Literature Collection

Opioid agonist medication, including methadone, is considered the first-line treatment for opioid use disorder. Methadone, when taken daily, reduces the risk of fatal overdose; however, overdose risk increases following medication cessation. Amid an overdose epidemic accelerated by the proliferation of fentanyl, ensuring continuity of methadone treatment during the COVID-19 pandemic is a vital public health priority. (Am J Public Health. 2021;111(12):2115-2117. https://doi.org/10.2105/AJPH.2021.306523).

BACKGROUND: Pharmacists remain an underutilized resource in the treatment of opioid use disorder (OUD). Although studies have engaged pharmacists in dispensing medications for OUD (MOUD), few studies have evaluated collaborative care models in which pharmacists are an active, integrated part of a primary care team offering OUD care. METHODS: This study seeks to implement a pharmacist integrated MOUD clinical model (called PrIMO) and evaluate its feasibility, acceptability, and impact across four diverse primary care sites. The Consolidated Framework for Implementation Research is used as an organizing framework for study development and interpretation of findings. Implementation Facilitation is used to support PrIMO adoption. We assess the primary outcome, the feasibility of implementing PrIMO, using the Stages of Implementation Completion (SIC). We evaluate the acceptability and impact of the PrIMO model at the sites using mixed-methods and combine survey and interview data from providers, pharmacists, pharmacy technicians, administrators, and patients receiving MOUD at the primary care sites with patient electronic health record data. We hypothesize that it is feasible to launch delivery of the PrIMO model (reach SIC Stage 6), and that it is acceptable, will positively impact patient outcomes 1 year post model launch (e.g., increased MOUD treatment retention, medication regimen adherence, service utilization for co-morbid conditions, and decreased substance use), and will increase each site's capacity to care for patients with MOUD (e.g., increased number of patients, number of prescribers, and rate of patients per prescriber). DISCUSSION: This study will provide data on a pharmacist-integrated collaborative model of care for the treatment of OUD that may be feasible, acceptable to both site staff and patients and may favorably impact patients' access to MOUD and treatment outcomes. TRIAL REGISTRATION: The study was registered on Clinicaltrials.gov (NCT05310786) on April 5, 2022, https://www.clinicaltrials.gov/study/NCT05310786?id=NCT05310786&rank=1 

BACKGROUND: Despite the high prevalence of alcohol use disorders (AUDs), in 2016, only 7.8% of individuals meeting diagnostic criteria received any type of AUD treatment. Developing options for treatment within primary care settings is imperative to increase treatment access. As part of a trial to implement AUD pharmacotherapy in primary care settings, this qualitative study analyzed pre-implementation provider interviews using the Consolidated Framework for Implementation Research (CFIR) to identify implementation barriers. METHODS: Three large Veterans Health Administration facilities participated in the implementation intervention. Local providers were trained to serve as implementation/clinical champions and received external facilitation from the project team. Primary care providers received a dashboard of patients with AUD for case identification, educational materials, and access to consultation from clinical champions. Veterans with AUD diagnoses received educational information in the mail. Prior to the start of implementation activities, 24 primary care providers (5-10 per site) participated in semi-structured interviews. Transcripts were analyzed using common coding techniques for qualitative data using the CFIR codebook Innovation/Intervention Characteristics, Outer Setting, Inner Setting, and Characteristics of Individuals domains. Number and type of barriers identified were compared to quantitative changes in AUD pharmacotherapy prescribing rates. RESULTS: Four major barriers emerged across all three sites: complexity of providing AUD pharmacotherapy in primary care, the limited compatibility of AUD treatment with existing primary care processes, providers' limited knowledge and negative beliefs about AUD pharmacotherapy and providers' negative attitudes toward patients with AUD. Site specific barriers included lack of relative advantage of providing AUD pharmacotherapy in primary care over current practice, complaints about the design quality and packaging of implementation intervention materials, limited priority of addressing AUD in primary care and limited available resources to implement AUD pharmacotherapy in primary care. CONCLUSIONS: CFIR constructs were useful for identifying pre-implementation barriers that informed refinements to the implementation intervention. The number and type of pre-implementation barriers identified did not demonstrate a clear relationship to the degree to which sites were able to improve AUD pharmacotherapy prescribing rate. Site-level implementation process factors such as leadership support and provider turn-over likely also interacted with pre-implementation barriers to drive implementation outcomes.

