Evidence-based digital therapeutics are a promising approach for the scale-up of substance use disorder (SUD) treatments. Despite demonstrated efficacy, utilization of digital therapeutics is low. Strategic implementation approaches have potential for increasing digital therapeutic use. Applicability to health systems depends, in part, on the economic costs. The objective of this study was to describe implementation and intervention costs of implementation strategies to increase uptake of an evidence-based digital treatment for SUD. We conducted an economic evaluation alongside a hybrid type III cluster-randomized trial within a large integrated health system. All clinics implemented a standard implementation (SI) strategy, and clinics were assigned using 2x2 factorial randomization to additionally receive practice facilitation (PF) and/or health coaching (HC). Implementation costs included the cost of time devoted to implementation activities and direct operating costs. Time devoted to implementation activities was ascertained through structured meeting logs and time use surveys. Operating costs were captured using project budget reports. Intervention costs included expenses for prescriptions and healthcare encounters related to the digital therapeutic, measured using electronic health record data. Univariate statistics were calculated for cost estimates with comparisons presented by trial arm, implementation activity, staff role and study month. Analyses were conducted from a health system perspective. Twenty-one primary care sites participated in the trial. Over the 50-month study period, the total cost of all implementation activities was $748,088. Implementation costs per clinic were highest in the SI + PF + HC arm ($48,029), followed by SI + HC ($36,544), SI + PF ($30,665) and SI alone ($24,774). Intervention costs were highest in the SI + PF + HC arm ($18,051), followed by SI + PF ($11,492), SI + HC ($967) and SI alone ($1,879). Findings from this study can guide health systems by informing the economic investment required to employ implementation strategies demonstrated to increase uptake of evidence-based practices for behavioral health conditions.Trial Registration: NCT05160233.

INTRODUCTION:Primary care patients with opioid use disorder (OUD) may receive treatment in primary care clinics or co-located specialty addiction treatment practices. To help guide operational leaders in organizing OUD care delivery systems, we described rates of OUD medication treatment among primary care patients in PRimary care Opioid Use Disorders treatment (PROUD) trial intervention clinics and four primary care clinics not in the trial because they already had OUD treatment programs in place (exemplar clinics). METHODS:Primary care patients seen at six PROUD trial intervention clinics that implemented the Massachusetts model of office-based addiction treatment (PROUD clinics) and four exemplar clinics (two co-located specialty models; two primary care models with universal prescribing, in which all primary care providers were expected to treat OUD) were compared. Primary outcomes were person-years (PY) of medication treatment for OUD with buprenorphine or extended-release naltrexone during follow up (3/2018-2/2020) and changes from baseline (3/2016-2/2018). RESULTS:Baseline primary care samples included 109,196 patients in PROUD clinics and 101,631 patients in exemplar clinics. Baseline OUD treatment rates varied across exemplar clinics (range: 10.9 to 328.7 PY per 10,000 primary care patients) but were higher than in PROUD clinics at baseline (3.9 PY per 10,000), with exemplar clinics with primary care models (established 2005 and 2017) providing the highest treatment rates to their primary care patients. During follow-up, PROUD clinics nearly tripled treatment, to 14.4 PY per 10,000, whereas most exemplar clinics increased treatment by less than 10% but still had higher treatment rates (range: 12.0 to 359.4 PY per 10,000). CONCLUSIONS:Primary care OUD treatment rates varied markedly. Exemplar clinics in which all primary care providers were expected to treat OUD had the highest treatment rates at baseline and follow-up, suggesting that universal prescribing is a promising approach to increasing OUD treatment in primary care.

