Prescription Digital Therapeutics for Substance Use Disorder in Primary Care: Mixed Methods Evaluation of a Pilot Implementation Study.

BACKGROUND: Delivering prescription digital therapeutics (ie, evidence-based interventions designed to treat, manage, or prevent disorders via websites or smartphone apps) in primary care could increase patient access to substance use disorder (SUD) treatments. However, the optimal approach to implementing prescription digital therapeutics in primary care remains unknown. OBJECTIVE: This pilot study is a precursor to a larger trial designed to test whether implementation strategies (practice facilitation [PF] and health coaching [HC]) improve the delivery of prescription digital therapeutics for SUDs in primary care. This mixed methods study describes outcomes among patients in the 2 pilot clinics and presents qualitative findings on implementation. METHODS: From February 10 to August 6, 2021, a total of 3 mental health specialists embedded in 2 primary care practices of the same integrated health system were tasked with offering app-based prescription digital therapeutics to patients with SUD. In the first half of the pilot, implementation activities included training and supportive tools. PF (at 1 clinic) and HC (at 2 clinics) were added in the second half. All study analyses relied on secondary data, including electronic health records and digital therapeutic vendor data. Primary outcomes were the proportion of patients reached by the prescription digital therapeutics and fidelity related to ideal use. We used qualitative methods to assess the adherence to planned activities and the barriers and facilitators to implementing prescription digital therapeutics. RESULTS: Of all 18 patients prescribed the apps, 10 (56%) downloaded the app and activated their prescription, and 8 (44%) completed at least 1 module of content. Patients who activated the app completed 1 module per week on average. Ideal use (fidelity) was defined as completing 4 modules per week and having a monthly SUD-related visit; 1 (6%) patient met these criteria for 10 weeks (of the 12-week prescription period). A total of 5 (28%) patients had prescriptions while HC was available, 2 (11%) were successfully contacted, and both declined coaching. Clinicians reported competing clinical priorities, technical challenges, and logistically complex workflows in part because the apps required a prescription. Some pilot activities were impacted by staff turnover that coincided with the COVID-19 pandemic. The facilitators to implementation were high engagement and the perception that the apps could meet patient needs. CONCLUSIONS: The pilot study encountered the barriers to implementing prescription digital therapeutics in a real-world primary care setting, especially staffing shortages, turnover, and competing priorities for clinic teams. The larger randomized trial will clarify the extent to which PF and HC improve the implementation of digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT04907045; https://clinicaltrials.gov/study/NCT04907045.

Prevalence of alcohol use disorders documented in electronic health records in primary care across intersections of race or ethnicity, sex, and socioeconomic status.

BACKGROUND: Diagnosis of alcohol use disorder (AUD) in primary care is critical for increasing access to alcohol treatment. However, AUD is underdiagnosed and may be inequitably diagnosed due to societal structures that determine access to resources (e.g., structural racism that limits opportunities for some groups and influences interpersonal interactions in and beyond health care). This study described patterns of provider-documented AUD in primary care across intersections of race, ethnicity, sex, and community-level socioeconomic status (SES). METHODS: This cross-sectional study used EHR data from a regional healthcare system with 35 primary care clinics that included adult patients who completed alcohol screenings between 3/1/2015 and 9/30/2020. The prevalence of provider-documented AUD in primary care based on International Classification of Diseases-9 (ICD-9) and ICD-10 diagnoses was compared across intersections of race, ethnicity, sex, and community-level SES. RESULTS: Among 439,375 patients, 6.6% were Latine, 11.0% Asian, 5.4% Black, 1.3% Native Hawaiian/Pacific Islander (NH/PI), 1.5% American Indian/Alaska Native (AI/AN), and 74.2% White, and 58.3% women. The overall prevalence of provider-documented AUD was 1.0% and varied across intersecting identities. Among women, the prevalence was highest for AI/AN women with middle SES, 1.5% (95% CI 1.0-2.3), and lowest for Asian women with middle SES, 0.1% (95% CI 0.1-0.2). Among men, the prevalence was highest for AI/AN men with high and middle SES, 2.0% (95% CI 1.1-3.4) and 2.0% (95% CI 1.2-3.2), respectively, and lowest for Asian men with high SES, 0.5% (95% CI 0.3-0.7). Black and Latine patients tended to have a lower prevalence of AUD than White patients, across all intersections of sex and SES except for Black women with high SES. There were no consistent patterns of the prevalence of AUD diagnosis that emerged across SES. CONCLUSION: The prevalence of provider-documented AUD in primary care was highest in AI/AN men and women and lowest in Asian men and women. Findings of lower prevalence of provider-documented AUD in Black and Hispanic than White patients across most intersections of sex and SES differed from prior studies. Findings may suggest that differences in access to resources, which vary in effects across these identity characteristics and lived experiences, influence the diagnosis of AUD in clinical care.

