewhat more democratic in nature. Decisions leading to actions, for example to administer a certain drug, have immediate consequences (positive and negative) and iteratively inform future decisions. Recent research has also begun to characterize the factors that contribute to successful collaboration and performance among medical teams. This research suggests the need for a view of medical expertise that situates the individual practitioner within a particular social context and recognizes that although knowledge and skill are critical determinants of expertise, the ability to employ distributed cognitive resources (e.g., the knowledge and memory of other individuals), coordinate decision-action cycles, monitor continuously changing situations, and when necessary, offload information to others are equally valued attributes.


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  3. Ericsson K A, Smith J 1991 Toward a General Theory of Expertise: Prospects and Limits. Cambridge University Press, New York
  4. Feltovich P J, Spiro R, Coulson R L 1989 The nature of conceptual understanding in biomedicine: The deep structure of complex ideas and the development of misconceptions. In: Evans D A, Patel V L (eds.) Cogniti e Science in Medicine: Biomedical Modeling. MIT Press, Cambridge, MA, pp. 113–72
  5. Gaba D 1992 Dynamic decision-making in anesthesiology: Cognitive models and training approaches. In: Evans D A, Patel V L (eds.) Ad anced Models of Cognition for Medical Training and Practice. Springer-Verlag, Berlin
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