Vorhersage individueller psychotherapieverläufe

Background: Patients/clients, insurance companies as well as professional organizations and the public health legislative are getting increasingly concerned about the integration of psychometric data collection and the monitoring of psychotherapeutic services. In addition to more traditional evaluation and cost-effectiveness studies, there is a need for decision tools to support adaptive and selective indication procedures in daily practice. Objective: In this paper, we develop an empirical operationalization of such a system, which can provide systematic information to the therapist about each patient's progress in therapy. Methods: Growth modeling techniques were used to determine the influence of initial patient characteristics to treatment progress. Furthermore ongoing treatment process information was used to determine adaptive models of the course of treatment. The study is based on longitudinal data of 890 patients and a subsample of 75 patients. The course for treatment was psychometrically docu-mented for all patients. Results: The results of these analyses allow the prediction of individual patient progress based on initial characteristics as well as a continuous adaption of these original predictions based on the actual course of treatment. Conclusions: The integration of these tools into psychotherapeutic practice can support adaptive and selective indication.