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Background: Eye‐movement protocols are considered empirical, evidence‐based treatments for mental disorders, and there is ample research around their efficacy. However, little research as to why these approaches are effective exists. In this study, an emerging approach, titled Tholáge neuropsychotherapy (called RELEASE at the time of the study), is explained and tested through an exploratory mixed method experiment. Aims: A new theory on the efficacy of eye‐movement treatments is also presented, exploring existing research and contending that specific emotions are associated with specific eye movements. If proven, it may be possible to use eye movements to access specific emotions, for therapeutic outcomes. Methods: In this experiment, there were 14 participants (n = 14) who experienced Tholáge as a treatment for depression. Two one‐way ANOVAs were conducted and found to be statistically significant and have a large effect size. Qualitative responses were shared from participants, and themes were highlighted. Results: The ANOVAs and qualitative responses demonstrated that Tholáge was effective with emotional regulation, and participants showed improvement on measures of affect and depression (the Positive and Negative Affect Schedule and Beck Depression Inventory). Discussion: These results show that there is potential for this new eye‐movement treatment in reducing symptoms of depression. Conclusion: These findings can help practitioners in treatment of depression and potentially other diagnoses as well. There needs to be more research studies conducted on the efficacy of this treatment protocol. [ABSTRACT FROM AUTHOR]

Background:  Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring., Objectives:  In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes., Methods:  We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system., Results:  Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention., Conclusion:  We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system., Competing Interests: None declared., (Thieme. All rights reserved.)