Improving measurement feedback systems for measurement-based care

Abstract

Objectives: Measurement feedback systems (MFS) are a class of health information technologies developed to facilitate measurement-based care. The individual clinical decision support features within MFS are diverse and their influence on clinicians is largely unknown. This study tested the impact of MFS features on clinicians’ progress assessments and treatment decisions in different scenarios. Method: Clinicians (n = 299) were randomly assigned to view one of six combinations of the following MFS features: graph, expected change trajectory line, alert, and treatment suggestions. The assigned feature combination was paired with three vignettes and clinical data representing three clinical scenarios: patient deterioration, no progress, and approaching remission. Clinicians answered questions after each vignette, and at the conclusion. Results: MFS features differentially impacted clinicians’ progress assessment accuracy, their likelihood of making a treatment change, and their treatment choices. Which feature was most impactful varied depending on the clinical scenario. Clinicians reported graphs influenced their assessments and choices significantly more than the other features, and the majority stated they would prefer to use all of the features. Conclusions: Specific MFS features impact clinicians’ assessments and choices to greater degrees, and the impact of those features can be influenced by the clinical state of the patient.