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Background and objectives: Substance use disorders (SUD) among healthcare professionals threaten both patient safety and workforce stability. Professional health programs (PHPs) aim to support recovery and safe practice reentry, yet outcomes for non-physician professionals remain underexplored. This study assessed return to use and professional outcomes among healthcare professionals monitored through the Utah Professionals Health Program (UPHP), a cohort predominantly composed of non-physician participants.
Methods: A retrospective cohort study used data from 183 UPHP participants with SUD (60.7% nurses) enrolled between 2013 and 2024. The primary outcome was return to use, defined by positive toxicology results or self-report. Logistic regression was used to identify predictors of return to use.
Results: Return to use occurred in 48.6% of participants, with the highest incidence (33.9%) in year one. By year five, 81.4% had completed monitoring, and 55.9% were working full-time in healthcare. In adjusted models, age (OR = 0.93 per year, p = .039), duration of SUD before UPHP enrollment (OR = 1.14 per year, p = .004), and injury history (OR = 0.23, p = .017) were significantly associated with return to use.
Discussion and conclusions: This non-physician dominant cohort highlights need for early recovery support and profession-specific care. Return to use was most frequent in year one, underscoring the need for intensive early-phase monitoring. Tailored support for injury-related substance use, along with peer support and employment services may improve outcomes.
Scientific significance: Findings provide novel insight into recovery outcomes for non-physician healthcare professionals in a PHP, a group underrepresented in addiction research.
Background and objectives: Gambling screening in the US military was mandated in 2019, but few studies have assessed the prevalence of experiencing gambling problems or its association with adverse behavioral health concerns.
Methods: This secondary analysis used data from active-duty U.S. military service members participating in the 2018 iteration of the Health-Related Behaviors Survey (n = 17,098). Individuals experiencing gambling problems were identified with the 2-item Lie-Bet screener, a well-validated screener. Psychological distress was measured with the Kessler-6. Outcomes also included measures for insufficient sleep, tobacco use, marijuana use, and binge drinking. Weighted multivariable logistic regressions were used to evaluate associations.
Results: Approximately 1.6% of the weighted sample were identified as service members experiencing gambling problems. Individuals experiencing gambling problems had 3.1-fold greater odds of severe psychological distress when compared to those who screened negative (95% Confidence Interval (CI): 2.06-4.67). Similar associations were observed for individuals experiencing gambling problems and insufficient sleep, tobacco use, and binge drinking. There was no association detected between individuals experiencing gambling problems and marijuana use.
Discussion and conclusion: A very small percentage of U.S. military service members were identified as experiencing gambling problems. Service members experiencing gambling problems had a significantly greater adjusted odds of psychological distress, insufficient sleep, tobacco use, and binge drinking.
Scientific significance: Additional research is needed to assess the potential benefits and risks of the new mandatory military screening effort, and how best to support service members and their families in mitigating deleterious health concerns associated with experiencing gambling problems.
Implementation of screening, brief intervention, and referral to treatment (SBIRT) for substance use in primary care remains challenging. Care coordination (CC) may strengthen SBIRT by supporting brief interventions, improving referral follow-through, and enhancing continuity. CC models and outcomes vary, however, and no prior scoping review has synthesized these.
A comprehensive search of 10 resources, including Embase, MEDLINE, and PsycINFO, was conducted on 4/15/2025. Eligible studies described outpatient SBIRT models adding CC for any age group. Articles were excluded if they lacked CC, presented no original data, or were not in English. Relevant implementation and patient-level outcomes were extracted to describe CC models and their impact.
Of 737 abstracts and 50 full texts reviewed, 15 met the inclusion criteria. Studies spanned primary care, specialty care, and age ranges. CC models included embedded behavioral health providers, centralized linkage managers, and remote coordinators using phone or digital platforms. CC was associated with higher screening completion, brief intervention delivery, referral initiation, and treatment engagement. Successful integration incorporated structured communication, follow-up protocols, and accessible technology. Barriers included limited billing infrastructure, weak electronic record integration, and unclear roles.
CC appears to enhance SBIRT implementation and treatment engagement across outpatient settings. Embedded approaches offer relational benefits, while remote and digital strategies show promise for scalability. Comparative studies are needed to assess effectiveness, cost, and contextual fit.
Provides the first review of CC models within SBIRT, highlighting their implementation impact and guiding future optimization.
Background and objectives: AI chatbots are increasingly used in patient education. For opioid use disorder (OUD), content must be readable and non-stigmatizing. We compared ChatGPT responses with U.S. health organization FAQs on readability, complexity, and stigma.
Methods: We analyzed 50 OUD FAQs paired with ChatGPT GPT-4o responses. Outcomes included word and sentence counts, lexical density, and six readability indices. Paired differences were tested with t tests or Wilcoxon signed-rank tests.
Results: ChatGPT responses were longer than FAQs, with a mean word count of 253.7 vs. 76.6 (difference 177; 95% CI, 151-203) and sentence count of 18.2 vs. 9.0 (difference 9.2; 95% CI, 7.6-10.9). Lexical density was higher by 6.5 percentage points (95% CI, 4.0-9.0), with more characters (0.55; 95% CI, 0.40-0.70) and syllables per word (0.19; IQR, 0.14-0.24). Readability grade levels were consistently higher: Coleman-Liau +3.43, Gunning Fog +3.47, SMOG + 2.96, Flesch-Kincaid +3.61, Automated Readability Index +4.33, and Flesch Reading Ease -20.4 (all p < .05). Stigmatizing term frequency was similar, 0.98 vs. 0.28 per answer (95% CI, -1.3 to +3.3).
Discussion: ChatGPT responses were longer and more complex than FAQs, although the frequency of stigmatizing language was similar.
Conclusions: ChatGPT produced more comprehensive but less readable content than FAQs, revealing a gap with health literacy standards. While stigmatizing terms were uncommon unless simplified, length and complexity may hinder use.
Scientific significance: Findings quantify readability and stigma trade-offs in AI-generated OUD education and emphasize the need for plain language prompting and human review.
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