Background Assertive community treatment (ACT) is an evidence-based practice that provides intensive, in vivo services for adults with severe mental illness. Some ACT and intensive case management teams have integrated consumers as team members with varying results. Methods We reviewed the literature examining the outcomes of having consumer providers on case management teams, with attention devoted to randomized controlled trials (RCTs). Results We identified 16 published studies, including 8 RCTs. Findings were mixed, with evidence supporting consumer-provided services for improving engagement, and limited support for reduced hospitalizations. However, evidence was lacking for other outcomes areas such as symptom reduction or improved quality of life. Conclusion Including a consumer provider on an ACT team could enhance the outreach mechanisms of ACT, using a more recovery-focused approach to bring consumers into services and help engage them over time. More rigorous research is needed to further evaluate integrating consumer providers on teams.

People vary in the amount of control they want to exercise over decisions about their healthcare. Given the importance of patient-centered care, accurate measurement of these autonomy preferences is critical. This study aimed to assess the factor structure of the Autonomy Preference Index (API), used widely in general healthcare, in individuals with severe mental illness. Data came from two studies of people with severe mental illness (N = 293) who were receiving mental health and/or primary care/integrated care services. Autonomy preferences were assessed with the API regarding both psychiatric and primary care services. Confirmatory factor analysis was used to evaluate fit of the hypothesized two-factor structure of the API (decision-making autonomy and information-seeking autonomy). Results indicated the hypothesized structure for the API did not adequately fit the data for either psychiatric or primary care services. Three problematic items were dropped, resulting in adequate fit for both types of treatment. These results suggest that with relatively minor modifications the API has an acceptable factor structure when asking people with severe mental illness about their preferences to be involved in decision-making. The modified API has clinical and research utility for this population in the burgeoning field of autonomy in patient-centered healthcare.

Background Despite the growing trend of integrating primary care and mental health services, little research has documented how consumers with severe mental illnesses manage comorbid conditions or view integrated services. Objectives We sought to better understand how consumers perceive and manage both mental and physical health conditions and their views of integrated services. Methods We conducted semi-structured interviews with consumers receiving primary care services integrated in a community mental health setting. Results Consumers described a range of strategies to deal with physical health conditions and generally viewed mental and physical health conditions as impacting one another. Consumers viewed integration of primary care and mental health services favorably, specifically its convenience, friendliness and knowledge of providers, and collaboration between providers. Conclusions Although integration was viewed positively, consumers with SMI may need a myriad of strategies and supports to both initiate and sustain lifestyle changes that address common physical health problems.

Background: As adolescent suicide rates continue to rise, innovation in risk identification is warranted. Machine learning can identify suicidal individuals based on their language samples. This feasibility pilot was conducted to explore this technology’s use in adolescent therapy sessions and assess machine learning model performance. Method: Natural language processing machine learning models to identify level of suicide risk using a smartphone app were tested in outpatient therapy sessions. Data collection included language samples, depression and suicidality standardized scale scores, and therapist impression of the client’s mental state. Previously developed models were used to predict suicidal risk. Results: 267 interviews were collected from 60 students in eight schools by ten therapists, with 29 students indicating suicide or self-harm risk. During external validation, models were trained on suicidal speech samples collected from two separate studies. We found that support vector machines (AUC: 0.75; 95% CI: 0.69–0.81) and logistic regression (AUC: 0.76; 95% CI: 0.70–0.82) lead to good discriminative ability, with an extreme gradient boosting model performing the best (AUC: 0.78; 95% CI: 0.72–0.84). Conclusion: Voice collection technology and associated procedures can be integrated into mental health therapists’ workflow. Collected language samples could be classified with good discrimination using machine learning methods.