Automating the assessment of multicultural orientation through machine learning and natural language processing.

Recent scholarship has highlighted the value of therapists adopting a multicultural orientation (MCO) within psychotherapy. A newly developed performance-based measure of MCO capacities exists (MCO-performance task [MCO-PT]) in which therapists respond to video-based vignettes of clients sharing culturally relevant information in therapy. The MCO-PT provides scores related to the three aspects of MCO: cultural humility (i.e., adoption of a nonsuperior and other-oriented stance toward clients), cultural opportunities (i.e., seizing or making moments in session to ask about clients' cultural identities), and cultural comfort (i.e., therapists' comfort in cultural conversations). Although a promising measure, the MCO-PT relies on labor-intensive human coding. The present study evaluated the ability to automate the scoring of the MCO-PT transcripts using modern machine learning and natural language processing methods. We included a sample of 100 participants (n = 613 MCO-PT responses). Results indicated that machine learning models were able to achieve near-human reliability on the average across all domains (Spearman's ρ = .75, p < .0001) and opportunity (ρ = .81, p < .0001). Performance was less robust for cultural humility (ρ = .46, p < .001) and was poorest for cultural comfort (ρ = .41, p < .001). This suggests that we may be on the cusp of being able to develop machine learning-based training paradigms that could allow therapists opportunities for feedback and deliberate practice of some key therapist behaviors, including aspects of MCO. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?

BACKGROUND: The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings. METHODS: Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses. RESULTS: The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording. CONCLUSIONS: Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors' roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors' and clinical supervisors' overall experiences in their places of work.

Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

Importance: Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale. Objective: To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care. Design, Setting, and Participants: In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile therapy app (Talkspace). Therapists were licensed to provide mental health treatment and were either independent contractors or employees of the product company. Participants were self-referred via online sign-up and received services via their insurance or self-pay and were assigned a diagnosis from their health care professional. Exposure: All clients received counseling services from a licensed mental health clinician. Main Outcomes and Measures: The primary outcomes were client engagement in counseling (number of weeks), treatment satisfaction, and changes in client symptoms, measured via the 8-item version of Patient Health Questionnaire (PHQ-8). A previously trained, transformer-based, deep learning model automatically categorized messages into types of therapist interventions and summaries of clinical content. Results: The total sample included 166 644 clients treated by 4973 therapists (20 600 274 messages). Participating clients were predominantly female (75.23%), aged 26 to 35 years (55.4%), single (37.88%), earned a bachelor's degree (59.13%), and were White (61.8%). There was substantial variability in intervention use and treatment content across therapists. A series of mixed-effects regressions indicated that collectively, interventions and clinical content were associated with key outcomes: engagement (multiple R = 0.43), satisfaction (multiple R = 0.46), and change in PHQ-8 score (multiple R = 0.13). Conclusions and Relevance: This quality improvement study found associations between therapist interventions, clinical content, and client-reported outcomes. Consistent with traditional forms of counseling, higher amounts of supportive counseling were associated with improved outcomes. These findings suggest that machine learning-based evaluations of content may increase the scale and specificity of psychotherapy research.

A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care.

Background: Evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. When programs have been taken to scale, declines in the quality of implementation diminish intervention effects. Gold-standard methods of implementation monitoring are cost-prohibitive and impractical in resource-scarce delivery systems. Technological developments using computational linguistics and machine learning offer an opportunity to assess fidelity in a low burden, timely, and comprehensive manner. Methods: In this study, we test two natural language processing (NLP) methods [i.e., Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT)] to assess the delivery of the Family Check-Up 4 Health (FCU4Health) program in a type 2 hybrid effectiveness-implementation trial conducted in primary care settings that serve primarily Latino families. We trained and evaluated models using 116 English and 81 Spanish-language transcripts from the 113 families who initiated FCU4Health services. We evaluated the concurrent validity of the TF-IDF and BERT models using observer ratings of program sessions using the COACH measure of competent adherence. Following the Implementation Cascade model, we assessed predictive validity using multiple indicators of parent engagement, which have been demonstrated to predict improvements in parenting and child outcomes. Results: Both TF-IDF and BERT ratings were significantly associated with observer ratings and engagement outcomes. Using mean squared error, results demonstrated improvement over baseline for observer ratings from a range of 0.83-1.02 to 0.62-0.76, resulting in an average improvement of 24%. Similarly, results demonstrated improvement over baseline for parent engagement indicators from a range of 0.81-27.3 to 0.62-19.50, resulting in an approximate average improvement of 18%. Conclusions: These results demonstrate the potential for NLP methods to assess implementation in evidence-based parenting programs delivered at scale. Future directions are presented. Trial registration: NCT03013309 ClinicalTrials.gov.

