Can we measure therapist-patient alliance from dialogue turns in real time?

COMPASS uses sentence embeddings (SentenceBERT, 384-dimensional) to project each dialogue turn onto representations of the 36-item Working Alliance Inventory. The result: a 36-dimensional working alliance score for every patient and therapist turn, decomposable into three subscales — task (collaborative nature), bond (affective connection), and goal (agreement on objectives). Combined with Temporal Topic Modeling using the Embedded Topic Model (ETM), this produces turn-resolution topic scores that track conversation focus over time.

Analyzing 950+ sessions across anxiety, depression, schizophrenia, and suicidality reveals condition-specific dynamics. Anxiety and depression sessions show convergence in bond and task scales as therapy progresses — a positive signal of alliance formation. Schizophrenia and suicidality sessions do not show this convergence. Suicidality trajectories are notably more spread out in bond and task scales, indicating significant patient-therapist misalignment.

The interpretable output identifies actionable patterns: discussing "Emotional States and Mental Health" increases task and bond scales for depression but decreases them for suicidality. Topic-to-alliance mapping enables therapists to identify which conversational strategies are working or failing for each condition — something previously requiring clinical intuition.

Since Can conversation structure predict dialogue success better than content?, alliance trajectories may represent a domain-specific instance of a general phenomenon: the shape of the conversation carries diagnostic information independent of content. The therapeutic application — real-time feedback on whether alliance is forming or deteriorating — is more clinically mature than general conversational geometry, because it maps onto a validated clinical construct (WAI).

Source: Psychology Therapy Practice