Factors influencing virtual collaborative care outcomes for depression and anxiety.

Context and Aims: In the United States, access to evidence‑based behavioral health treatment remains limited, contributing to inadequate treatment for individuals with depression and anxiety disorders. The Collaborative care model (CoCM), the integration of behavioral healthcare into primary care, has been shown to be effective in addressing this issue, particularly when delivered virtually through telehealth platforms. While collaborative care has been shown to be effective, little has been studied to understand the impact of patient treatment factors on patient improvement. This study aims to analyze factors associated with patient improvement, measured by PHQ‑9 and GAD‑7 score changes, in patients with depression and anxiety disorders from Concert Health, a national behavioral medical group offering collaborative care across 18 states. Methods and Material: Stepwise logistic regression models were utilized to identify factors influencing patient improvement in standardized symptom screener scores (PHQ‑9 and GAD‑7). Relevant patient‑level data, including demographics, clinical engagement, insurance type, clinical touchpoints, and other variables, were analyzed. Results are presented as odds ratios (ORs). Results and Conclusions: We find that increased clinical touchpoints were associated with improved outcomes in both depression (PHQ‑9) and anxiety (GAD‑7) populations. Commercial insurance was linked to a greater likelihood of improvement relative to Medicaid, and the use of C‑SSRS suicide screeners had varied effects on patient outcomes depending on the diagnosis. The duration of time spent in appointments showed a nuanced impact, suggesting an optimal length for touchpoints. Psychiatric consults also impact patient outcomes in both populations. This study sheds light on factors influencing patient outcomes in virtual collaborative care for depression and anxiety disorders, which may be used to inform and motivate further research and allow providers to better optimize and understand the impacts of treatment choices in collaborative care settings. [ABSTRACT FROM AUTHOR]