OBJECTIVE: A rural primary care clinic implemented an advance practice providers, including nurse practitioner (APRN)-led integrated behavioral health program to facilitate holistic health care delivery. METHODS: Implementation was facilitated by Health Resources and Services Administration Grant funding to a state University College of Nursing. The College formed an academic-practice partnership with a Federally Qualified Health Center (FQHC) to implement integrated care in a rural satellite clinic administered by the FQHC. An interdisciplinary team (two family APRNs, a psychiatric APRN, a licensed behavioral health provider, and the Grant Project Director who is a Psychiatric APRN and a licensed Psychologist) provided the integrated care based on the University of Washington's Collaborative Care Model. RESULTS: This brief report describes the implementation of integrated care during the clinic's first year, services provided, lessons learned, community response, and improvement in anxiety and depressive symptoms for patients who were treated for behavioral health problems. An exemplar illustrates how collaborative care addressed one patient's behavioral health and primary care needs. CONCLUSIONS: APRN-led collaborative care can expand access to holistic, affordable care in rural areas to improve mental health. Adaptation and flexibility in traditional roles may be necessary and determining post-grant access to funding for services will be necessary for sustainability.

OBJECTIVE: A rural primary care clinic implemented an advance practice providers, including nurse practitioner (APRN)-led integrated behavioral health program to facilitate holistic health care delivery. METHODS: Implementation was facilitated by Health Resources and Services Administration Grant funding to a state University College of Nursing. The College formed an academic-practice partnership with a Federally Qualified Health Center (FQHC) to implement integrated care in a rural satellite clinic administered by the FQHC. An interdisciplinary team (two family APRNs, a psychiatric APRN, a licensed behavioral health provider, and the Grant Project Director who is a Psychiatric APRN and a licensed Psychologist) provided the integrated care based on the University of Washington's Collaborative Care Model. RESULTS: This brief report describes the implementation of integrated care during the clinic's first year, services provided, lessons learned, community response, and improvement in anxiety and depressive symptoms for patients who were treated for behavioral health problems. An exemplar illustrates how collaborative care addressed one patient's behavioral health and primary care needs. CONCLUSIONS: APRN-led collaborative care can expand access to holistic, affordable care in rural areas to improve mental health. Adaptation and flexibility in traditional roles may be necessary and determining post-grant access to funding for services will be necessary for sustainability.

IMPORTANCE: Sleep is crucial for healthy growth, academic success, executive functioning, and mental health. However, sleep is not consistently and rigorously addressed in pediatric primary care. OBJECTIVE: To describe the development and reach, adoption, implementation, effectiveness, and maintenance of a well-child visit, electronic sleep screener with educational resources in a large primary care network. DESIGN, SETTING, AND PARTICIPANTS: In this case-control study of primary care practices in Pennsylvania and New Jersey, retrospective, observational electronic health records and implementation data were drawn for preimplementation (November 1, 2018, to September 30, 2019), phased-scaling (October 1, 2019, to June 30, 2021), implementation (July 1, 2021, to June 30, 2022), and maintenance (July 1, 2022, to June 30, 2023) periods. Multivariate logistic regression examined the effectiveness by comparing implementation vs preimplementation rates of sleep disorder diagnosis, polysomnogram orders, and sleep-related referrals. Patients were seen for a well-child visit during the preimplementation and implementation periods, without exclusions. Data were analyzed from October 10, 2023, to May 2, 2025. EXPOSURE: An age-based, electronic sleep screener assessing infant bed sharing, frequent snoring (≥3 nights/week), perceived sleep problems, insufficient sleep duration, and adolescent daytime sleepiness. MAIN OUTCOMES AND MEASURES: The Reach, Effectiveness, Adoption, Implementation, and Maintenance framework guided the outcomes including sleep screener use, results, and primary care clinician (PCC)-rendered sleep disorder diagnosis, polysomnogram orders, and sleep-related referrals at the well-child visit. RESULTS: A total of 409 217 well-child visits for 288 307 unique patients aged 18 years or younger (51.2% male; 49.9% White non-Hispanic or Latine) were included in the preimplementation and implementation periods. During implementation, 204 872 unique patients in 31 practices completed the screener, with adoption in 89.5% of all well-child visits. Overall, 9.7% of patients endorsed frequent snoring, 12.2% sleep problems, and 34.4% insufficient sleep. Infant bed sharing was endorsed in 6.5% of infants, whereas 14.7% of adolescents endorsed daytime sleepiness. Compared with the preimplementation period, at well-child visits with a completed sleep screener, PCCs were significantly more likely to render a sleep disorder diagnosis (odds ratio, 1.64 [95% CI, 1.56-1.73]), order a polysomnogram (odds ratio, 2.67 [95% CI, 2.32-3.20]), and refer to sleep (odds ratio, 6.48 [95% CI, 5.03-8.34]) or otolaryngology (odds ratio, 4.46 [95% CI, 3.95-5.02]) clinics. Minimal adaptations occurred during implementation, and adoption was high and persistent (92.5% of well-child visits) during the maintenance period. CONCLUSIONS AND RELEVANCE: In this case-control study, a brief, electronic well-child visit sleep screener was widely adopted and maintained in a sociodemographically diverse primary care network and was associated with increased recognition and management of sleep problems.