Journal of Nursing Education

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Knowledge-Driven Problem-Solving Models in Nursing Education

Krystyna M Cholowski, RN, MEdStud; Lorna K S Chan, BEd, PhD

Abstract

ABSTRACT

This paper compares the hypothetico-deductive model of clinical problem solving commonly used in current nurse education and practice with the knowledge-driven problemsolving model (Bordage, Grant, & Marsden, 1990). It is argued that the knowledge-driven model provides a more complete account of the processes involved in clinical problem solving. The knowledge-driven model emphasizes the organization and availability of relevant content knowledge stored in memory as the prime determinant of clinical problem solving. This contention is discussed in relation to the development of a clinical problem-solving task for nursing students and its implications for nursing curricula.

Abstract

ABSTRACT

This paper compares the hypothetico-deductive model of clinical problem solving commonly used in current nurse education and practice with the knowledge-driven problemsolving model (Bordage, Grant, & Marsden, 1990). It is argued that the knowledge-driven model provides a more complete account of the processes involved in clinical problem solving. The knowledge-driven model emphasizes the organization and availability of relevant content knowledge stored in memory as the prime determinant of clinical problem solving. This contention is discussed in relation to the development of a clinical problem-solving task for nursing students and its implications for nursing curricula.

Introduction

In recent years, the question of how best to teach nursing students to develop problem-solving skills has become an issue of major importance to nurse educators. Many efforts to promote problem-solving skills in nursing have been founded on certain common assumptions about the nature of clinical problem solving. Among these is the assumption that problem-solving skills are dependent on a set of generalizable processes, largely independent of relevant content knowledge (Holbert & Abraham, 1988; Neufeld, Norman, Feightner, & Barrows, 1981). Thus, differences in performance are considered to be the result of a lack of understanding of the principles underlying problem-solving processes. These assumptions have more recently come under question (Carnevali, 1984; Grant & Marsden, 1987; Groen & Patel, 1988; Pardue, 1987; Toliver, 1988). In particular, recent research (Balla, Biggs, Gibson, & Chang, 1990) has challenged the idea that problem-solving processes alone are sufficient to explain the development of expertise and has begun to emphasize the role of content knowledge in explaining clinical problem-solving performance.

In this paper, two approaches to clinical problem solving in nursing will be considered. We compared the hypotheticodeductive model of clinical problem solving commonly used in current nurse education and practice with knowledgedriven problem-solving models (Bordage et al., 1990). It will be suggested that these two models represent fundamentally different approaches to the study and teaching of clinical problem solving. Whereas the hypotheticodeductive model (HDM) represents a process of deductive reasoning that sustains definable procedures, knowledgedriven models (KDM) focus on the organization and availability of relevant content knowledge stored in memory as the key component of clinical problem solving (Bordage et al.). It will be argued that KDMs provide a more complete account of the processes involved in clinical problem solving. This contention is discussed in relation to the development of a clinical problem-solving task for teaching nursing students.

Hypothetico-Deductive Model (HDM): A Framework for Teaching

The hypothetico-deductive model has been described as a general heuristic that draws on Bruner's account of discovery learning and promotes the notion of self-directed learning (Gordon, 1980; Laurillard, 1989; Norman, 1988). The purpose of instruction under this model is to promote the acquisition of the problem-solving processes by moving the student through several cycles of data identification, hypothesis generation, information gathering, problem reformulation, and possible solution (Tanner, Padrick, Westfall, & Putzier, 1987). Under the assumptions of the HDM, the goal of self-directed learning is to enable the students to develop the skills necessary to identify where their knowledge is inadequate, then obtain and use the content knowledge needed to complete the task (Clarke, 1988). The acquisition of the hypothetico-deductive processes is the necessary condition for expert problem solving as these processes are seen as generalizable across all problems.

The HDM provides a framework for teaching students to tackle clinical problems systematically, and it gives students an easily comprehensible explanation of problemsolving behavior (Chase, 1988; Gross, Takazawa, & Rose, 1987; Jones, 1988; Norman, 1988). The processes espoused by the model have become linked with problem-based learning and have been advanced and developed in nurse education (Little & Ryan, 1988).

