Explore how depression screening quality measures oversimplify complex mental health care, leading to false positives, over-treatment, and unintended harm to patients and providers. This episode dives...

Discover the thriving Depression Screening Market, hitting $5.0B by 2032 (8.18% CAGR). Explore AI-driven tools & B2B mental health solutions.

Background: The clinical and cost-related consequences of internet-based depression screening, in combination with automated feedback, have been rarely investigated. We aimed to conduct a cost-effectiveness analysis (CEA) of DISCOVER, a three-armed, observer-masked, randomized controlled trial that focused on two versions of automated feedback interventions after internet-based depression screening. Objective: This study aims to evaluate the cost-effectiveness of automated non-tailored and tailored feedback interventions, after internet-based depression screening, from a societal perspective. Methods: Participants were recruited from the general population via traditional and social media. Participants who were undiagnosed but screened positive for depression on an online version of the Patient Health Questionnaire-9 (PHQ-9; ≥10 points) were randomized to automatically receive either no feedback, non-tailored feedback, or tailored feedback. The feedback interventions included the depression screening result, a recommendation to seek professional advice, and brief general information about depression. The tailored feedback was additionally framed as per the participants’ symptom profiles, treatment preferences, as well as health insurance plan, and local residency. The time horizon was six months. The main outcome was the incremental cost-effectiveness ratio (ICER) from a societal perspective using quality-adjusted life-years (QALY) based on the EQ-5D-5L. Cost-effectiveness acceptability curves (CEAC) were constructed. Furthermore, several sensitivity analyses and explorative subgroup analyses were conducted. Results: 1,012 participants (no feedback: 343; non-tailored feedback: 338; tailored feedback: 331) were included. Differences in costs and effects were not statistically significant. Still, ICER results indicated that both no feedback and tailored feedback exhibited dominance over non-tailored feedback. The ICER of tailored feedback compared to no feedback was €109,730/QALY, whereas both costs and QALYs were lower in tailored feedback. The cost-effectiveness probability of tailored feedback compared to no feedback ranged between 41% and 80%. Sensitivity analyses exhibited similar trends. Conclusions: Six months post-intervention, feedback interventions had no statistically significant effect on costs from a societal perspective nor on QALYs. Tailored feedback was associated with moderate cost-effectiveness probabilities compared to no feedback. Explorative subgroup analyses revealed subpopulations for which the interventions might be cost-effective. Clinical Trial: ClinicalTrials.gov: NCT04633096

Background: The evolution of language models, particularly large language models, has introduced transformative potential for psychological assessment, challenging traditional rating scale methods that have dominated clinical practice for over a century. Objective: This study aimed to develop and validate an automated assessment paradigm that integrates natural language processing with conventional measurement tools to assess depressive symptoms, exploring its #feasibility as a novel approach in psychological evaluation. Methods: A cohort of 115 participants, including 28 (24.3%) individuals diagnosed with depression, completed the Beck Depression Inventory Fast Screen via a custom ChatGPT interface (BDI-FS-GPT) and the Chinese version of the Patient Health Questionnaire–9 (PHQ-9). Statistical analyses included the Spearman correlation (PHQ-9 vs BDI-FS-GPT scores), Cohen κ (diagnostic agreement), and area under the curve (AUC) evaluation. Results: Spearman analysis revealed a moderate correlation between PHQ-9 and BDI-FS-GPT scores. The Cohen κ indicated moderate diagnostic agreement between the PHQ-9 and the BDI-FS-GPT (κ=0.43; 76.5% agreement), substantial agreement between the BDI-FS-GPT and the clinical diagnosis (κ=0.72; 88.7% agreement), and moderate agreement between the PHQ-9 and the clinical diagnosis (κ=0.55; 71.4% agreement). The BDI-FS-GPT demonstrated excellent diagnostic accuracy (AUC=0.953) at a cutoff of 3, detecting 89.3% of participants with depression with an 11.5% false-positive rate compared to the PHQ-9 (AUC=0.859) at a cutoff of 5 (sensitivity=71.4%; false-positive rate=13.8%). Participants also reported significantly higher satisfaction with the automated assessment compared to the traditional scale (P=.02). Conclusions: The automated assessment paradigm framework combines the interactivity and personalization of natural language processing–powered tools with the psychometric rigor of traditional scales, suggesting a preliminary #feasibility paradigm for future psychological assessment. Its ability to enhance engagement while maintaining reliability and validity provides encouraging evidence, warranting validation in larger and more diverse studies as large language model technology advances.

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Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...

Although the national screening rate for depression rose ...