Academic journal article Journal of Mental Health Counseling

Case Conceptualization and Treatment Planning: Investigation of Problem-Solving and Clinical Judgment

Academic journal article Journal of Mental Health Counseling

Case Conceptualization and Treatment Planning: Investigation of Problem-Solving and Clinical Judgment

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). …

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