Alcohol and drug use screening in primary care is recommended by the US Preventive Services Taskforce, yet multi-level correlates of screening, and measurement properties of screening metrics, are unclear. This study characterized the reliability, variability, and multi-level factors associated with guideline-concordant alcohol and drug screening. Retrospective longitudinal cohort study. A total of 1,097,127 randomly selected visit observations of 479,634 adult primary care patients (2019–2022) from one healthcare system in the US Pacific Northwest. Patients contributed up to one observation per year. The primary outcome was completion of substance use screening on the index visit date or in the prior year. Mixed-effects models estimated the probability of guideline-concordant screening completion. Intraclass correlation coefficients (ICCs) quantified variability at patient, provider, clinic, and county levels, and the Spearman-Brown prophecy formula estimated reliability. Average marginal effects generated from logistic regression models quantified associations between patient-, provider-, clinic-, and community-level characteristics and screening completion. Patients resided in 428 counties and visited 802 primary care providers across 36 clinics. Screening was present for 80

Healthcare systems often struggle to provide timely access to care for patients with substance use disorder, and many patients do not successfully engage with treatment. Care navigation interventions have emerged as a strategy to help patients navigate complex health systems, address barriers to care, and improve treatment initiation and engagement, but little information exists about how to optimize these interventions within health system settings. In preparation for the Addressing Barriers to Care for Substance Use Disorder Trial, a hybrid effectiveness-implementation study conducted in a large integrated healthcare system, we undertook a series of design activities including piloting, stakeholder engagement, and iterative process redesign. As part of this broader effort, this qualitative study elicited the perspectives of patients and clinicians on barriers and facilitators to substance use treatment and ways to optimize the implementation of care navigation within the healthcare system’s mental health access center. We conducted semi-structured interviews with 21 patients who sought treatment for substance use disorder and eight clinicians working in the mental health access center who assessed and referred patients to treatment. Interview transcripts were analyzed using the Rapid Group Analysis Process method, a novel rapid qualitative analysis method, to identify crosscutting themes related to care navigation. Analyses identified several crosscutting themes that conveyed ways that care navigators could help connect patients to substance use treatment: by communicating effectively with patients, by leveraging substance use disorder resources, by supporting patient motivation, and by addressing logistical barriers to treatment. These themes closely aligned with the study’s existing care navigation protocol. Findings also informed several refinements to improve the fit of care navigation within the health system’s existing workflows. Identification of crosscutting themes via qualitative interviews confirmed the relevance of care navigation to the study population and helped optimize the intervention within the healthcare system. This study provides an example of how to incorporate stakeholder perspectives during intervention development and early implementation, particularly when adapting evidence-based approaches for new populations and settings. The clinical trial referenced in this study was prospectively registered at www.clinicaltrials.gov (NCT06729957) on December 9, 2024.

Abstract Background Randomised implementation trials evaluate the effects of implementation strategies on implementation outcomes and may also monitor clinical effectiveness. Routine healthcare data are used in implementation trials for participant identification, intervention delivery, and/or outcome ascertainment. Trial efficiency (scientific, operational, statistical, and economic) is operationalised across trial design, processes, superstructure, infrastructure, and stakeholder engagement (the Trial Efficiency Pentagon). Despite frequent usage, the contribution of routine data to implementation trial efficiency remains underexplored. We aimed to investigate how the use of routine healthcare data affects trial efficiency in two implementation trials. Methods We conducted a qualitative comparative case study of two implementation trials, one UK-based and one US-based. Participants were purposively sampled from trial teams involved in the use and management of routine healthcare data. Data were collected through semi-structured interviews, document analysis, and feedback workshops. Framework analysis guided by the Trial Efficiency Pentagon was used to analyse the data, and data flow diagrams were developed to visualise routine data pathways within each trial. Results The two trials (DIGITS and IMP2ART) used routine data to characterise the practice population of eligible patients, support clinical and economic outcome evaluation, facilitate audit and feedback, and assist in intervention delivery. Common facilitators that supported the use of routine data included sufficient IT and hardware capacity, relatively low cost, centralised regulatory approval for multi-site studies, and strong collaboration and partnerships. Common barriers included administrative complexity, redundant bureaucratic processes, and challenges with data sharing requirements. Key differences included the DIGITS trial’s in-house data warehouses within an integrated healthcare system ensured high data quality and enabled preliminary analyses. In contrast, the IMP2ART trial, managing a larger national sample, employed an external research database to integrate data from various EHR systems but faced challenges such as legacy systems, diverse coding practices and site-specific approvals. Data quality can act as either a facilitator or a barrier. Conclusions Routine data has an impact on implementation trial efficiency across trial design, processes, superstructure, infrastructure, and stakeholder engagement. To improve trial efficiency in public healthcare systems, researchers must address technological and regulatory barriers to accessing data. In private healthcare systems, data use and access hinges on investing in robust IT infrastructure and ensuring comprehensive organisational commitment. Trial registration IMP2ART trial registration: ISRCTN15448074; DIGITS trial Clinicaltrials.gov Identifier: NCT05160233.

