Empirically and clinically useful decision making in psychotherapy: Differential predictions with treatment response models

Wolfgang Lutz*, Stephen M. Saunders, Scott C. Leon, Zoran Martinovich, Joachim Kosfelder, Dietmar Schulte, Klaus Grawe, Sven Tholen

*Corresponding author for this work

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research networks. This article presents a strategy to disaggregate patients into homogeneous subgroups to generate optimal expected treatment response profiles, which can be used to predict and track the progress of patients in different treatment modalities. The study was based on data from 618 diagnostically diverse patients treated with either a cognitive-behavioral treatment protocol (n = 262) or an integrative cognitive-behavioral and interpersonal treatment protocol (n = 356). The validity of expected treatment response models to predict treatment in those 2 protocols for individual patients was evaluated. The ways such a procedure might be used in outpatient centers to learn more about patients, predict treatment response, and improve clinical practice are discussed.

Original languageEnglish (US)
Pages (from-to)133-141
Number of pages9
JournalPsychological assessment
Volume18
Issue number2
DOIs
StatePublished - Jun 1 2006

Keywords

  • Adaptive decision making
  • Differential predictions
  • Expected treatment response
  • Outcomes management
  • Patient-focused research

ASJC Scopus subject areas

  • Clinical Psychology
  • Psychiatry and Mental health

Fingerprint Dive into the research topics of 'Empirically and clinically useful decision making in psychotherapy: Differential predictions with treatment response models'. Together they form a unique fingerprint.

  • Cite this