Using AI-Supported Supervision in a University Telemental Health Training Clinic

Artificial Intelligence (AI) technologies have the potential of transforming clinical education and supervision in university-based telemental health training clinics. AI can improve the accuracy of diagnoses, automate routine tasks, and personalize treatment plans, potentially enhancing the accessibility and quality of mental health care. In this paper, we describe why training clinics serve as an optimal setting to adopt innovation and share lessons from the field to inform future integrations of AI in clinical supervision. The lessons include support for case conceptualization, feedback on session quality, and automation of routine tasks such as sending standardized assessments and writing progress notes. However, implementing new technology requires careful consideration of ethical and practical issues such as data privacy, algorithmic bias, and transparency. AI-supported supervision can provide valuable support for clinical training, but adequate training and education are necessary for successful integration

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Effectiveness of a digital cognitive behavior therapy-guided self-help intervention for eating disorders in college women: A cluster randomized clinical trial

Importance: Eating disorders (EDs) are common, serious psychiatric disorders on college campuses, yet most affected individuals do not receive treatment. Digital interventions have the potential to bridge this gap. Objective: To determine whether a coached, digital, cognitive behavior therapy (CBT) intervention improves outcomes for college women with EDs compared with referral to usual care. Design, Setting, and Participants: This cluster randomized trial was conducted from 2014 to 2018 at 27 US universities. Women with binge-purge EDs (with both threshold and subthreshold presentations) were recruited from enrolled universities. The 690 participants were followed up for up to 2 years after the intervention. Data analysis was performed from February to September 2019. Interventions: Universities were randomized to the intervention, Student Bodies-Eating Disorders, a digital CBT-guided self-help program, or to referral to usual care. Main Outcomes and Measures: The main outcome was change in overall ED psychopathology. Secondary outcomes were abstinence from binge eating and compensatory behaviors, as well as ED behavior frequencies, depression, anxiety, clinical impairment, academic impairment, and realized treatment access. Results: A total of 690 women with EDs (mean [SD] age, 22.12 [4.85] years; 414 [60.0%] White; 120 [17.4%] Hispanic; 512 [74.2%] undergraduates) were included in the analyses. For ED psychopathology, there was a significantly greater reduction in the intervention group compared with the control group at the postintervention assessment (β [SE], -0.44 [0.10]; d = -0.40; t1387 = -4.23; P \u3c .001), as well as over the follow-up period (β [SE], -0.39 [0.12]; d = -0.35; t1387 = -3.30; P \u3c .001). There was not a significant difference in abstinence from any ED behaviors at the postintervention assessment (odds ratio, 1.48; 95% CI, 0.48-4.62; P = .50) or at follow-up (odds ratio, 1.51; 95% CI, 0.63-3.58; P = .36). Compared with the control group, the intervention group had significantly greater reductions in binge eating (rate ratio, 0.82; 95% CI, 0.70-0.96; P = .02), compensatory behaviors (rate ratio, 0.68; 95% CI, 0.54-0.86; P \u3c .001), depression (β [SE], -1.34 [0.53]; d = -0.22; t1387 = -2.52; P = .01), and clinical impairment (β [SE], -2.33 [0.94]; d = -0.21; t1387 = -2.49; P = .01) at the postintervention assessment, with these gains sustained through follow-up for all outcomes except binge eating. Groups did not differ in terms of academic impairment. The majority of intervention participants (318 of 385 participants [83%]) began the intervention, whereas only 28% of control participants (76 of 271 participants with follow-up data available) sought treatment for their ED (odds ratio, 12.36; 95% CI, 8.73-17.51; P \u3c .001). Conclusions and Relevance: In this cluster randomized clinical trial comparing a coached, digital CBT intervention with referral to usual care, the intervention was effective in reducing ED psychopathology, compensatory behaviors, depression, and clinical impairment through long-term follow-up, as well as realizing treatment access. No difference was found between the intervention and control groups for abstinence for all ED behaviors or academic impairment. Given its scalability, a coached, digital, CBT intervention for college women with EDs has the potential to address the wide treatment gap for these disorders. Trial Registration: ClinicalTrials.gov Identifier: NCT02076464