Offering nurse care management for opioid use disorder in primary care: Impact on emergency and hospital utilization in a cluster-randomized implementation trial.

BACKGROUND: Patients with opioid use disorder (OUD) have increased emergency and hospital utilization. The PROUD trial showed that implementation of office-based addiction treatment (OBAT) increased OUD medication treatment compared to usual care, but did not decrease acute care utilization in patients with OUD documented pre-randomization (clinicaltrials.gov/study/NCT03407638). This paper reports secondary emergency and hospital utilization outcomes in patients with documented OUD in the PROUD trial. METHODS: This cluster-randomized implementation trial was conducted in 12 clinics from 6 diverse health systems (March 2015-February 2020). Patients who visited trial clinics and had an OUD diagnosis within 3 years pre-randomization were included in primary analyses; secondary analyses added patients with OUD who were new to the clinic or with newly-documented OUD post-randomization. Outcomes included days of emergency care and hospital utilization over 2 years post-randomization. Explanatory outcomes included measures of OUD treatment. Patient-level analyses used mixed-effect regression with clinic-specific random intercepts. RESULTS: Among 1988 patients with documented OUD seen pre-randomization (mean age 49, 53 % female), days of emergency care or hospitalization did not differ between intervention and usual care; OUD treatment also did not differ. In secondary analyses among 1347 patients with OUD post-randomization, there remained no difference in emergency or hospital utilization despite intervention patients receiving 32.2 (95 % CI 4.7, 59.7) more days of OUD treatment relative to usual care. CONCLUSIONS: Implementation of OBAT did not reduce emergency or hospital utilization among patients with OUD, even in the sample with OUD first documented post-randomization in whom the intervention increased treatment.

Considering the Role of Information and Context in Promoting Health-Related Behavioral Change.

This poster considers the role that information and context may play in health management. We employ a well-known taxonomy of techniques for promoting behavioral change to consider how social media authors describe their recovery from substance use. We harvest discussion posts from subreddits, or discussion forums, about alcohol, cannabis, and opioids, and perform content analysis to identify behavioral change techniques (BCTs) described in the content. We then consider the role that the context of information use plays in these BCTs, as well as how interventions and technologies might be leveraged to better support the recovery process.

Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.

Importance: Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments. Objective: To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization. Design, Setting, and Participants: The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after. Intervention: The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine. Main Outcomes and Measures: The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up. Results: During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70). Conclusions and Relevance: The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD. Trial Registration: ClinicalTrials.gov Identifier: NCT03407638.

Impact of an intervention to implement provision of opioid use disorder medication among patients with and without co-occurring substance use disorders.

INTRODUCTION: Co-occurring substance use disorders (SUDs) are common among people with opioid use disorder (OUD) and known to hinder receipt of medications for OUD (MOUD). It is important to understand how MOUD care implemented outside of SUD specialty settings impacts access for patients with co-occurring SUDs. The Veterans Health Administration's (VA) Stepped Care for Opioid Use Disorder Train the Trainer (SCOUTT) initiative was implemented in primary care, mental health, and pain clinics in 18 VA facilities, and was found to increase MOUD receipt. This study assessed the SCOUTT initiative's impact among patients with and without co-occurring SUDs. METHODS: This study used a controlled interrupted time series design. We extracted electronic health record data for patients with OUD with visits in SCOUTT intervention or matched comparison clinics during the post-implementation year (9/1/2018-8/31/2019). We examined the monthly proportion of patients who received MOUD in SCOUTT intervention or comparison clinics (primary care, mental health, and pain clinics), or in a VA SUD specialty clinic (where patients may have been referred), during the pre- and post-implementation years. Segmented logistic regression models estimated pre-post changes in outcomes (immediate level change from the final month of the pre-implementation period to the first month of the post-implementation period, change in trend/slope) in intervention vs. comparison facilities, adjusting for patient characteristics and pre-implementation trends. We stratified analyses by the presence of co-occurring SUDs. RESULTS: Among patients without co-occurring SUDs, the pre-post trend/slope change in MOUD received in SCOUTT intervention or comparison clinics was greater in intervention vs. comparison facilities (adjusted odds ratio [aOR]: 1.06, 95% confidence interval [CI]: 1.02-1.10), and the immediate increase in MOUD received in SUD clinics was greater in intervention vs. comparison facilities (aOR: 1.12, 95% CI: 1.02-1.22). These changes did not significantly differ in intervention vs. comparison facilities among patients with co-occurring SUDs. CONCLUSIONS: The SCOUTT initiative may have increased MOUD receipt primarily among patients without co-occurring SUDs. Focusing on increasing MOUD receipt for patients with co-occurring SUDs may improve the overall effectiveness of MOUD implementation efforts.