Abstract BackgroundMany substance use disorder (SUD) care pathways exist in health care systems. However, patients with SUD often report poor care experiences, particularly regarding timely follow-up, clinician and general satisfaction ratings, and care communication. As SUD care pathways involve transitions across clinicians and venues of care, adequate treatment requires coordination across clinicians and settings to ensure unmet needs are addressed, and appropriate SUD care is delivered. Surprisingly little is known about the real-world care pathways patients engage in once identified as having SUD. ObjectiveThis study protocol describes research that will comprehensively characterize the pathways of care used by those who obtain care for SUD and compare the quality and outcomes associated with these care pathways. Specific aims are to (1) apply multistate models (MSM) to characterize the spectrum of care transitions among patients with SUD within a large integrated health system, (2) generate data-driven insights to improve care delivery for SUD using estimated MSMs, and (3) observe and explore patient and clinician experiences with care transitions across common care pathways using qualitative methods. MethodsQuantitative data sources will include electronic health care records, insurance claims, self-reported measures of substance use and SUD symptoms, and death data from an integrated health care system in Washington State. To identify care pathways, we will apply continuous time, multistate modeling methods to empirically observe the longitudinal course of SUD care transitions undertaken by patients over time; each “state” or occurrence of care will be characterized by the intervention received (eg, evaluation or assessment, behavioral treatment or counseling, and pharmacotherapy) and setting of care (eg, outpatient, intensive outpatient, or inpatient or residential). The estimated parameters from the fitted MSMs will be transformed or interpreted to characterize SUD care quality, such as wait times for SUD visits, and receiving an adequate psychotherapy dose. To compare outcomes associated with care pathways, we will examine terminal states, including death and loss to follow-up. Using a mixed methods design, we will sample and interview patients engaged in empirically derived pathways to understand their care experiences, observe and interview clinicians to elucidate health system factors that impact SUD care transitions, and integrate qualitative and quantitative findings using joint displays. ResultsThis study was funded in June 2025 and received institutional review board approval on July 17, 2025. We expect preliminary data collection for quantitative analyses to be complete by May 2026, and final data collection will be complete by November 2027. We expect qualitative data collection completion by November 2029. ConclusionsThis exploratory study will identify and compare the quality and outcomes associated with common pathways that patients take when they obtain treatment for SUD and provide decision-makers with information on how to better organize SUD care delivery.

Objectives:In a sample of patients with diagnosed alcohol use disorder (AUD), we examined the prevalence of both stigmatizing (eg, "alcohol abuse") and highly stigmatizing, non-medical descriptors (eg, "alcoholic") documented in electronic health records (EHRs). We also assessed whether the use of these terms-entered by clinicians when documenting AUD using search tools and picklists-varied across patient subgroups within a regional health system. Study Design:Cross-sectional study. Measurements:This study used EHR data from AUD diagnoses documented (January 3, 2015-May 5, 2023) for adult primary care patients identified as Asian, Black, Latine, or White. Four researchers classified AUD descriptors used to diagnose AUD as "stigmatizing" or "highly stigmatizing." Stigmatizing AUD descriptors were terms that carry negative connotations, imply blame, moral failing, or character flaws. Highly stigmatizing AUD descriptors were non-standard medical terms with strong negative connotations. Results:Among 61 886 AUD diagnoses (18 068 patients: 3.5% Asian, 5.0% Black, 5.9% Latine, 85.6% White; 40.5% women), stigmatizing descriptors were used in 88.5% and highly stigmatizing in 18.6% of diagnoses. Differences across intersectional subgroups were minimal. Conclusion:Most AUD descriptors were stigmatizing (1 in 5 highly stigmatizing), with men showing slightly higher prevalence than women.

