Guy J. Groen

Knowledge Based Solution Strategies in Medical Reasoning

The techniques of propositionol analysis are used to examine the protocols of seven cardiologists in a task involving the diagnosis of a case of acute bacterial endocarditis and an explanation of its underlying pathophysiology. It is shown that the explanations of physicians making an accurate diagnosis can be accounted for in terms of a model consisting of pure forward reasoning through a network of causal rules, actuated by relevant propositions embedded in the stimulus text. These rules appear to derive from the physicians underlying knowledge base rather than any information in the text itself. In contrast, subjects with inacurate diagnoses tend to make use of a mixture of forward and backward reasoning, beginning with a high level hypothesis and proceeding in a top-down fashion to the propositions embedded in stimulus text, or to the generation of irrelevant rules.

Medical expertise asa function of task difficulty

This paper is concerned with factors that disrupt the pattern of forward reasoning characteristic of experts with accurate performance. Two experiments are described. In the first, the performances of cardiologists, psychiatrists, and surgeons in diagnostic explanation of a clinical problem in cardiology were examined. In the second, the performances of cardiologists and endocrinologists in diagnostic explanation of clinical problems within and outside their domains of expertise were examined. The performances of researchers and practicing physicians are also compared. The results of Experiment 1 replicated earlier results regarding the relationship between forward reasoning and accurate diagnosis. There were no differences in recall as a function of expertise. Experts did not show any bias toward using specific knowledge from their own areas of expertise. The results of Experiment 2 showed that the breakdown of forward reasoning was related to the structure of the task. In particular, nonsalient cues induced some backward reasoning even in subjectswith accurate diagnoses. Some differences were also found between the types of explanation used by researchers and practitioners. The practitioners referred more to clinical components in their explanations, whereas the researchers focused more on the biomedical components.

Medical problem-solving: some questionable assumptions.

Summary. This paper questions the idea that expert doctors use the hypothetico‐deductive method when developing diagnoses of routine clinical cases. Up to now, this has not been justified by empirical evidence but by two indirect arguments. The first is that it is the standard procedure of scientific method. The second is that it is supported by research in cognitive psychology comparing the problem‐solving behaviour of experts and novices. It is argued in this paper that both areas have been misinterpreted. In particular, the evidence from research in cognitive psychology on expert‐novice comparisons indicates that the use of the hypothetico‐deductive method is a characteristic of novices rather than experts. Experts use what are called strong methods, which are dependent on a highly elaborated and structured knowledge base. It is concluded that a considerable amount of research on the nature of such strong methods in expert clinical reasoning is needed before any confident claims can be made regarding the use of the hypothetico‐deductive or any other method.

Developmental accounts of the transition from medical student to doctor: some problems and suggestions

Summary. This paper is concerned with the difficulties in integrating the results of recent cognitive research on expert‐novice in clinical reasoning. The focus is on the issue of stage theories as a means of achieving such integration. Objections to the use of such theories in developmental psychology are reviewed. In light of these objections, it is argued that it is important to distinguish between stage theories and stage‐like phenomena. It is concluded that a stage theory is not likely to provide adequate integration. Some alternative explanations, which include both cognitive and non‐cognitive factors, are discussed.

Cognitive Frameworks for Clinical Reasoning: Application for Training and Practice

The application of cognitive psychology to medical education has been hampered by a failure to distinguish between specific theories that explain psychological phenomena and general frameworks that provide psychological rationales for educational situations. This has resulted in considerable misunderstanding by practitioners wishing to apply the results of this research. For example, Barrows (1990) criticizes cognitive research on medical problem solving as having been primarily restricted to clinical diagnosis and hence largely irrelevant to the overall workup of a patient. What he seems to have in mind is an encompassing cognitive framework in which all the psychological aspects of the workup can be covered, and that only experiments examining this total framework should be taken seriously. However, there are compelling reasons to believe that a cognitive framework and a cognitive theory are inherently quite different and that an understanding of the relationship between the two is important for applications of psychological research to educational practice. As Anderson (1983) puts it, a framework is a general pool of constructs for understanding a domain, but it is not tightly enough organized to constitute a predictive theory.

Real Versus Artificial Expertise: The Development of Cognitive Models of Clinical Reasoning

The purpose of this paper is to give an account of our approach to the study of clinical reasoning in medicine. This research has been in the domain of cognitive psychology rather than artificial intelligence and it is important to begin by stressing that they are two somewhat separate areas with their own paradigmatic approaches. However, there exists a well-established tradition of cross-fertilization of ideas between the two areas. There are, in general, two ways in which this cross-fertilization can take place. One is to develop a model that operates as both a psychological theory and an AI model, the most prominent recent example being SOAR (Newell, in press). The second, which is far more common, is to make use of the ideas and techniques in one area to develop a theory in the other, resulting in a complementary evolution of parallel areas rather than the development of a homogeneous theory. The predominance of this latter approach stems from the fact that the demands made upon theories are quite different in the two areas. The primary test of a psychological theory lies in its relationship to empirical data. AI does not suffer from this constraint. On the other hand, AI models need to satisfy a requirement of precision of definition that tends to be impossible to achieve in psychological models except within highly delimited domains.

A propositional analyst's assistant

A system (PAN) that is designed to assist both the inexperienced and the experienced user in performing propositional analysis is described. The system is implemented in UCSD Pascal on the Apple II and Apple He microcomputers.

Causal and Conditional Reasoning

an endocrinology problems and transcribed their remarks. When the physicians attempted to relate multiple pieces of information in the protocols, Joseph and Patel identified this action as a link. They identified two types of links; causal and conditional. They distinguished between these links based on their &dquo;strengths of implication.&dquo; According to their definition, conditional links imply only