Improving Measurement Feedback Systems for Measurement-Based Care

Abstract

Measurement-based care (MBC) is increasingly recognized as a beneficial addition to behavioral healthcare but is nevertheless underutilized by clinicians. Measurement feedback systems (MFS) are a class of health information technologies developed to increase adoption and facilitate MBC. These systems often include a diverse array of features, though there is little knowledge about the influence they might have on MBC. To that end, this vignette-based study tested the impact of four MFS features (progress graph, expected change trajectory, alert, clinical decision support) on key, clinician-driven MBC processes (progress assessment accuracy and treatment adjustments) in the context of different clinical scenarios (patient deterioration, no progress, and remission). Analyses revealed MFS features differentially impacted clinicians’ progress assessment accuracy, how likely they were to make a treatment change, and their specific treatment choices. However, which feature was most impactful varied depending on the clinical scenario. When asked to reflect on their answers, clinicians reported the graphs influenced their progress assessments and treatment choices significantly more than the other three features. However, when asked which MFS feature(s) they would prefer to use in their own clinical work, the majority stated they would like to use all of them.