INTRODUCTION:Understanding conditions in which interventions succeed or fail is critical. The PRimary care Opioid Use Disorders treatment (PROUD) trial, a cluster-randomized hybrid study, tested whether implementation of office-based addiction treatment supported by a nurse increased medication of OUD. Six health systems each provided two primary care (PC) clinics that were randomly assigned to implement the intervention or usual care. This secondary, exploratory study used an innovative mixed methods approach to understand contextual factors that consistently distinguished intervention clinics that increased OUD treatment from those that did not. METHODS:The study collected contextual information through field notes, health system debriefs, and nurse interviews. Rapid qualitative analysis using a template based on the Practical, Robust Implementation and Sustainability Model identified themes reflecting the external environment, recipients, and implementation infrastructure. The study used qualitative themes to create binary factors reflecting barriers and facilitators potentially critical to implementation success and assigned clinics a factor value of 1 if present and 0 if absent. Two clinic-level outcomes were defined: 1) significant increase in patient-years of OUD treatment from baseline to two-year follow-up; and 2) high rate of OUD treatment at two-year follow-up (≥20 per 10,000 patient-years). Coincidence analysis, a cross-case configurational method, identified difference-makers for both OUD outcomes across intervention clinics. RESULTS:Qualitative analysis yielded 11 themes which were dichotomized and consolidated into 9 factors. Two factor values perfectly distinguished between intervention clinics with and without increased OUD treatment (outcome #1): (a) presence of strong support from PC staff and providers and (b) lack of OUD treatment in the community. Intervention clinics increased OUD treatment when either factor value was present; when both were absent, clinics did not increase treatment. Strong support from PC staff and providers was independently sufficient to achieve high rates of OUD treatment (outcome #2) while the absence of support explained low rates of treatment. Importantly, strong support from leadership was not sufficient for either outcome. CONCLUSION:Strong support from staff and providers consistently differentiated between clinics with increased OUD treatment across both outcomes in the PROUD trial from those without. OUD programs should consider increasing support across clinic roles.

INTRODUCTION:Prior studies have highlighted potential inequities in provider-documented alcohol use disorder (AUD) across race, ethnicity, and sex. Whether subgroup differences in AUD reflect true variation or diagnostic disparities is unknown. This study aims to describe variations in the prevalence of provider-documented AUD across race, ethnicity, and sex: 1) after adjustment for alcohol consumption, and 2) after additional adjustment for patient-reported AUD symptoms. METHODS:In Kaiser Permanente Washington, patients with high-risk drinking (AUDIT-C score 7-12; 2.4 % of screened patients) complete a validated Alcohol Symptom Checklist of DSM-5 AUD symptoms with results documented in electronic health records. This study included Asian, Black, Latine, and White patients in primary care settings (03/2015-02/2022) who indicated high-risk drinking and thus completed an Alcohol Symptom Checklist. The prevalence of AUD was estimated for women and men across race or ethnic groups using marginally standardized generalized linear models. Models were first unadjusted, then adjusted for consumption (AUDIT-C scores 7-12), and then consumption plus AUD symptom counts (0-11). RESULTS:Among 14,442 patients with high-risk drinking (6.0 % Asian, 5.8 % Black, 7.8 % Latine, 80.4 % White; 32.1 % women), provider-documented AUD increased with alcohol consumption and the number of AUD symptoms. The prevalence of AUD across 8 subgroups defined by race, ethnicity, and sex varied in analyses adjusted for alcohol consumption alone (range 11.6 % [95 % CI: 9.3-14.4] to 20.2 % [18.9-21.5]). However, after adjustment for both alcohol consumption and AUD symptoms, the prevalence of AUD ranged from 11.2 % [95 % CI: 7.9-15.6] to 15.0 % [95 % CI: 13.9-16.3] in women, and from 11.0 % [95 % CI: 8.7-13.8] to 15.1 % [95 % CI: 14.3-16.0] in men. AUD did not appear to vary across race or ethnicity. CONCLUSIONS:In this study of primary care patients with high-risk drinking in a regional healthcare system that routinely assesses AUD symptoms, variations in provider-documented AUD diagnosis across race, ethnicity, and sex were observed after adjusting for alcohol consumption but were diminished after adjusting for AUD symptoms. This may suggest that among patients with similar alcohol consumption and AUD symptoms, intersectional variations in AUD diagnosis may be less apparent. Assessing AUD severity with Alcohol Symptom Checklists may help support equitable clinical AUD diagnosing.