The widespread use of the HDM, however, has been questioned by some researchers who maintain that the content knowledge available to the individual for problem solving may be a more important factor in predicting competence (Bordage et al., 1990; Corcoran, Narayan, & Moreland, 1988; De Voider & De Grave, 1989; Groen & Pa tel, 1985). Researchers working from this perspective suggest that what helps to improve students' problem solving is finding better ways of presenting the content knowledge to be acquired and finding ways of helping students structure their knowledge (Bowers & McCarthy, 1993; Claessen & Boshuizen, 1985).

Knowledge-Driven Models (KDM): A Framework for Teaching

Knowledge-driven problem-solving models are based on the assumption that students typically try to understand new information on the basis of existing content knowledge (Glaser, 1984; Smith, 1992). This proposition emphasizes the structure and accessibility of relevant content knowledge as the prime features of clinical problem solving. Efficiently stored content knowledge is structured into networks (or schémas) of information interconnected by rational links (Thompson, Ryan, & Kitzman, 1990). According to Putnam (1987), it is this richly interconnected structure of knowledge that constitutes understanding and allows the individual to recognize and match clinical data with the appropriate schema. One of the attributes of expertise is the ability to make effective use of this interconnected structure, allowing the rapid recognition of important clinical information and the appropriate use of this information in a specific clinical context (Benner, 1984; Hughes & Young, 1992; Putnam).

Many different techniques have been used to examine various aspects of knowledge structure and their relationship to clinical problem solving. Subsequently, a large array of KDMs have been introduced into the literature. A model of "semantic networks" describes problem solving in terms of abstract diagnostic concepts that subsume clinical data and act to generate meaningful relationships between data items (Bordage & Lemieux, 1991). Schmidt, Norman, and Boshuizen (1990) refer to "illness scripts* to describe the extensive schematic networks that develop with expertise. Bordage and Zacks (1984) refer to a problem-solving method where "prototype categorization* is based on overlapping attributes of information rather than distinctive features. In a similar vein, Norman, Brooks, Allen, and Rosenthal (1990) describe problem solving in terms of "instant-based categorization* where the currently processed information is tied to a rich network of similar examples.

Although each of these models retains its own focus on problem solving, their common underlying theme is the importance of knowledge structure in clinical problem solving. Generally, these models suggest that competence in clinical problem-solving results from a highly personalized body of knowledge that is structured in such a way that it can be easily retrieved from memory to be applied to the case at hand (Carr, 1991).

Research Findings Related to Clinical Problem Solving

Much of the research investigating clinical problem solving has been reported using in situ analytic techniques. Evidence for the idea of a generic hypotheticodeductive problem-solving process has been reported by Elstein, Shulman, and Sprafka (1978) and Barrows and colleagues (Barrows & Bennett, 1972; Neufeld et al., 1981). While these studies have clearly demonstrated the use of a set of hypothetico-deductive processes in problem solving, the research has not been able to account for différences in outcome between expert and novice problem solving (Tanner et al., 1987). In situ studies comparing the problem-solving processes used by expert and novice problem solvers have consistently found that regardless of experience, all individuals use the same set of problemsolving processes. Clearly, however, expert problem solving is more efficient and yields better outcomes than novice problem solving (Balla et al., 1990; Corcoran, 1986; Grant & Marsden, 1988; Holden & Klinger, 1988; Itano, 1989).

According to Kaufman and Patel (1991), the results of studies comparing the problem-solving processes used by expert and novice problem solvers indicate that differences primarily relate to the quality and accuracy of the hypotheses generated and the accuracy of the diagnosis. These differences, however, are not a feature of the hypotheticodeductive process itself, but are more likely to be an indication of how efficiently the clinical data have been acted on (Bordage & Lemieux, 1991). Consistent with these concerns is the wide literature reporting only weak correlations between the components of problem-solving processes (Bornor & Durrell, 1987; Norman, 1988; Norman, Tugwell, Feightner, Muzzin, & Jacoby, 1985; Neufeld, Norman, McAuley, Repo, & Henry, 1983; Pavin, Neufeld, Norman, Walker, & Whelan, 1979). In addition, data investigating the internal consistency and validity of measures of problem-solving processes suggest low transfer of problemsolving processes regardless of the nature of the problem or the scoring method used (Schmidt et al., 1990).

