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Objective: The objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions., Materials and Methods: We identified modeling targets using diagnostic codes for suicide attempts within 30, 90, or 365 days following a temporally grouped visit cluster. Structured data included medications, diagnoses, procedures, and demographics, while unstructured data consisted of terms extracted with regular expressions from clinical notes. We compared models trained only on structured data (controls) to hybrid models trained on both structured and unstructured data. We used two temporalization schemes for clinical notes: fixed 90-day windows and flexible epochs. We trained and assessed random forests and hybrid LSTM neural networks using AUPRC and AUROC, with additional evaluation of sensitivity and PPV at 95% specificity., Results: The training set included 2,364,183 visit clusters with 2,009 30-day suicide attempts, and the testing set contained 471,936 visit clusters with 480 suicide attempts. Models trained with temporal CUIs outperformed those trained with only structured data. The window-temporalized LSTM model achieved the highest AUPRC (0.056 ± 0.013) for the 30-day prediction range. Hybrid models generally showed better performance compared to controls across most metrics., Discussion and Conclusion: This study demonstrated that incorporating EHR-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Objectives: To describe the impact of a standardized opioid prescribing intervention when implemented in three family medicine (FM) residency training - clinics-environments that face operational challenges including regular resident turnover., Design: We performed a retrospective cohort study to compare patterns of long-term opioid prescribing between residency and nonresidency clinics., Setting: This study took place within a large, academic, health system., Patients and Participants: Three FM residency clinics were compared with three nonresidency FM clinics., Interventions: A standardized opioid prescribing process was developed and implemented within the FM residency clinics. Nonresidency clinics used an independent process and were not exposed to the intervention., Main Outcome Measures: Descriptive comparisons were performed for treatment and control clinics' opioid prescribing from 2015 to 2018. The primary outcome was a patient's annual opioid exposure supplied from these select clinics. We also examine coprescribing with high-risk medications that potentiate the overdose risk of opioid prescriptions. Difference-in-difference modeling was used to control for clinic-level variation in practice., Results: Statistically significant decreases were observed in both residency and nonresidency clinics for the mean number of opioid prescriptions and the mean daily morphine milligram equivalent. These decreases were comparable between the residency and nonresidency clinics., Conclusions: Residency clinics face unique challenges and require innovative solutions to keep up with best practices in opioid prescribing. Our residency clinics' implementation of a standardized intervention, including electronic health record integration, standardized processes, and metric management, suggests steps that may be valuable in achieving outcomes comparable to nonresidency clinics in large health systems.

Pediatric primary health care (PPHC) is of principal importance to the health and development of all children, helping them reach their true potential. Pediatricians, as the clinicians most intensively trained and experienced in child health, are the natural leaders of PPHC within the context of the medical home. Given the rapidly evolving models of pediatric health care delivery, including the explosion of telehealth in the wake of the COVID-19 pandemic, pediatricians, together with their representative national organizations such as the American Academy of Pediatrics (AAP), are the most capable clinicians to guide policy innovations on both the local and national stage., Competing Interests: FINANCIAL DISCLOSURE: The authors have indicated they do not have a financial relationship relevant to this article to disclose., (Copyright © 2022 by the American Academy of Pediatrics.)

The nature of the HIV epidemic in the United States and Canada has changed with a shift toward rural areas. Socioeconomic factors, geography, cultural context, and evolving epidemics of injection drug use are coalescing to move the epidemic into locations where populations are dispersed and health care resources are limited. Rural-urban differences along the care continuum demonstrate the implications of this sociogeographic shift. Greater attention is needed to build a more comprehensive understanding of the rural HIV epidemic in the United States and Canada, including research efforts, innovative approaches to care delivery, and greater community engagement in prevention and care.