Literature Collection

BACKGROUND: The COVID-19 pandemic drove significant disruptions in access to substance use disorder (SUD) treatment and harm reduction services. Healthcare delivery via telemedicine has increasingly become the norm, rendering access to a phone essential for engagement in care. METHODS: Adult patients with SUD who lacked phones (n = 181) received a free, pre-paid phone during encounters with inpatient and outpatient SUD programs. We evaluated changes in healthcare engagement including completed in-person and telemedicine outpatient visits and telephone encounters 30 days before and after phone receipt. We used descriptive statistics, where appropriate, and paired t-tests to assess the change in healthcare engagement measures. RESULTS: Patients were predominantly male (64%) and white (62%) with high rates of homelessness (81%) and opioid use disorder (89%). When comparing 30 days before to 30 days after phone receipt, there was a significant increased change in number of telemedicine visits by 0.3 (95% CL [0.1,0.4], p < 0.001) and telephone encounters by 0.2 (95% CL [0.1,0.3], p = 0.004). There was no statistically significant change in in-person outpatient visits observed. CONCLUSIONS: Pre-paid phone distribution to patients with SUD was associated with an increased healthcare engagement including telemedicine visits and encounters.

Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.

INTRODUCTION: A substantial number of trauma-exposed veterans seen in primary care report significant symptoms of PTSD and depression. While primary care mental health integration (PCMHI) providers have been successful in delivering brief mental health treatments in primary care, few studies have evaluated interventions that combine mobile health resources with PCMHI groups. This pilot study assessed the potential benefits of webSTAIR, a 10-module transdiagnostic treatment for trauma-exposed individuals, supported by 5 biweekly group sessions delivered via telehealth. The transdiagnostic and mobile health nature of the treatment, as well as the therapist and peer support provided through group sessions, may offer an innovative approach to increasing access to patient-centered and trauma-informed treatment in primary care settings. MATERIALS AND METHODS: Thirty-nine male and female veterans with trauma-related symptoms (i.e., PTSD and/or depression) participated in group webSTAIR. Mixed effects analyses were conducted to assess changes in PTSD and depression at pre-, mid-, and post-treatment. Baseline symptom severity was assessed as a predictor of module completion and group attendance. The project was part of a VHA quality improvement project, and IRB approval was waived by the affiliated university. RESULTS: Analyses revealed significant pre-to-post improvement in both PTSD and depression outcomes with a large effect size for PTSD (Hedges' gav = 0.88) and medium to large for depression (Hedges' gav = 0.73). Of participants who completed the baseline assessment, 90% began webSTAIR; of those, 71% completed the program. Baseline symptoms of PTSD and depression did not predict group attendance or module completion. CONCLUSIONS: Good outcomes and a satisfactory retention rate suggest that group webSTAIR may provide easily accessible, high-quality, and effective treatment for patients presenting with trauma-related problems without increasing therapist or system burdens. The results suggest the value of conducting a randomized controlled trial to test the effectiveness of group webSTAIR relative to PCMHI usual care or other evidence-based, disorder-specific (e.g., PTSD) treatments for trauma-exposed individuals in PCMHI.

INTRODUCTION: A substantial number of trauma-exposed veterans seen in primary care report significant symptoms of PTSD and depression. While primary care mental health integration (PCMHI) providers have been successful in delivering brief mental health treatments in primary care, few studies have evaluated interventions that combine mobile health resources with PCMHI groups. This pilot study assessed the potential benefits of webSTAIR, a 10-module transdiagnostic treatment for trauma-exposed individuals, supported by 5 biweekly group sessions delivered via telehealth. The transdiagnostic and mobile health nature of the treatment, as well as the therapist and peer support provided through group sessions, may offer an innovative approach to increasing access to patient-centered and trauma-informed treatment in primary care settings. MATERIALS AND METHODS: Thirty-nine male and female veterans with trauma-related symptoms (i.e., PTSD and/or depression) participated in group webSTAIR. Mixed effects analyses were conducted to assess changes in PTSD and depression at pre-, mid-, and post-treatment. Baseline symptom severity was assessed as a predictor of module completion and group attendance. The project was part of a VHA quality improvement project, and IRB approval was waived by the affiliated university. RESULTS: Analyses revealed significant pre-to-post improvement in both PTSD and depression outcomes with a large effect size for PTSD (Hedges' gav = 0.88) and medium to large for depression (Hedges' gav = 0.73). Of participants who completed the baseline assessment, 90% began webSTAIR; of those, 71% completed the program. Baseline symptoms of PTSD and depression did not predict group attendance or module completion. CONCLUSIONS: Good outcomes and a satisfactory retention rate suggest that group webSTAIR may provide easily accessible, high-quality, and effective treatment for patients presenting with trauma-related problems without increasing therapist or system burdens. The results suggest the value of conducting a randomized controlled trial to test the effectiveness of group webSTAIR relative to PCMHI usual care or other evidence-based, disorder-specific (e.g., PTSD) treatments for trauma-exposed individuals in PCMHI.

