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Background: Although behavioral interventions have been found efficacious and effective in randomized clinical trials for most mental illnesses, the quality and efficacy of mental healthcare delivery remains inadequate in real-world settings, partly due to suboptimal treatment fidelity. This “therapist drift” is an ongoing issue that ultimately reduces the effectiveness of treatments, however until recently there have been limited opportunities to assess adherence beyond large randomized controlled studies. Objective: This study explored therapists’ use of a standard component that is pertinent across most behavioral treatments - prompting clients to summarize their treatment session as a means for consolidating and augmenting their understanding of the session and the treatment plan. Methods: The dataset for this study comprised 17,607 behavioral treatment sessions given by 322 therapists to 3,519 patients in 37 behavioral healthcare programs across the U.S. Sessions were captured by a therapy-specific artificial intelligence (AI) platform, and an automatic speech recognition system (ASR) transcribed the treatment meeting and separated the data to the therapist and client utterances. A search for possible session summary prompts was then conducted, with two psychologists validating the text that emerged.Results: We found that despite clinical recommendations, only 54 sessions (0.30%) included a summary. Exploratory analyses indicated that session summaries mostly addressed relationships (N = 27), work (N = 20), change (N= 6), and alcohol (N = 5). Sessions with meeting summaries were also characterized with greater therapist interventions, and included greater use of validation, complex reflections, and proactive problem-solving techniques. Conclusions: To the best of our knowledge, this is the first study to assess a large, diverse dataset of real-world treatment practices. Findings provide evidence that fidelity with the core components of empirically-based psychological interventions as designed is a challenge in real-world settings. Results of this study can inform the development of machine learning and AI algorithms and offer nuanced, timely feedback to providers, thereby improving the delivery of evidence-based practices and quality of mental healthcare services, and facilitating better clinical outcomes in real-world settings. |
