BACKGROUND: Hybrid care models that blend synchronous and asynchronous tools have shown promise in increasing access to care. However, few studies have compared their effectiveness to an appropriate control group, and no RCT has been conducted to tease apart the role of the clinician, app and Digital Navigator components. OBJECTIVE: We aim to evaluate the effectiveness of a blended hybrid care model, the Digital Clinic, in reducing anxiety and depression, compared to a large primary care control group. METHODS: Effectiveness was assessed by comparing GAD-7 and PHQ-9 scores from intake to end of follow-up. Independent t-tests were used to evaluate mean percentage score changes for each subgroup, with Cohen's d calculated to estimate effect sizes. We constructed univariate logistic regression models to identify predictors of improvement for depression and anxiety. RESULTS: Patients in the hybrid model (n = 208) experienced greater reductions in PHQ-9 and GAD-7 scores compared to patients in primary care with 0-3 months follow-up (n = 1077), yielding effect sizes of -0.50 and -0.37, respectively. Improvements in self-efficacy predicted better outcomes for both depression and anxiety, while increases in emotional self-awareness were predictive of anxiety improvement. Total app time and therapist alliance were significant predictors of depression improvement. CONCLUSION: Eight-week blended hybrid care models can offer effective treatment outcomes for depression and anxiety, even when compared to the real-world setting of primary care. These findings can inform future research aimed at elucidating the mechanisms that drive individual clinical effects.