Research has shown that evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. However, if they are not implemented with fidelity, there is a potential that they will not produce the same effects. Gold-standard methods of implementation monitoring include observations of program sessions. This is expensive and difficult to implement in delivery settings with limited resources. Using data from a trial of the Family Check-Up 4 Health program in primary care settings that served Latino families, we investigated the potential to make use of a form of machine learning called natural language processing (NLP) to monitor program delivery. NLP-based ratings were significantly associated with independent observer ratings of fidelity and participant engagement outcomes. These results demonstrate the potential for NLP methods to monitor implementation in evidence-based parenting programs delivered at scale.

The Silent Treatment?: Changes in patient emotional expression after silence.

Psychotherapy can be an emotionally laden conversation, where both verbal and non-verbal interventions may impact the therapeutic process. Prior research has postulated mixed results in how clients emotionally react following a silence after the therapist is finished talking, potentially due to studying a limited range of silences with primarily qualitative and self-report methodologies. A quantitative exploration may illuminate new findings. Utilizing research and automatic data processing from the field of linguistics, we analysed the full range of silence lengths (0.2 to 24.01 seconds), and measures of emotional expression - vocally encoded arousal and emotional valence from the works spoken - of 84 audio recordings Motivational Interviewing sessions. We hypothesized that both the level and the variance of client emotional expression would change as a function of silence length, however, due to the mixed results in the literature the direction of emotional change was unclear. We conducted a multilevel linear regression to examine how the level of client emotional expression changed across silence length, and an ANOVA to examine the variability of client emotional expression across silence lengths. Results indicated in both analyses that as silence length increased, emotional expression largely remained the same. Broadly, we demonstrated a weak connection between silence length and emotional expression, indicating no persuasive evidence that silence leads to client emotional processing and expression.

Injunctive and descriptive normative feedback for college drinking prevention: Is the whole greater than the sum of its parts?

OBJECTIVE: Single-component personalized normative feedback (PNF) interventions and multicomponent personalized feedback interventions (PFI) have been shown to reduce alcohol consumption among college students. The present study compared the efficacy of PNF interventions targeting descriptive norms alone (descriptive PNF), injunctive norms alone (injunctive PNF), or their combination (combined PNF), against a multicomponent PFI and an attention control condition. METHOD: Undergraduates (N = 1,137) across two universities who reported a minimum of one past-month episode of heavy episodic drinking (i.e., 4 +/5 + drinks on a single occasion for females/males) completed assessments at baseline and 3, 6, and 12 months postintervention. RESULTS: Relative to the attention control, participants in each of the four intervention conditions showed greater reductions in perceived descriptive/injunctive norms, total drinks per week, and alcohol-related consequences. Peak estimated blood alcohol concentration was also reduced in the injunctive PNF, combined PNF, and multicomponent PFI conditions, with the latter two conditions showing an advantage for duration of effects. The multicomponent PFI condition also evidenced greater reductions than the injunctive PNF in descriptive norms at 3-month and injunctive norms at 6- and 12-month follow-ups. No other group comparisons on any outcome were significant. CONCLUSIONS: Each intervention has merit for use in college student harm reduction efforts. Single-component or combined PNF could be considered a potential starting point, as PNF is less burdensome than a multicomponent PFI when considering ease and length of delivery. Results can inform optimization of norms-based interventions and guide recommendations on efficacious components for reducing alcohol use and harms on college campuses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

High rates of oral anticoagulation in atrial fibrillation patients observed in a large multi-specialty health system in the Northeast.

BACKGROUND: Anticoagulation is a cornerstone in atrial fibrillation (AF) management for stroke prevention. Studies showed that oral anticoagulants (OAC), previously limited to warfarin, were underused. Recently, non-vitamin K oral anticoagulants (NOACs) have seen widespread adoption, but it has not been well studied whether there has been a subsequent increase in OAC usage in AF patients. METHODS: We quantified OAC rates in AF patients in a large multispecialty health system in the Northeast United States. A total of 351,795 patients seen in the network over the preceding 18 months were reviewed. RESULTS: Of these patients, 8727 (2.5%) carried a diagnosis of AF, and, of the 6933 patients with a CHA2DS2-VASc score of 2 or higher, 5576 (80.4%) had an OAC listed as an active medication or had received a left atrial appendage occlusion device. Of the 6605 patients treated with an OAC, 5308 (80.4%) were treated with a NOAC and 1295 (19.6%) were prescribed warfarin. A higher percentage of patients with CHA2DS2-VASc ≥ 2 who had seen a cardiologist were treated with an OAC vs. those who had not seen a cardiologist in the prior 18 months (83.95% vs. 67.43%, p < 0.01). CONCLUSIONS: We show dramatically increased OAC usage among patients with AF and that NOACs comprise the large majority of OACs compared with previous studies. This suggests an association between widespread adoption of NOACs and increased oral anticoagulation rates. Future directions include assessing barriers to oral anticoagulation and developing interventions to reduce disparity in OAC use between clinics.

