Feedback-informed treatment in clinical practice: Reaching for excellence.

2017 ◽  
Keyword(s):  
Clinical Practice ◽  
Informed Treatment ◽  
Feedback Informed Treatment

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

2020 ◽  
Author(s):  
Daryl Mahon
Keyword(s):  
Clinical Practice ◽  
Routine Practice ◽  
Attrition Rates ◽  
Extant Literature ◽  
Case Load ◽  
Initial Preference ◽  
Process And Outcome ◽  
Informed Treatment ◽  
Feedback Informed Treatment

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.

scholarly journals Feedback-informed treatment in emergency psychiatry; a randomised controlled trial

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Flip Jan van Oenen ◽  
Suzy Schipper ◽  
Rien Van ◽  
Robert Schoevers ◽  
Irene Visch ◽  
...  
Keyword(s):  
Randomised Controlled Trial ◽  
Controlled Trial ◽  
Emergency Psychiatry ◽  
Informed Treatment ◽  
Randomised Controlled ◽  
Feedback Informed Treatment

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

2020 ◽  
Author(s):  
George (Jeb) S Brown ◽  
Christophe Cazauvieilh
Keyword(s):  
Effect Size ◽  
Hierarchical Linear Model ◽  
Outcome Data ◽  
Effect Sizes ◽  
First Year ◽  
Multiple Outcome ◽  
Informed Treatment ◽  
The Mean ◽  
Second Year ◽  
Feedback Informed Treatment

Abstract: 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.

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