Patients’ and Psychologists’ Preferences for Feedback Reports on Expected Mental Health Treatment Outcomes: A Discrete-Choice Experiment

Abstract

In recent years, there has been an increasing focus on routine outcome monitoring (ROM) to provide feedback on patient progress during mental health treatment, with some systems also predicting the expected treatment outcome. The aim of this study was to elicit patients’ and psychologists’ preferences regarding how ROM system-generated feedback reports should display predicted treatment outcomes. In a discrete-choice experiment, participants were asked 12–13 times to choose between two ways of displaying an expected treatment outcome. The choices varied in four different attributes: representation, outcome, predictors, and advice. A conditional logistic regression was used to estimate participants’ preferences. A total of 104 participants (68 patients and 36 psychologists) completed the questionnaire. Participants preferred feedback reports on expected treatment outcome that included: (a) both text and images, (b) a continuous outcome or an outcome that is expressed in terms of a probability, (c) specific predictors, and (d) specific advice. For both patients and psychologists, specific predictors appeared to be most important, specific advice was second most important, a continuous outcome or a probability was third most important, and feedback that includes both text and images was fourth in importance. The ranking in importance of both the attributes and the attribute levels was identical for patients and psychologists. This suggests that, as long as the report is understandable to the patient, psychologists and patients can use the same ROM feedback report, eliminating the need for ROM administrators to develop different versions.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study

“…Although this research was aimed at making predictions, rather than explaining relations, we used LASSO regression to inform clinicians about how the algorithm works. In the health care setting, this is important as health care professionals often want to understand which parameters affect and how they contribute to a prediction [ 33 ]. By looking at the coefficients of each LASSO model, it can be concluded that the algorithms rely on the variables’ early change in the Symptom Distress subscale and the total scores of the OQ-45.2, as well as having a paid job at the start of the treatment and age.…”