You never know what you are going to get: Large-scale assessment of therapists' supportive counseling skill use.

Supportive counseling skills like empathy and active listening are critical ingredients of all psychotherapies, but most research relies on client or therapist reports of the treatment process. This study utilized machine-learning models trained to evaluate counseling skills to evaluate supportive skill use in 3,917 session recordings. We analyzed overall skill use and variation in practice patterns using a series of mixed effects models. On average, therapists scored moderately high on observer-rated empathy (i.e., 3.8 out of 5), 3.3% of the therapists' utterances in a session were open questions, and 12.9% of their utterances were reflections. However, there were substantial differences in skill use across therapists as well as across clients within-therapist caseloads. These findings highlight the substantial variability in the process of counseling that clients may experience when they access psychotherapy. We discuss findings in the context of both the need for therapists to be responsive and flexible with their clients, but also potential costs related to the lack of a more uniform experience of care. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How the VA is training the Next-Generation workforce for learning health systems.

Objectives: The U.S. Department of Veterans Affairs (VA) has been a national leader in Learning Health System (LHS) implementation due to its combined mission of research, education, clinical care, and emergency preparedness. We describe the current VA LHS training ecosystem within the Veterans Health Administration's Office of Academic Affiliations (OAA), Office of Research and Development (ORD), ORD's Health Services Research and Development (HSR&D) program, and Innovation Ecosystem (IE), including lessons learned regarding their sustainment. Methods: The VA LHS training ecosystem is based on the Learning Loop and HSR&D Quality Enhancement Research Initiative (QUERI) Roadmap, which describes VA learning opportunities, underlying infrastructures, and core competencies. Results: VA-focused LHS educational programs include data-to-knowledge initiatives in health sciences and analytics, for example, OAA/HSR&D health services and informatics research fellowships; knowledge-to-performance opportunities in implementation and quality improvement, for example, QUERI Learning Hubs and IEs' Diffusion of Excellence Initiative; and performance-to-data embedded opportunities, for example, IE's entrepreneur fellowship programs and QUERI's Advancing Diversity in Implementation Leadership. These training programs are supported by combined VA research and clinical operations investments in funding, informatics, governance, and processes. Lessons learned include ongoing alignment of research funding with operational priorities and capacity, relentless recruitment and retention of implementation, system, and information scientists especially from under-represented groups, sustainment of data infrastructures suitable for research and quality improvement, and ensuring sustainable funding opportunities for researchers to work on system-wide health care problems. Conclusions: There is an urgent need to expand training opportunities in LHSs, especially as health care is increasingly driven by multiple interested parties, impacted by persistent health disparities exacerbated by emerging public health threats, and rapid technology growth. With ongoing alignment of research and clinical goals, foundational support through research funding, underlying clinical operations infrastructures, and active engagement interested parties, VA's LHS training ecosystem promotes a more LHS-savvy, 21st century workforce.

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

BACKGROUND: Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. ("Lyssn") a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform. METHODS: Phase I will work from an existing software prototype to develop a LyssnCBT user interface geared to the needs of community mental health (CMH) agencies. Core activities include a user-centered design focus group and interviews with community mental health therapists, supervisors, and administrators to inform the design and development of LyssnCBT. LyssnCBT will be evaluated for usability and implementation readiness in a final stage of Phase I. Phase II will conduct a stepped-wedge, hybrid implementation-effectiveness randomized trial (N = 1,875 clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes and reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. DISCUSSION: Successful execution will provide automated, scalable CBT fidelity feedback for the first time ever, supporting high-quality training, supervision, and quality assurance, and providing a core technology foundation that could support the quality delivery of a range of EBPs in the future. TRIAL REGISTRATION: ClinicalTrials.gov; NCT05340738 ; approved 4/21/2022.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Delivering Remote Measurement-Based Care in Community Addiction Treatment: Engagement and Usability Over a 6-Month Clinical Pilot.