Revolutionizing Mental Healthcare Services through AI-Augmentation: A New Model

Artificial intelligence (AI) offers unmet potential for mental health services but also presents new challenges for providers. As AI tools become more ubiquitous in our lives, there is a disconnect between clients’ interests and the adoption of AI in mental healthcare. AI algorithms are informed by expert knowledge, can process multiple data points quickly, and perform many automatic tasks. As such, they can complement the therapy provided by trained professionals and relieve much of the administrative burden healthcare workers experience. In this paper we offer a model for using AI in the daily practice of mental health providers. We discuss ways for clinicians to utilize the data and the services provided by AI to augment therapists’ decision making and maintain the interpersonal connection, while delegating other functions to automated services. We review how AI can augment and scale therapy in many parts of the clinician’s work, from initial screening and routine measurement-based care to digitally-enabled and blended interventions, as well as operational services. We review evidence related to responsible implementation of AI in clinical practice and suggest future research directions and implementation strategies for AI-augmentation

Detecting Climate Anxiety in Therapy through Natural Language Processing

Over the past decade, climate anxiety and eco-anxiety have rapidly been gaining attention. While there is no doubt that climate-related events can certainly lead to psychological distress, if the prevailing popular thinking is accurate, one would expect to see an observable, parallel increase in these issues being explicitly expressed in therapy. Natural language processing (NLP) models capture key moments and conversational topics occurring in therapy sessions, and can be trained to identify climate discussions to help inform training and provide feedback to therapists. This study analyzed 32,542 therapy sessions provided in the U.S. between July 2020 and December 2022. The transcriptions included 1,722,273 labeled therapist-client micro-dialogues. The results indicated that climate- and weather-related topics constituted only a small percentage of the total conversations, with climate change mentioned in only 0.3% of the sessions. Clients with greater depressive or anxiety symptoms mentioned weather and climate significantly less than those with mild or no symptoms. Findings suggest that despite the documented mental health concerns associated with global warming, this study shows that these issues have yet to be adequately represented in psychotherapy. This study highlights the need for increased training for therapists in understanding and addressing with their clients the psychological effects of climate change and eco-anxiety. The use of NLP models can provide valuable feedback to therapists and assist in identifying key moments and conversational topics that can inform training and improve the effectiveness of therapy sessions

The Sound of Mental Health: Audio Features as Indicators of Depression and Anxiety Symptoms in Behavioral Treatment

Measurement-based care (MBC) is critical for assessing clients' mental health in therapy and guiding evidence-based treatment. However, self-report assessments have low completion rates, necessitating less intrusive methods to obtain objective data. Audio features present a promising avenue for passive prediction of clients' mental health status during therapy sessions. This study investigated the use of audio features extracted from therapy sessions to predict depression and anxiety symptoms in a diverse dataset of 2,348 therapy sessions delivered in real-world behavioral health programs. Leveraging machine learning models and a range of audio features, the algorithms predicted whether clients had either mild or moderate-severe depression and anxiety. Our models achieved high accuracy, precision, and recall rates (for anxiety: accuracy of 75.1%, precision of 81.3%, and recall of 61.3% ; for depression, prediction accuracy of 73.2%, precision of 71.4% , and recall of 68.1%). Despite limitations such as limited demographic data and agnosticism of contextual factors, our approach represents a significant step towards improving MBC in mental health settings. Audio analysis emerges as a cost-effective and non-intrusive method for passive prediction, thereby improving therapy monitoring and enhancing patient outcomes

Behavioral Treatments Clinical Guidelines vs. Real-World Treatment Data: The Use of Session Summaries

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 was limited opportunity to assess adherence beyond controlled studies and at scale. 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 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 also included greater therapist use of validation, complex reflections, and proactive problem-solving techniques. Conclusions: Findings suggest that fidelity with the core components of evidence-based psychological interventions as designed is a challenge in real-life 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

Behavioral Treatments Clinical Guidelines vs. Real-World Treatment Data: The Use of Session Summaries

Implementation of evidence-based mental health practices in the fiel