Interpreting Progress Feedback to Guide Clinical Decision-Making in Children’s Mental Health Services

Abstract

Measurement feedback systems (MFSs) can help improve clinical outcomes by enhancing clinical decision-making. Unfortunately, limited information exists to guide the use and interpretation of data from MFSs. This study examined the amount of data that would provide a reasonable and reliable prediction of a client's rate of symptomatology in order to help inform clinical decisionmaking processes. Results showed that use of more data predicted greater levels of accuracy. However, there were diminishing returns on the ability for additional data to improve predictive accuracy. Findings inform efforts to develop guidelines on the interpretation of data from MFSs.

Decision making and the use of evidence-based practice: Is the three-legged stool balanced?

“…At present, there are limited data to guide treatment selection based on clinical characteristics, biomarkers, or genetic variations that reliably predict differential effectiveness or adverse effects for specific treatments for very common presenting problems such as depression or anxiety (Hahn et al, 2015; Schneider, Arch, & Wolitzky-Taylor, 2015; Simon & Perlis, 2010), and when there are predictions, they are based on numerous considerations that result in algorithms or empirically based indices to inform treatment selection (DeRubeis et al, 2014; Huibers et al, 2015; Kraemer, 2013). While tools to use assessment of patient progress and functioning to guide treatment have been developed in the past decade (cf., Lambert, 2007; Tsai, Moskowitz, Brown, Park, & Chorpita, 2016), none of this research or these strategies was used or mentioned by anyone in our sample. Additionally, no clinicians described selecting a treatment approach based on a patient’s preference, despite the current emphasis of patient-oriented care.…”

“…Advances in research to guide treatment matching as well as better packaging of advances in the field may increase the perceived value of standardized assessment. Furthermore, practical tools to assist clinicians in assessment and treatment planning (Lambert, 2015; Tsai et al, 2016) and to support consideration of each aspect of evidence based practice may result in greater use of the research to guide practice.…”

Community-Based Providers’ Selection of Practices for Children and Adolescents With Comorbid Mental Health Problems

“…The rationale behind modular treatment design is to allow for flexibility in the content and coordination of therapy practices in order to enhance the fit between EBTs and the contexts in which they are ultimately being applied—whether those contexts include diagnostic comorbidity, emergent life events (e.g., Chorpita, Korathu-Larson, Knowles, & Guan, 2014), poor response to treatment (e.g., Tsai, Moskowitz, Brown, Park, & Chorpita, 2016), or other complexities that may arise during the course of therapy. Specifically, modular treatment design separates practice content (e.g., exposures for anxiety, relaxation skills, rewards) from practice coordination (e.g., implement exposures for anxiety after creating a fear hierarchy, implement a rewards system if a client seems unmotivated to participate in therapy) to allow therapy procedures to be applied in an individualized manner without compromising the delivery of or empirical support behind the treatment (Chorpita, Daleiden, & Weisz, 2005).…”