This article proposes methods for designing randomized controlled trials studying the implementation and effectiveness of digital interventions, meaning websites or applications ("apps") that patients use in healthcare. Deploying digital interventions for behavioral health differs from implementing traditional interventions such as medications or human-delivered therapy. Prior trial design guidance has ignored the existence of international governmental evidence standards, has paid insufficient attention to implementation reporting guidelines, and has not described methods for empirically testing the approach for organizing the delivery of digital interventions. This framework for designing hybrid effectiveness-implementation trials of digital behavioral health interventions helps researchers articulate research questions that matter to decision-makers and meaningfully contribute to implementation. The framework outlines three phases: 1) frame effectiveness and implementation questions in terms of the digital intervention components, types of clinical support for the digital intervention, and specific strategies for implementing the digital intervention; 2) define and delineate actors, activities, action targets, dose, temporality, and outcomes to maximize inference and reproducibility; and 3) specify trial design features used for hybrid classification. We illustrate the utility of this framework with two effectivenessimplementation studies of digital interventions for substance use. This framework can help researchers decide on appropriate methodology and help decision-makers apply findings.

Objective:Medications for opioid use disorder (OUD) are under-utilized among adolescents and young adults ("youth"). Offering buprenorphine or naltrexone in primary care settings may reduce barriers to their use among youth. We conducted a secondary, patient-level analysis of the PROUD cluster-randomized clinical trial, which tested the implementation of a nurse care management intervention to support prescribing OUD medications. Methods:12 primary care clinics from 6 health systems were randomized in 2018 and patient-level data was collected from 2 years before to 2 years after randomization. The primary outcome was any OUD medication treatment (i.e., buprenorphine or extended-release injectable naltrexone) during the post-randomization period for youth ages 16-25 years. Results:A total of 20,253 youth ages 16-25 years were seen in intervention and 26,562 in usual care clinics during the study period. Comparing patients by clinic arm, we did not detect a statistically significant difference in the odds of receiving OUD medication treatment after randomization (odds ratio 1.75, 95% CI 0.63-4.89). Among the small number of patients (n=67) who received OUD medication after randomization, median treatment days were 81.5 days (IQR 30-177) and 64 days (IQR 24-206) in intervention or usual care clinics, respectively. Conclusions:We did not find evidence that implementing a primary care nurse care management model meaningfully increased OUD medication treatment among youth. In this special population, youth-centered approaches may be needed to promote prescribing and overcome known barriers to care, such as provider and patient hesitancy to use OUD medications.

BackgroundScreening for certain types of substance use is common in health care. Screening for social risks-modifiable social conditions affecting health-is less common. Understanding how binge drinking, cannabis use, and tobacco use co-occur with social risks could inform interventions targeting these factors simultaneously.ObjectiveExamine the relationship between binge drinking, tobacco, and cannabis use and social risks among US health care users.DesignRetrospective population-based cross-sectional study.ParticipantsAdults from 15 US states or territories who participated in the 2022 Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System survey and had a health care visit in the past 2 years (N = 76,891).Main MeasuresSocial risks included employment loss/reduction, receipt of food stamps, difficulty paying for food, inability to pay bills, threat of utility shutoff, and lack of reliable transportation. Exposures were the presence and frequency of past-month binge drinking, cannabis use, or nicotine product use. We assessed rates of modifiable social risks by substance use variables and calculated marginal effects adjusting for age, sex, race/ethnicity, and education.Key ResultsOverall, 30.9% reported at least one social risk. Difficulty paying for food was the most common (12.4%). The adjusted prevalence of having any social risks was elevated among individuals with past-month cannabis or tobacco use (e.g., 42.1% and 42.6%, respectively), but not among those with past-month binge drinking (29.3%). However, individuals with daily binge drinking, cannabis use, or tobacco use had substantially increased social risks. The probability of experiencing social risks increased with the number of substances used.LimitationsA cross-sectional survey.ConclusionsCannabis and tobacco may signal underlying social risks, even at low frequency. Binge drinking was associated with social risks only when it occurred daily. Future research should determine whether addressing substance use can reduce social risks, and vice versa, within integrated health care services.