It seems, then, that hypothetico-deductive processes do not generalize with equal success across different clinical problems (Berner, 1984), and that while expert and novice problem solvers may employ the same strategies or processes in dealing with clinical problems, the processes may be used in different ways yielding different solutions. Accordingly, it may be argued that the effectiveness of the hypothetico-deductive processes may depend significantly on the richness of the underlying knowledge base used in problem solving (Claessen & Boshuizen, 1985; Tanner, 1984).

Gale, Grant, and Marsden (Gale, 1982; Gale & Marsden, 1982, 1983; Grant & Marsden, 1987, 1988) have identified consistent differences in the knowledge structures of expert and novice physicians in solving clinical problems. Grant and Marsden (1988) suggest that:

. . . the qualitative changes in knowledge that seem to occur with increasing experience begins with narrow, idiosyncratic and inappropriate memory store, through increasing breadth and similarity to the final characteristic of a perhaps slightly narrower, useful and finely tuned primary knowledge base which has been organized to respond to the demands of clinical problems and practice (p. 178).

Grant and Marsden's position implies that the development of expertise in problem solving is not merely adding to a pool of knowledge but, rather, such expertise develops as content knowledge is used in response to contextual demands. This suggests that as knowledge is used in problem solving, exemplars are built up using principles that allow the restructuring to occur. Thus, the more knowledge is used, the more sophisticated and efficient the structures become (Glaser, 1984).

Knowledge-based differences in clinical problem solving have also been identified by Bordage and associates in their research on semantic structures (Bordage, 1991; Bordage et al., 1990; Bordage & Lemieux, 1991; Bordage & Zacks, 1984). Their research is primarily concerned with how experts use their knowledge to generate highly structured representations of clinical information. This allows for reconceptualization of clinical information. Bordage et al. use the example of how "three times in the last 2 days* becomes "acute and intermittent.* In turn, these structures help to yield diagnostic decisions. Other studies (Bordage & Lemieux; Cholowski & Chan, 1992; Ramsden, Whelan, & Cooper, 1989) have found that the most successful problem solvers are those who use clinical data to build coherent networks of relationships and construct a "deeper" memory representation of the problem at hand.

Knowledge-based differences have also been documented on tasks related to clinical interview (Kaufman & Patel, 1991), recall and comprehension of clinical text (Groen & Patel, 1988; Patel, Evans, & Kaufman, 1990; Patel, Groen, & Scott, 1988), and perceptual diagnostic tasks (Lesgold et al., 1988; Norman et al., 1990). An important feature of these studies is the emphasis given to the primary role of content knowledge in explaining competence in clinical problem solving. Accordingly, this suggests that strategies to teach problem solving need to provide a mechanism through which student nurses may develop rich and well-organized knowledge structures that guide subsequent problem-solving activities (Patel, Evans, & Groen, 1989).

Teaching of Clinical Problem Solving

Silver and Marshall (1990) maintain that an important component of successful problem solving is "an adequate store of domain-relevant knowledge* (p. 266), particularly "extensive and accessible knowledge" (p. 267). It is contended that effective instruction to enhance successful problem solving is to make explicit certain implicit aspects of the knowledge needed for problem solving. These include, first, visual pattern recognition involving the rapid observation of regularity in patterns of information typically observed in clinical problems and, second, the underlying organization of relevant knowledge into hierarchies or clusters of related concepts and procedures.

Regarding pattern recognition, research indicates that certain features of a problem, once recognized, may trigger particular solution methods (Clement, 1983; Davis, 1984; Jones, 1988). Hence, drawing students' attention to regularities and patterns associated with typical clinical problems during instruction could assist students in developing pattern recognition skills that would enhance their problem-solving performance. As for knowledge organization, it is suggested that all clinical problem solving involves the retrieval of information from one's store of relevant content knowledge and that the efficient retrieval of information during problem solving may depend on the way that information is organized in long-term memory, that is, the relevant memory schema. Problem-solving research comparing experts and novices indicates that experts had much more richly connected schémas based on the underlying principles for problem representation, whereas novices had simpler and less complex schémas that focus only on surface features of problems (Chi, Glaser, & Rees, 1981; Thompson et al., 1990).

