Clinician Engagement In Feedback Informed Treatment (FIT) and Patient Outcomes

Aim, Methods, Results, DiscussionAim: This paper presents analyses of outcome data for 317 therapists treating 14,161 patients over a three-year period to determine if therapists’ effect sizes increased over time. Each therapist treated at least 5 patients in each of their first two years of using outcome measures. Multiple outcome questionnaires were employed. All measures also included a brief alliance scale administered concurrently. Method: A severity adjusted effect size was calculated for each patient using intake scores and diagnostic group as predictors. The mean severity adjusted effect size for each therapist was calculated for their first and second years of using the outcome tools. This was done using a hierarchical linear model to control for sample size in each year, with a minimum sample of 5 cases in each year. Therapist engagement in receiving feedback was measured by counting the number of times the therapist logged into the online platform to view their results in each of the two years.Results: Therapists who logged in the view their data at least 24 times in the second year (n=123; 37%) averaged .92 effect size compared to .82 effect size for those seen by therapists who reviewed their results less frequently (n=214; 63%). Login frequency during the first year was not predictive of effect size during the second year. Discussion: The data provides evidence that effect sizes can trend upwards with measurement and feedback. Therapists’ engagement in receiving feedback appears to increase the likelihood of effect size gain.

The Role of Practice-Based Evidence and Feedback Informed Treatment for Improving Therapy Outcomes

Attrition rates and deterioration of counselling and psychotherapy clients are two major concerns for those delivering psychological therapies across differential modalities. While a variety of correlations are said to contribute to attrition and deterioration such as, client, therapist and clinical level, identifying and improving outcomes for this cohort of people in routine practice is difficult. Even with the addition of hundreds of empirically supported treatments added to the profession, outcomes have not improved in line with these new approaches. Methods to limit the extent of poor outcomes has been established in the extent literature, thus, practice-based evidence is put forward focusing on Feedback Informed Treatment (FIT).

Introducing Feedback Informed Preference Accommodation (FIPA): A Case Study in Clinical Practice

Psychotherapy is a successful modality for those who engage in and complete a course of treatment. However, attrition rates and negative outcomes make up a significant and under discussed proportion of clinicians’ case load in routine practice. Innovative and novel methods to address these issues have been identified within the extant literature. However, their uptake can be impacted by issues such as utility and brevity. The present paper seeks to establish a framework for integrating Feedback Informed Treatment (FIT) and the Cooper-Norcross Inventory of Preferences (C-NPI) in clinical practice. That is, using the C-NPI for initial preference accommodation and following this up on a session to session basis to monitor the process and outcome of therapy. An overview of both approaches is provided, and a rationale for their integration elucidated. The author terms this integration, Feedback Informed Preference Accommodation (FIPA). A Case Study is put forward to demonstrate this process in clinical practice.

Feedback Informed Treatment

Feedback Informed Treatment (FIT) involves clinicians gathering on-going real time feedback from clients to determine what is working and what is not working in therapy. This chapter provides an overview of feedback informed treatment (FIT), including its historical evolution, guiding principles, and central tenets. It lays out how FIT is applied and integrated into practice, including how client feedback is obtained. The authors describe how it can guide treatment decisions in a variety of settings and with diverse populations. A case example illustrates FIT in practice. Finally, the authors review the outcome research on FIT and share ideas about its future directions.