Practical and motivational barriers can deter people from engaging in substance use disorder (SUD) treatment, even those who seek treatment. Care navigation is a psychosocial intervention that seeks to facilitate patients’ timely access to care by identifying and intervening upon barriers. Few trials have tested the effectiveness of care navigation when embedding in real-world healthcare, and no trials have studied the process of implementing care navigation into clinical practice. This protocol describes a study that will evaluate whether care navigation can increase treatment engagement among patients seeking SUD treatment. The Addressing Barriers to Care for Substance Use Disorder (ABC-SUD) study is a hybrid type I cluster-randomized effectiveness-implementation trial. It is conducted in a mental health access center of an integrated healthcare system in Washington state. Within this center, licensed mental health clinicians assess patient needs and use shared decision-making to establish SUD treatment plans for patients (usual care). This study tests whether an added care navigation intervention can improve patient engagement in SUD treatment. Care navigation begins after a treatment plan is made and provides up to 7 weeks of support focused on enhancing patient motivation to initiate and engage in treatment, problem-solving barriers (e.g., transportation logistics), and accommodating patient preferences (e.g., preferred language of care, cultural preferences). This trial uses a two period, two sequence crossover design. Clinicians are randomized to offer care navigation to patients during the first or second study period (i.e., clinicians are assigned to an initial study condition and switch conditions halfway through the trial). Care navigation is implemented with several strategies: leadership engagement, clinical workflow specifications, electronic health record (EHR) tools, training, performance improvement, and electronic learning collaborative. The primary outcome—obtained from EHRs and insurance claims—is engagement in SUD treatment, defined as ≥3 SUD treatment visits within 48 days of a treatment plan. This study uses standardized measures of implementation climate and outcomes to examine mechanisms with which the intervention strategies exert their impact on implementation and effectiveness outcomes. The ABC-SUD study will test whether care navigation improves SUD treatment engagement while concurrently generating information about its implementation in healthcare. This study was prospectively registered at www.clinicaltrials.gov (NCT06729957) on December 9, 2024.

BackgroundExisting literature shows that persons with substance use disorder (SUD) experience different stages of readiness to reduce or abstain from substance use, and tailoring intervention change strategies to these stages may facilitate recovery. Moreover, stigma may serve as a barrier to recovery by preventing persons with SUDs from seeking treatment. In recent years, the behavior change technique (BCT) taxonomy has increasingly become useful for identifying potential efficacious intervention components; however, prior literature has not addressed the extent to which these techniques may naturally be used to recover from substance use, and knowledge of this may be useful in the design of future interventions. ObjectiveWe take a three-step approach to identifying strategies to facilitate substance use recovery: (1) characterizing the extent to which stages of change are expressed in social media data, (2) identifying BCTs used by persons at different stages of change, and (3) exploring the role that stigma plays in recovery journeys. MethodsWe collected discussion posts from Reddit, a popular social networking site, and identified subreddits or discussion forums about 3 substances (alcohol, cannabis, and opioids). We then performed qualitative data analysis using a hybrid inductive-deductive method to identify the stages of change in social media authors’ recovery journeys, the techniques that social media content authors used as they sought to quit substance use, and the role that stigma played in social media authors’ recovery journeys. ResultsWe examined 748 posts pertaining to 3 substances: alcohol (n=316, 42.2%), cannabis (n=335, 44.8%), and opioids (n=135, 18%). Social media content representing the different stages of change was observed, with the majority (472/748, 63.1%) of narratives representing the action stage. In total, 11 categories of BCTs were identified. There were similarities in BCT use across precontemplation, contemplation, and preparation stages, with social support seeking and awareness of natural consequences being the most common. As people sought to quit or reduce their use of substances (action stage), we observed a variety of BCTs, such as the repetition and substitution of healthful behaviors and monitoring and receiving feedback on their own behavior. In the maintenance stage, reports of diverse BCTs continue to be frequent, but offers of social support also become more common than in previous stages. Stigma was present throughout all stages. We present 5 major themes pertaining to the manifestation of stigma. ConclusionsPatterns of BCT use and stigmatizing experiences are frequently discussed in social media, which can be leveraged to better understand the natural course of recovery from SUD and how interventions might facilitate recovery from substance use. It may be important to incorporate stigma reduction across all stages of the recovery journey.