To improve problem-solving performance, then, instruction must focus on modifying the organization and structure of relevant knowledge stored in students' longterm memory. It is important to provide more organized instruction that will facilitate the development of wellstructured and, hence, more readily accessible schémas. Eylon and Reif (1984, cited in Silver & Marshall, 1990) demonstrated that students who were provided with hierarchical instruction, which stressed the way concepts were related in the particular problems being discussed, performed better on recall and problem-solving tasks than students receiving nonhierarchical instruction.

The solving of routine problems can best be explained in terms of schémas as discussed above. When encountering problems that are not readily recognizable as belonging to a particular class, the construction of appropriate problem representation is critical (Silver & Marshall, 1990; Smith, 1992). In order to solve a complex clinical problem, the clinician must construct an understanding of the problem that connects his or her store of knowledge with the task requirements of the problem. This is called problem representation.

Let us take the example of a renal nursing problem (acute poststreptococcal glomerulonephritis). In order to construct an appropriate problem representation, students need to know the structure of the kidneys and the function they serve in filtering blood, excreting waste products, and regulating the concentrations of electrolytes in extracellular fluid. Without a well-structured and accessible content knowledge of the renal system, students are unlikely to recognize the importance of symptoms such as reduced output of urine, or the potential dangers associated with a buildup of toxins in the blood. Unsuccessful problem solvers are likely to attempt to solve problems without constructing adequate memory representation of the clinical problem (Silver & Marshall, 1990). Typically, nonexperts do not make use of related content knowledge, but rather focus on current clinical data. Consequently, the data are treated in a sequential and nonintegrative fashion (e.g., coffee-colored urine, concentrated urine, passing little urine, slight edema, vital signs raised, blood tests raised). This sequential processing does not yield a full understanding of the problem nor an understanding of the relationship between clinical data and the individual's content knowledge base (Balla et al., 1990).

Expert memory representation of the problem, on the other hand, is typically organized around fundamental principles and concepts that subsume isolated details (e.g., renal failure affects the electrolyte balances, fluid volume, and blood content, which can have a far-reaching effect on all systems of the body). These principles are derived from a content knowledge base that is characterized by complex networks of interconnected knowledge.

A Knowledge-Based Problem-Solving Task

An example of a knowledge-based problem-solving task for nursing students is described below. The task aims to allow students to develop a well-structured content knowledge and the procedures to deal with that knowledge in a clinical problem-solving context (Figure). The task involves presenting a clinical case history that calls on students to generate a nursing assessment for the case described, to generate a number of nursing diagnoses, and to provide for a logical major diagnosis. The nursing assessment subtask encourages students to collect, interpret, and organize clinical information as a prelude to diagnosis. Students are then required to list four important nursing diagnoses and to select one of these as the major nursing diagnosis. This subtask calls on students to integrate their content knowledge with important clinical information in order to make an accurate and high-quality nursing diagnosis. Finally, the problem-solving task requires students to provide a detailed explanation for choosing the major nursing diagnosis. This subtask is the primary focus of the teaching strategy and engages students in the task of rationalizing and elaborating on their diagnostic decisions.

Figure. A knowledge-based problem-solving task for nursing students.

Figure. A knowledge-based problem-solving task for nursing students.

Such a problem-solving task provides the context for active and generative learning (Bransford, Sherwood, Hasselbring, Kinzer, & Williams, 1990) by students and for interactive dialogues (Brown, Campione, Reeve, Ferrara, & Palincsar, 1991) between the expert and novice, which will promote the development of a richly interconnected knowledge base. The use of questions such as "Why is the urine coffee colored?" "What could have led to the reduced output of urine?" and "What could be the consequences of passing little urine?" would prompt the linking of clinical data with relevant content knowledge and the formation of a new integrated schema. Initially, the tutor (expert) may be modeling the generation and asking such questions. Gradually, students (novices) will be able to generate and ask their own questions. Guided student-generated questioning (King, 1992) may also act to prompt higher level conceptual activity and force the novice's attention away from surface details. King suggests that the asking of "what, why, and how" questions is a useful strategy to clarify concepts and promote hierarchical structures among pieces of information.

