To estimate the variability in outcomes attributable to therapists in clinical practice, the authors analyzed the outcomes of 6,146 patients seen by approximately 581 therapists in the context of managed care. For this analysis, the authors used multilevel statistical procedures, in which therapists were treated as a random factor. When the initial level of severity was taken into account, about 5% of the variation in outcomes was due to therapists. Patient age, gender, and diagnosis as well as therapist age, gender, experience, and professional degree accounted for little of the variability in outcomes among therapists. Whether or not patients were receiving psychotropic medication concurrently with psychotherapy did affect therapist variability. However, the patients of the more effective therapists received more benefit from medication than did the patients of less effective therapists.

A number of systems provide feedback regarding client progress and experience of the therapeutic alliance to clinicians. Available evidence indicates that access to such data improves retention and outcome for clients most at risk for treatment failure. Over the last several years, the team at the Institute for the Study of Therapeutic Change has worked to develop an outcome management system that not only provides valid and reliable feedback, but also is as user-friendly as possible for therapists and consumers. In this article, we describe the system and summarize current research findings.

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This study estimates pretreatment-posttreatment effect size benchmarks for the treatment of major depression in adults that may be useful in evaluating psychotherapy effectiveness in clinical practice. Treatment efficacy benchmarks for major depression were derived for 3 different types of outcome measures: the Hamilton Rating Scale for Depression (M. A. Hamilton, 1960A. Hamilton, , 1967, the Beck Depression Inventory (A. T. Beck, 1978;A. T. Beck & R. A. Steer, 1987), and an aggregation of low reactivity-low specificity measures. These benchmarks were further refined for 3 conditions: treatment completers, intent-to-treat samples, and natural history (wait-list) conditions. The study confirmed significant effects of outcome measure reactivity and specificity on the pretreatment-posttreatment effect sizes. The authors provide practical guidance in using these benchmarks to assess treatment effectiveness in clinical settings.