BACKGROUND:The National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) has supported clinical trials of substance use disorder (SUD) interventions for 25 years. This review describes the use of implementation outcomes across CTN trials, characterizes outcomes included, and identifies gaps and potential opportunities to strengthen implementation research within the CTN and the field of SUD treatment. METHODS:This systematic review included active or completed studies listed on the CTN Dissemination Library webpage as of August 18, 2021, and approved by the CTN for development by January 1, 2022. Study summaries and protocols were reviewed if they: 1) measured at least one implementation outcome and 2) examined a practice change, intervention, or process. Extracted data elements included trial design characteristics, implementation frameworks, and outcome assessment domains informed by the RE-AIM and Proctor Implementation Outcomes Frameworks. RESULTS:114 protocols were considered, 42 full-text protocols were screened, and 25 were included for data extraction. Start dates of trials spanned a 20-year period (2004-2024) with latter studies including more implementation outcomes. Fidelity (n = 29) and reach/penetration (n = 26) were the most included implementation outcomes. Equity was not identified in any protocols. Methods of defining, capturing, and evaluating outcomes data varied across trials and outcomes. CONCLUSION:The inclusion of implementation outcomes increased over time, perhaps reflecting a growing emphasis on implementation research. Incorporating measures of equity could advance knowledge about differential receipt or effectiveness of SUD interventions. Future research should seek to improve the consistency and comprehensiveness in descriptions of implementation science elements.

Designing effectiveness studies with implementation in mind can allow interventionists to translate their research into real-world practice. Implementation outcomes measure how much and how well a particular intervention was implemented and can provide valuable insights into any heterogeneity in effectiveness outcomes. As part of the National Institutes of Health’s (NIH) Helping to End Addiction Long-term® (HEAL) Initiative’s Data2Action program, a workgroup of the Research Adoption Support Center (RASC) was tasked with creating a resource guide to aid clinical interventionists in integrating implementation outcomes into their research plans. This paper aims to provide a plain-language, pragmatic guide to implementation outcomes for clinical interventionists, including key considerations for each outcome and examples of implementation-effectiveness studies that reported implementation outcomes. We conclude by discussing the limitations of our guide and implementation outcome reporting more broadly. We offer suggestions for implementation scientists and clinical interventionists to work toward a common goal of improved implementation outcome reporting.

BACKGROUND:Research on use experience and recovery has often focused on a single substance or polysubstance use. However, there can be substance-specific differences; understanding these can be critical to developing targeted interventions. We examined social media relating to alcohol, cannabis, and/or opioids to: 1) construct use profiles highlighting salient settings, actors, and contextual factors; and 2) characterize differences in recovery strategies depending on readiness to change. METHODS:We constructed a dataset of Reddit posts from subreddits pertaining to alcohol, cannabis, and opioids, authored between January 2013 and December 2019. We leveraged computational techniques to sample posts containing stigma, logistic regression to compare substance use experiences, and content analysis to identify stages of change and recovery strategies. RESULTS:We examined 748 posts (alcohol, n = 316; cannabis, n = 335; opioids, n = 135). Regression models indicated leisure settings, coworkers, health, and legal consequences were associated with alcohol versus other substances. Posts involving cannabis were more likely to include school, and heightened self-awareness, demonstrated through curiosity, disgust, and realization. Posts involving opioids were more likely to include anticipated stigma, anger, healthcare, medications, financial, and religious content; they were less likely to include home and leisure. With respect to recovery strategies, social support seeking and awareness of substance use consequences were more common in earlier stages of readiness. In the action and maintenance stages, there was greater use of recovery strategies overall. CONCLUSIONS:Substance-specific use profiles highlighted salient settings, actors, and contextual factors. Recovery strategies were also differentiated across stages of change, affording opportunities for treatment and intervention.