Associations Between Distinct Co-occurring Substance Use Disorders and Receipt of Medications for Opioid Use Disorder in the Veterans Health Administration.

OBJECTIVES: Among people with opioid use disorder (OUD), having a co-occurring substance use disorder (SUD) is associated with lower likelihood of receiving OUD treatment medications (MOUD). However, it is unclear how distinct co-occurring SUDs are associated with MOUD receipt. This study examined associations of distinct co-occurring SUDs with initiation and continuation of MOUD among patients with OUD in the national Veterans Health Administration (VA). METHODS: Electronic health record data were extracted for outpatients with OUD who received care August 1, 2016, to July 31, 2017. Analyses were conducted separately among patients without and with prior-year MOUD receipt to examine initiation and continuation, respectively. SUDs were measured using diagnostic codes; MOUD receipt was measured using prescription fills/clinic visits. Adjusted regression models estimated likelihood of following-year MOUD receipt for patients with each co-occurring SUD relative to those without. RESULTS: Among 23,990 patients without prior-year MOUD receipt, 12% initiated in the following year. Alcohol use disorder (adjusted incidence rate ratio [aIRR], 0.80; 95% confidence interval [CI], 0.72-0.90) and cannabis use disorder (aIRR, 0.78; 95% CI, 0.70-0.87) were negatively associated with initiation. Among 11,854 patients with prior-year MOUD receipt, 83% continued in the following year. Alcohol use disorder (aIRR, 0.94; 95% CI, 0.91-0.97), amphetamine/other stimulant use disorder (aIRR, 0.94; 95% CI, 0.90-0.99), and cannabis use disorder (aIRR, 0.95; 95% CI, 0.93-0.98) were negatively associated with continuation. CONCLUSIONS: In this study of national VA outpatients with OUD, those with certain co-occurring SUDs were less likely to initiate or continue MOUD. Further research is needed to identify barriers related to specific co-occurring SUDs.

Treating opioid use disorder in veterans with co-occurring substance use: a qualitative study with buprenorphine providers in primary care, mental health, and pain settings.

BACKGROUND: Most people with opioid use disorder (OUD) have co-occurring substance use, which is associated with lower receipt of OUD medications (MOUD). Expanding MOUD provision and care linkage outside of substance use disorder (SUD) specialty settings is a key strategy to increase access. Therefore, it is important to understand how MOUD providers in these settings approach care for patients with co-occurring substance use. This qualitative study of Veterans Health Administration (VA) clinicians providing buprenorphine care in primary care, mental health, and pain settings aimed to understand (1) their approach to addressing OUD in patients with co-occurring substance use, (2) perspectives on barriers/facilitators to MOUD receipt for this population, and (3) support needed to increase MOUD receipt for this population. METHODS: We interviewed a purposive sample of 27 clinicians (12 primary care, 7 mental health, 4 pain, 4 pharmacists) in the VA northwest network. The interview guide assessed domains of the Tailored Implementation for Chronic Diseases Checklist. Interviews were transcribed and qualitatively analyzed using inductive content analysis. RESULTS: Participants reported varied approaches to identifying co-occurring substance use and addressing OUD in this patient population. Although they reported that this topic was not clearly addressed in clinical guidelines or training, participants generally felt that patients with co-occurring substance use should receive MOUD. Some viewed their primary role as providing this care, others as facilitating linkage to OUD care in SUD specialty settings. Participants reported multiple barriers and facilitators to providing buprenorphine care to patients with co-occurring substance use and linking them to SUD specialty care, including provider, patient, organizational, and external factors. CONCLUSIONS: Efforts are needed to support clinicians outside of SUD specialty settings in providing buprenorphine care to patients with co-occurring substance use. These could include clearer guidelines and policies, more specific training, and increased care integration or cross-disciplinary collaboration. Simultaneously, efforts are needed to improve linkage to specialty SUD care for patients who would benefit from and are willing to receive this care, which could include increased service availability and improved referral/hand-off processes. These efforts may increase MOUD receipt and improve OUD care quality for patients with co-occurring substance use.

