Why this matters

Routine measurement and feedback-informed care have long been associated with stronger psychotherapy outcomes. In many settings, however, those tools remain primarily clinician-facing, limiting how directly clients engage with the information generated during treatment.

SessionGlance was designed to help close that gap. By combining brief symptom measures with clinician ratings and AI-supported summaries, the platform helps make therapeutic feedback more visible, more accessible, and more useful for clients between sessions.

These pilot studies explored a practical question: when SessionGlance is embedded into outpatient psychotherapy, does it appear to improve engagement and support better clinical trajectories over time?

How the studies were conducted

Two quasi-experimental pilot studies examined SessionGlance in routine outpatient psychotherapy. In both studies, the platform combined brief symptom measures such as the PHQ and GAD with clinician ratings to generate client-facing session summaries and reflection questions.

Study 1: Treatment engagement

Forty-nine adult clients at a Midwestern outpatient clinic were assigned either to SessionGlance (n = 20) or to a waitlist control condition (n = 29). The primary outcome was treatment engagement, defined as the proportion of scheduled sessions attended.

Study 2: Symptoms and functioning

A separate cohort of 36 adult clients received psychotherapy from one of three licensed therapists and entered either the SessionGlance or waitlist condition as part of a staged rollout. After sessions, clients completed the PHQ-2 and GAD-2, while therapists rated psychosocial functioning using the Global Assessment of Functioning (GAF).

Linear mixed-effects models were used to estimate clinical trajectories over the course of treatment, allowing the team to evaluate differences in change as treatment progressed.

What the early results suggest

What to take from these pilots

These early findings are encouraging, particularly because they emerged within a real-world clinical setting. SessionGlance was linked with markedly better engagement, statistically significant gains in global functioning, and promising trend-level effects for depression and anxiety outcomes.

The results should still be interpreted with appropriate caution. The studies were quasi-experimental, participants were not randomly assigned, and the sample sizes were modest. These findings support feasibility and potential clinical value, but they do not yet establish causality.

Even so, the pattern is meaningful. The data suggest that an AI-supported, client-visible feedback process may help strengthen how clients stay connected to therapy and how clinicians monitor progress over time.