The "think aloud" modeling and shaping processes in interactive dialogues would also involve the monitoring and evaluation of hypothesized diagnoses against reorganized clinical data and rearranged knowledge structures. Questions like "Is my diagnosis consistent with all the given data and what I know about the renal system?" require the matching and synthesis of information, old and new, from various sources. Part of the interaction of old and new knowledge includes the individual's awareness of the appropriate procedure to generate the links between the knowledge components. Accordingly, procedural knowledge (knowing how) has a role in activating, accessing, and using prior knowledge as a means for understanding new knowledge. The kinds of procedural supports that King (1992) talks about include "strategic questions" (e.g., "What are you trying to do here?" and "What do you know about the problem so far?*). Prawat (1989) argues:

Being able to create an adequate representation of the problem is only half the battle, however. One must be able to relate this representation to a previous one that resulted in correct problem solution. It is this relating of one problem situation to another that mediates access to potentially relevant conceptual and procedural knowledge (p. 16).

A key factor in accessible knowledge, therefore, is the interconnectedness of pre-existing schema, which, in turn, allows the individual to quickly identify what is important in the clinical data and how to integrate these data to make appropriate clinical decisions. In other words, the richer and more elaborate the existing knowledge structures, the greater the possibility of generating appropriate diagnostic hypotheses.

In our cited example, to adequately explain the nursing diagnosis of "altered tissue perfusion: renal, cardiopulmonary, related to renal dysfunction (NANDA)," students need to link new clinical data to relevant prior renal knowledge and also to pre-existing knowledge of other body systems. Subsequently, they need to elaborate on these connections. The critical feature in the elaborations is how and why information from various sources is brought together. For example, having recognized that the patient has a reduced output of urine and increased levels of toxins in the blood, and knowing that renal disorders give rise to alterations in the body systems, students should make connections between the two and deduce that the patient's decreased urinary output and increased levels of toxins may be related to kidney dysfunction. Subsequently, the effects of kidney dysfunction on other systems of the body should be inferred. The level of inference involved is hence reflected in the diagnosis nominated as the major diagnosis.

Clearly, the teaching strategy described calls on students to use procedural knowledge to systematically approach the clinical task (e.g., nursing assessment, diagnostic hypotheses). The task does not isolate the problem-solving processes; the emphasis is on the qualitatively different ways that stored content knowledge is used to process new clinical data. Thus, the task directly taps into the way students actually rearrange their existing content knowledge to make new kinds of knowledge structures to assist in clinical problem solving (Norman et al., 1985).

Implications for Nursing Curricula

The enhancement of nurses' clinical problem-solving performance, then, appears to focus on issues related to the fundamental process of the accessing and structuring of relevant content knowledge underlying problem-solving activity rather than just the procedures described in the HDM of clinical problem solving. Nurses must learn to elaborate on and critically analyze their ideas by giving examples and summaries and pointing out inconsistencies in their own line of reasoning on the basis of an extensive and well-structured content knowledge. Where student nurses are instructed to reflect on their ideas in meaningful and structured contexts, it seems more likely that they will develop a well-structured and accessible knowledge base that can be readily applied to clinical nursing situations (King, 1992; Reynolds, Shepard, Lapan, Kreek, & Goetz, 1990).

Tanner (1993) points out that there is little evidence of a relationship between measures of critical thinking and problem solving in nursing and suggests that ". . . there is much work to be done, both in understanding the complexity of critical thinking as it relates to nursing practice, and in developing relevant educational methods, both in teaching and outcome assessment" (p. 100).

If nursing schools are to prepare students to think critically in order to facilitate conceptual understanding as a basis for solving increasingly complex nursing problems, critical thinking and problem solving must be taught, practiced, and continually reinforced in meaningful context. Moreover, current educational research indicates that such developments in critical thinking skills are most successful when such skills are grounded in a rich and elaborate content knowledge. It would seem fruitful then that, within the context of the knowledge-based problem-solving task described earlier, problem-based nursing instruction should shift emphasis away from the processes of problem solving to the development of highly accessible and well-structured knowledge bases through the systematic provision of problem-solving tasks in meaningful contexts.

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