Implementation and workflow strategies for integrating digital therapeutics for alcohol use disorders into primary care: a qualitative study.

BACKGROUND: Alcohol use disorders (AUD) are prevalent and often go untreated. Patients are commonly screened for AUD in primary care, but existing treatment programs are failing to meet demand. Digital therapeutics include novel mobile app-based treatment approaches which may be cost-effective treatment options to help fill treatment gaps. The goal of this study was to identify implementation needs and workflow design considerations for integrating digital therapeutics for AUD into primary care. METHODS: We conducted qualitative interviews with clinicians, care delivery leaders, and implementation staff (n = 16) in an integrated healthcare delivery system in the United States. All participants had experience implementing digital therapeutics for depression or substance use disorders in primary care. Interviews were designed to gain insights into adaptations needed to optimize existing clinical processes, workflows, and implementation strategies for use with alcohol-focused digital therapeutics. Interviews were recorded and transcribed and then analyzed using a rapid analysis process and affinity diagramming. RESULTS: Qualitative themes were well represented across health system staff roles. Participants were enthusiastic about digital therapeutics for AUD, anticipated high patient demand for such a resource, and made suggestions for successful implementation. Key insights regarding the implementation of digital therapeutics for AUD and unhealthy alcohol use from our data include: (1) implementation strategy selection must be driven by digital therapeutic design and target population characteristics, (2) implementation strategies should seek to minimize burden on clinicians given the large numbers of patients with AUD who are likely to be interested in and eligible for digital therapeutics, and (3) digital therapeutics should be offered alongside many other treatment options to accommodate individual patients' AUD severity and treatment goals. Participants also expressed confidence that previous implementation strategies used with other digital therapeutics such as clinician training, electronic health record supports, health coaching, and practice facilitation would be effective for the implementation of digital therapeutics for AUD. CONCLUSIONS: The implementation of digital therapeutics for AUD would benefit from careful consideration of the target population. Optimal integration requires tailoring workflows to meet anticipated patient volume and designing workflow and implementation strategies to meet the unique needs of patients with varying AUD severity.

Addictions treatment mechanisms of change science and implementation science: A critical review.

This manuscript aims to contribute to the next phase of mechanisms of behavior change (MOBC) science on alcohol or other drug use. Specifically, we encourage the transition from a basic science orientation (i.e., knowledge generation) to a translational science orientation (i.e., knowledge application or Translational MOBC Science). To inform that transition, we examine MOBC science and implementation science and consider how these two research areas can intersect to capitalize on the goals, strengths, and key methodologies of each. First, we define MOBC science and implementation science and offer a brief historical rationale for these two areas of clinical research. Second, we summarize similarities in rationale and discuss two scenarios where one draws from the other-MOBC science on implementation strategy outcomes and implementation science on MOBC. We then focus on the latter scenario, and briefly review the MOBC knowledge base to consider its readiness for knowledge translation. Finally, we provide a series of research recommendations to facilitate the translation of MOBC science. These recommendations include: (1) identifying and targeting MOBC that are well suited for implementation, (2) use of MOBC research results to inform broader health behavior change theory, and (3) triangulation of a more diverse set of research methodologies to build a translational MOBC knowledge base. Ultimately, it is important for gains borne from MOBC science to affect direct patient care, while basic MOBC research continues to be developed and refined over time. Potential implications of these developments include greater clinical significance for MOBC science, an efficient feedback loop between clinical research methodologies, a multi-level approach to understanding behavioral change, and reduced or eliminated siloes between MOBC science and implementation science.