South Texas Residency Screening, Brief Intervention, and Referral to Treatment (SBIRT) Training: 12-Month Outcomes.

BACKGROUND: Screening, brief intervention, and referral to treatment (SBIRT) is an efficacious and cost-effective skill set when implemented in primary care settings regarding hazardous alcohol use. This study assesses the impact of medical resident SBIRT training across 3 specialties and identifies predictors of change in trainee behavior, attitudes, and knowledge over 12 months. METHODS: This program's substance use SBIRT training was developed and tailored to fit diverse curricular objectives and settings across an array of medical residency programs in South Texas. The 329 residents training in pediatrics, family medicine, and internal medicine during 2009-2012 constituted the trainee group reported in this analysis. Surveys assessing SBIRT-related knowledge, current practice, confidence, role responsibility, attitudes, beliefs, and readiness to change were completed by 234 (71%) trainees at 3 time points: pre-training, then 30 days and 12 months post-initial training. RESULTS: SBIRT-related knowledge, confidence, and practice increased from pre-training to 12-month follow-up. Residents who reported the least amount of pre-training clinical and/or prior academic exposure to substance use reported the greatest SBIRT practice increases. When controlling for demographic and prior exposure variables, the largest contributor to variance in SBIRT practice was attributed to residents' confidence in their SBIRT skills. CONCLUSIONS: SBIRT training that employs diverse educational methodologies as part of customizing the training to residency specialties can similarly enhance SBIRT-related knowledge, confidence, and practice. Trainee report of limited prior clinical or academic exposure to substance use and/or low confidence regarding SBIRT skills and their professional role responsibilities related to substance use predicted trainee success and sustained SBIRT strategy application. When customizing SBIRT training, curriculum developers should consider leveraging and capacity building related to those factors predicting continued use of SBIRT practices.

A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use.

Introduction: Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection. Method: Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated. Results: Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking. Conclusion: These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.

Prevalence of alcohol and other drug detections in non-transport injury events.

OBJECTIVE: To measure the prevalence of alcohol and/or other drug (AOD) detections in suspected major trauma patients with non-transport injuries who presented to an adult major trauma centre. METHODS: This registry-based cohort study examined the prevalence of AOD detections in patients aged ≥18 years who: (i) sustained non-transport injuries; and (ii) met predefined trauma call-out criteria and were therefore managed by an interdisciplinary trauma team between 1 July 2021 and 31 December 2022. Prevalence was measured using routine in-hospital blood alcohol and urine drug screens. RESULTS: A total of 1469 cases met the inclusion criteria. Of cases with a valid blood test (n = 1248, 85.0%), alcohol was detected in 313 (25.1%) patients. Of the 733 (49.9%) cases with urine drug screen results, cannabinoids were most commonly detected (n = 103, 14.1%), followed by benzodiazepines (n = 98, 13.4%), amphetamine-type substances (n = 80, 10.9%), opioids (n = 28, 3.8%) and cocaine (n = 17, 2.3%). Alcohol and/or at least one other drug was detected in 37.4% (n = 472) of cases with either a blood alcohol or urine drug test completed (n = 1263, 86.0%). Multiple substances were detected in 16.6% (n = 119) of cases with both blood alcohol and urine drug screens (n = 718, 48.9%). Detections were prevalent in cases of interpersonal violence (n = 123/179, 68.7%) and intentional self-harm (n = 50/106, 47.2%), and in those occurring on Friday and Saturday nights (n = 118/191, 61.8%). CONCLUSION: AOD detections were common in trauma patients with non-transport injury causes. Population-level surveillance is needed to inform prevention strategies that address AOD use as a significant risk factor for serious injury.

Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic.

The substance use disorder epidemic has emerged as a serious public health crisis, presenting complex challenges. Visual analytics offers a unique approach to address this complexity and facilitate effective interventions. This paper details the development of an innovative visual analytics dashboard, aimed at enhancing our understanding of the substance use disorder epidemic. By employing record linkage techniques, we integrate diverse data sources to provide a comprehensive view of the epidemic. Adherence to responsive, open, and user-centered design principles ensures the dashboard's usefulness and usability. Our approach to data and design encourages collaboration among various stakeholders, including researchers, politicians, and healthcare practitioners. Through illustrative outputs, we demonstrate how the dashboard can deepen our understanding of the epidemic, support intervention strategies, and evaluate the effectiveness of implemented measures. The paper concludes with a discussion of the dashboard's use cases and limitations.

Automated detection of substance use information from electronic health records for a pediatric population.

OBJECTIVE: Substance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data. MATERIALS AND METHODS: Pediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC). RESULTS: The dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively). CONCLUSIONS: It is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies.