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Article Excerpt
This investigation examined the cognitive factors that influence case conceptualization (CC) and treatment planning (TP) tasks among experienced mental health professionals. A thinking aloud process-tracing strategy was used to identify problem-solving styles and clinical judgment strategies used by 25 licensed psychologists, clinical mental health counselors, and clinical social workers while responding to a standardized case conceptualization and treatment planning task. Cluster analysis revealed a four cluster solution that differentiated among treatment planning scores of these clinicians. SPSS discriminant analyses identified (a) three problem-solving styles (i.e., differentiation, integration, affiliation) that correctly predicted cluster membership in 96% of cases, and (b) three clinical judgment strategies (i.e., minimal, complex, heuristic) that correctly predicted cluster membership for all of these clinicians. Implications of these findings for training and research are presented.
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Case conceptualization and treatment planning are frequent and universal clinical judgment tasks of mental health practitioners (Benbenishty & Treistman, 1998; Falvey, 2001; Garb, 1998; Prieto & Scheel, 2002; Strohmer & Leierer, 2000; Yennie, 1997). How clinicians elicit client information, weigh the value of that input, formulate hypotheses, and utilize cognitive schemas to inform their understanding of clients and their treatment needs has been the subject of an extensive empirical literature in cognitive psychology over the past 50 years. Unfortunately, that research has failed to lessen the considerable variability that exists between what is known about clinical decision-making and what is practiced. In fact, it has been suggested that experienced clinicians may be subject to more rather than less bias in their judgments than are novices (Strohmer & Leierer). Biased judgments are unacceptable among all health-related disciplines, as third party accountability standards increasingly demand evidence-based rather than intuitive or apprenticeship-based decision approaches (Chessare & Lieu, 1998). Educators, clinicians, and supervisors would benefit from a science of clinical reasoning to identify and improve decision-making processes during assessment and treatment planning tasks.
The literature on clinical judgment and information processing has provided some consensus regarding decision-making under uncertainty. For example, it is evident that human information-processing capacity is quite limited. The mind uses a variety of heuristics (i.e., cognitive shortcuts) to handle the information overload that is characteristic of complex judgments. These heuristics tend to reduce the complexity of problems by assessing probabilities based on a limited number of variables across many cases at the expense of considering innumerable variables germane to one individual.
Large caseloads and limited time for assessment and treatment planning would seem to favor such cognitive shortcuts. However, their impact on clinician performance remains unclear.
Studies support the prevalence of a handful of heuristics as potentially powerful explanatory tools in clinical judgment research (Garb, 1998; Hogarth, 1987; Moore, Smith, & Gonzalez, 1997). One common heuristic includes representativeness: assessing the probability that an event or symptom (e.g., reported hopelessness) belongs to a category (e.g., major depressive disorder) by the degree to which it reflects prototypical features of that category (Kahneman & Tversky, 1982). Although this heuristic permits a rapid matching of symptoms to diagnoses, it neglects population base rates and can lead to fundamental attribution errors (Nisbett & Ross, 1980). Another common heuristic is confirmatory bias, in which clinicians seek information that supports their initial hypothesis while ignoring data that may refute that hypothesis (Arkes, 1991). This heuristic simplifies the complexity of information gathering but may lead to premature foreclosure of diagnostic and treatment decisions. Other heuristics commonly found to contribute to inferential errors in clinical judgment include availability (i.e., the ease with which similar cases or events are recalled), illusory correlation (i.e., correlating traits and symptoms based on personal belief in the absence of objective criteria), primacy effects (i.e., rapid judgments based on very little data), and anchoring effects (i.e., influence attributable to the order of presentation of information on judgments) (Butcher & Schofield, 1984; Dawes, 1994; Kleinmuntz, 1990; Strohmer & Leierer, 2000; Tutin, 1993). While professional counselors acknowledge that heuristics provide necessary and often useful templates for clinical decision-making, a surprising number of clinicians, even experts, are unable to verbalize the presence or potential impact of these specific strategies on their decisions (Garb).
Research has also established that clinicians use organized knowledge structures (i.e., schemas) to process information (Anderson, 1996; Mahoney, 1998; Prieto & Scheel, 2002). Schemas may be based on some theoretical standard (i.e., clinical orientation), formal decision aids (i.e., DSM criteria), empirical evidence, or clinical experience. The validity of these schemas is obviously critical to the accuracy of judgments. Unfortunately, to a surprising degree, clinicians?even experts?are unable to report what schemas they use in their judgment processes (Faust, 1986; Garb, 1998). Research has failed to make explicit the intervening stages in clinical problem-solving styles that mediate between client data and outcome predictions (Strohmer & Leierer, 2000).