Objective: Measurement-based care (MBC) is an evidence-based practice in which patients routinely complete standardized measures throughout treatment to help monitor clinical progress and inform clinical decision-making. Despite its potential benefits, MBC is rarely used in community-based substance use disorder (SUD) treatment. In this pilot study, we evaluated the feasibility of incorporating a digital and remotely delivered MBC system into SUD treatment within a community setting by characterizing patients' and clinicians' engagement with and usability ratings toward the MBC system that was piloted. Methods: A pilot study was conducted with 30 patients receiving SUD treatment and eight clinicians providing SUD treatment in a large, publicly funded addiction and mental health treatment clinic. Services as usual within the clinic included individual psychotherapy, case management, group therapy, peer support, and medication management for mental health and SUD, including buprenorphine. Patients who enrolled in the pilot continued to receive services as usual and were automatically sent links to complete a 22-item questionnaire, called the weekly check-in, via text message or email weekly for 24 weeks. Results of the weekly check-in were summarized on a clinician-facing web-based dashboard. Engagement was characterized by calculating the mean number of weekly check-ins completed by patients and the mean number times clinicians logged into the MBC system. Ratings of the MBC system's usability and clinical utility were provided by patients and clinicians. Results: Patient participants (53.3% male, 56.7% white, 90% Medicaid enrolled) completed a mean of 20.60 weekly check-ins (i.e., 85.8% of the 24 expected per patient). All but one participating clinician with a patient enrolled in the study logged into the clinician-facing dashboard at least once, with an average of 12.20 logins per clinician. Patient and clinician ratings of usability and clinical utility were favorable: most patients agreed with statements that the weekly check-in was easy to navigate and aided self-reflection. All clinicians who completed usability questionnaires agreed with statements indicating that the dashboard was easy to navigate and that it provided meaningful information for SUD treatment. Conclusions: A digital and remotely delivered MBC system can yield high rates of patient and clinician engagement and high ratings of usability and clinical utility when added into SUD treatment as usual. The success of this clinical pilot may be attributable, in part, to the user-centered design processes that were used to develop and refine the MBC system that was piloted. Future efforts may focus on strategies to test whether MBC can be sustainably implemented and offers clinical benefits to patients in community SUD treatment settings.

An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates.

Text-based computational approaches for assessing the quality of psychotherapy are being developed to support quality assurance and clinical training. However, due to the long durations of typical conversation based therapy sessions, and due to limited annotated modeling resources, computational methods largely rely on frequency-based lexical features or dialogue acts to assess the overall session level characteristics. In this work, we propose a hierarchical framework to automatically evaluate the quality of transcribed Cognitive Behavioral Therapy (CBT) interactions. Given the richly dynamic nature of the spoken dialog within a talk therapy session, to evaluate the overall session level quality, we propose to consider modeling it as a function of local variations across the interaction. To implement that empirically, we divide each psychotherapy session into conversation segments and initialize the segment-level qualities with the session-level scores. First, we produce segment embeddings by fine-tuning a BERT-based model, and predict segment-level (local) quality scores. These embeddings are used as the lower-level input to a Bidirectional LSTM-based neural network to predict the session-level (global) quality estimates. In particular, we model the global quality as a linear function of the local quality scores, which allows us to update the segment-level quality estimates based on the session-level quality prediction. These newly estimated segment-level scores benefit the BERT fine-tuning process, which in turn results in better segment embeddings. We evaluate the proposed framework on automatically derived transcriptions from real-world CBT clinical recordings to predict session-level behavior codes. The results indicate that our approach leads to improved evaluation accuracy for most codes when used for both regression and classification tasks.

Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.

To capitalize on investments in evidence-based practices, technology is needed to scale up fidelity assessment and supervision. Stakeholder feedback may facilitate adoption of such tools. This evaluation gathered stakeholder feedback and preferences to explore whether it would be fundamentally feasible or possible to implement an automated fidelity-scoring supervision tool in community mental health settings. A partially mixed, sequential research method design was used including focus group discussions with community mental health therapists (n = 18) and clinical leadership (n = 12) to explore typical supervision practices, followed by discussion of an automated fidelity feedback tool embedded in a cloud-based supervision platform. Interpretation of qualitative findings was enhanced through quantitative measures of participants' use of technology and perceptions of acceptability, appropriateness, and feasibility of the tool. Initial perceptions of acceptability, appropriateness, and feasibility of automated fidelity tools were positive and increased after introduction of an automated tool. Standard supervision was described as collaboratively guided and focused on clinical content, self-care, and documentation. Participants highlighted the tool's utility for supervision, training, and professional growth, but questioned its ability to evaluate rapport, cultural responsiveness, and non-verbal communication. Concerns were raised about privacy and the impact of low scores on therapist confidence. Desired features included intervention labeling and transparency about how scores related to session content. Opportunities for asynchronous, remote, and targeted supervision were particularly valued. Stakeholder feedback suggests that automated fidelity measurement could augment supervision practices. Future research should examine the relations among use of such supervision tools, clinician skill, and client outcomes.