BACKGROUND: Delivering prescription digital therapeutics (ie, evidence-based interventions designed to treat, manage, or prevent disorders via websites or smartphone apps) in primary care could increase patient access to substance use disorder (SUD) treatments. However, the optimal approach to implementing prescription digital therapeutics in primary care remains unknown. OBJECTIVE: This pilot study is a precursor to a larger trial designed to test whether implementation strategies (practice facilitation [PF] and health coaching [HC]) improve the delivery of prescription digital therapeutics for SUDs in primary care. This mixed methods study describes outcomes among patients in the 2 pilot clinics and presents qualitative findings on implementation. METHODS: From February 10 to August 6, 2021, a total of 3 mental health specialists embedded in 2 primary care practices of the same integrated health system were tasked with offering app-based prescription digital therapeutics to patients with SUD. In the first half of the pilot, implementation activities included training and supportive tools. PF (at 1 clinic) and HC (at 2 clinics) were added in the second half. All study analyses relied on secondary data, including electronic health records and digital therapeutic vendor data. Primary outcomes were the proportion of patients reached by the prescription digital therapeutics and fidelity related to ideal use. We used qualitative methods to assess the adherence to planned activities and the barriers and facilitators to implementing prescription digital therapeutics. RESULTS: Of all 18 patients prescribed the apps, 10 (56%) downloaded the app and activated their prescription, and 8 (44%) completed at least 1 module of content. Patients who activated the app completed 1 module per week on average. Ideal use (fidelity) was defined as completing 4 modules per week and having a monthly SUD-related visit; 1 (6%) patient met these criteria for 10 weeks (of the 12-week prescription period). A total of 5 (28%) patients had prescriptions while HC was available, 2 (11%) were successfully contacted, and both declined coaching. Clinicians reported competing clinical priorities, technical challenges, and logistically complex workflows in part because the apps required a prescription. Some pilot activities were impacted by staff turnover that coincided with the COVID-19 pandemic. The facilitators to implementation were high engagement and the perception that the apps could meet patient needs. CONCLUSIONS: The pilot study encountered the barriers to implementing prescription digital therapeutics in a real-world primary care setting, especially staffing shortages, turnover, and competing priorities for clinic teams. The larger randomized trial will clarify the extent to which PF and HC improve the implementation of digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT04907045; https://clinicaltrials.gov/study/NCT04907045.

Prescription digital therapeutics (PDTs) are Food and Drug Administration (FDA)-authorized software-based treatments designed to treat a range of conditions on the smartphone. Their development and deployment rely on four foundational scientific domains: clinical, engagement, regulatory, and implementation. However, the relative representation of these domains in the PDT literature has not been systematically characterized. We conducted a bibliometric and thematic analysis of PubMed-indexed articles published between 2020 and 2025 containing the term "prescription digital therapeutic(s)." Metadata and abstracts were extracted, cleaned, and analyzed using natural language processing for this review. Topic modeling was performed to identify key thematic areas, and each abstract was classified into one or more of the four foundational domains using a structured keyword heuristic framework. Trends in publication volume, authorship, domain co-occurrence, and thematic focus were visualized. Sixty-one unique articles met the inclusion criteria. Publication activity increased over time, peaking in 2022 and 2024. Most first authors were based in the United States, with industry-affiliated authorships predominating. The most frequently publishing journals were Frontiers in Psychiatry and Health Affairs (Millwood). Clinical science was referenced in 45 (74%) papers, followed by engagement science in 35 (58%), regulatory science in 28 (46%), and implementation science in 18 (29%). Only seven (12%) articles addressed all four domains. Topic modeling identified five major themes: substance use and cost modeling, regulatory frameworks, insomnia treatment, engagement strategies, and gamified pediatric interventions. Co-occurrence analysis revealed strong overlap between clinical and engagement domains, while regulatory and implementation science appeared less frequently in combination. The literature on PDTs remains concentrated in clinical and engagement domains, with limited attention to regulatory strategy and real-world implementation. Greater integration across all four scientific domains is needed to ensure that PDTs are not only effective but also scalable, fundable, and embedded into routine care.