These findings leave educators responsible for clinical training and practice in a quandary. To the extent that practitioners cannot identify the judgment heuristics and cognitive schemas on which they base decisions and therefore, lack consistent feedback on the outcomes of those decisions, it seems unlikely that even experts will (a) learn from their experience, (b) transfer effective schemas to future problem-solving tasks, or (c) impart these schemas to mental health trainees. In fact, a conclusion of this extensive area of research is that experience alone does not improve clinical judgment (Dawes, 1994; Garb, 1998; Turk & Salovey, 1988).
In response to the failure of experience to predict judgment outcomes among mental health professionals, studies have attempted to make explicit the cognitive factors that may contribute to performance on judgment tasks. These studies have examined factors such as patterns of inquiry, how clinicians select and synthesize client information (Falvey, 2001); debiasing strategies in diagnostic decision-making (Hill & Ridley, 2001); and cognitive complexity, the ability to integrate large amounts of multidimensional information (Spengler & Strohmer, 1994). Another construct that has been examined is integrative complexity (Ladany, Marotta, & Muse-Burke, 2001; Suedfeldt, Tetlock, & Streufert, 1992), which consists of two components: differentiation, the ability to consider alternative perspectives regarding a phenomenon, and integration, the ability to develop complex connections among differentiated components.
Based on the above body of research, cognitive strategies that have demonstrated potential for improving judgment outcomes include (a) needing to simultaneously consider several alternative diagnoses, explanations, and treatment plans; (b) addressing environmental as well as internal factors influencing client behaviors; (c) decreasing reliance on memory alone in decision-making tasks; and (d) using formal decision aids such as diagnostic criteria, norms, and base rates to improve accuracy (Arkes, 1991; Falvey, 2001; Gambrill, 1990; Garb, 1998; Turk & Salovey, 1986). These findings offer general guidelines to assist in training mental health clinicians in case conceptualization and treatment planning.
Building upon this knowledge base, the current investigation seeks to provide more specificity regarding the cognitive factors that influence case conceptualization (CC) and treatment planning (TP) tasks. Using an intensive process-tracing design and a standardized mental health case, we evaluated transcribed text from a thinking aloud procedure and structured follow-up interviews, as well as written conceptualizations and treatment plans for the case. This design permitted clinician performance to be classified into (a) problem-solving styles (i.e., how clinicians gather and evaluate case information) and (b) clinical judgment strategies (i.e., how clinicians make judgments based on case information). Process variables related to the realistic constraints of evaluating clients and planning treatment that mental health professionals face in their daily clinical practice (e.g., time spent on task components, number of alternative diagnoses considered) were also addressed in the design of this study. Four research questions that guided this exploratory study included the following:
1. What impact does cognitive focus during the case review process have on case conceptualization (CC) and treatment planning (TP) tasks?
2. What problem-solving styles are evident during the case review, and how do they effect CC and TP?
3. What clinical judgment strategies best characterize clinician approaches to CC and TP?
4. Can CC and TP scores be predicted by a classification scheme that distinguishes among problem-solving styles and clinical judgment strategies?
METHOD
Participants
Twenty-five clinicians, nominated by professional mental health colleagues for their specialized work with children, agreed to participate in this study. The sample included 13 females and 12 males with a mean age of 45.1 years (SD = 10.5), and an average of 13 years of clinical experience (SD = 4.9). All participants were Caucasian, licensed either as psychologists (n = 10), clinical mental health counselors (n = 7), or clinical social workers (n = 8). Primary clinical orientations were reported as psychodynamic (n = 10), cognitive-behavioral (n = 6), family systems (n = 7), or humanistic (n = 2). Work settings included inpatient (n = 1), community mental health (n = 8), and private practice (n = 16). Of these clinicians, 19 (76%) reported in follow-up interviews that this standardized case was very similar to actual cases they encountered in their practice; the other 6 participants reported it was fairly similar.
Instruments
The Clinical Treatment Planning Simulation: Case 1-B (CTPS; Falvey, 1994) was one of two instruments used in this investigation. This simulation, which is one of four CTPS cases, represents a client with ADHD, a common clinical disorder among youth (APA, 2000). The case was constructed with input...
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