Automated evaluation of psychotherapy skills using speech and language technologies.

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called "motivational interviewing", our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist's vs. client's talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients' perspective). We describe our platform and its performance using a dataset of more than 5000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. Widespread use of automated psychotherapy rating tools may augment experts' capabilities by providing an avenue for more effective training and skill improvement, eventually leading to more positive clinical outcomes.

Multi-label Multi-task Deep Learning for Behavioral Coding.

We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions is the annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist client interactions in psychotherapy sessions. We experimentally compare the proposed learning approaches for estimating behaviors of interest in these datasets. Specifically, we compare single and multiple label learning approaches, single and multiple task learning approaches, and evaluate the performance of these approaches when incorporating turn context. We demonstrate the prediction performance gains which can be achieved by using the proposed paradigms and discuss the insights these models provide into these complex interactions.

Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospitalized Patients: Protocol for Formative and Pilot Feasibility Research.

BACKGROUND: Suicide is the 10th leading cause of death in the United States, with >47,000 deaths in 2019. Most people who died by suicide had contact with the health care system in the year before their death. Health care provider training is a top research priority identified by the National Action Alliance for Suicide Prevention; however, evidence-based approaches that target skill-building are resource intensive and difficult to implement. Advances in artificial intelligence technology hold promise for improving the scalability and sustainability of training methods, as it is now possible for computers to assess the intervention delivery skills of trainees and provide feedback to guide skill improvements. Much remains to be known about how best to integrate these novel technologies into continuing education for health care providers. OBJECTIVE: In Project WISE (Workplace Integrated Support and Education), we aim to develop e-learning training in suicide safety planning, enhanced with novel skill-building technologies that can be integrated into the routine workflow of nurses serving patients hospitalized for medical or surgical reasons or traumatic injury. The research aims include identifying strategies for the implementation and workflow integration of both the training and safety planning with patients, adapting 2 existing technologies to enhance general counseling skills for use in suicide safety planning (a conversational agent and an artificial intelligence-based feedback system), observing training acceptability and nurse engagement with the training components, and assessing the feasibility of recruitment, retention, and collection of longitudinal self-report and electronic health record data for patients identified as at risk of suicide. METHODS: Our developmental research includes qualitative and observational methods to explore the implementation context and technology usability, formative evaluation of the training paradigm, and pilot research to assess the feasibility of conducting a future cluster randomized pragmatic trial. The trial will examine whether patients hospitalized for medical or surgical reasons or traumatic injury who are at risk of suicide have better suicide-related postdischarge outcomes when admitted to a unit with nurses trained using the skill-building technology than those admitted to a unit with untrained nurses. The research takes place at a level 1 trauma center, which is also a safety-net hospital and academic medical center. RESULTS: Project WISE was funded in July 2019. As of September 2021, we have completed focus groups and usability testing with 27 acute care and 3 acute and intensive care nurses. We began data collection for research aims 3 and 4 in November 2021. All research has been approved by the University of Washington institutional review board. CONCLUSIONS: Project WISE aims to further the national agenda to improve suicide prevention in health care settings by training nurses in suicide prevention with medically hospitalized patients using novel e-learning technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33695.

Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions.

Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting. The quality of a CBT session is typically assessed by trained human raters who manually assign pre-defined session-level behavioral codes. In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically. We investigate both word-level and utterance-level features and propose feature fusion strategies to combine them. The utterance level features include dialog act tags as well as behavioral codes drawn from another well-known talk psychotherapy called Motivational Interviewing (MI). We propose a novel method to augment the word-based features with the utterance level tags for subsequent CBT code estimation. Experiments show that our new fusion strategy outperforms all the studied features, both when used individually and when fused by direct concatenation. We also find that incorporating a sentence segmentation module can further improve the overall system given the preponderance of multi-utterance conversational turns in CBT sessions.