Barriers and facilitators to collaborative care implementation within the New York State Collaborative Care Medicaid Program.

BACKGROUND: Since 2015, the New York State Office of Mental Health has provided state primary care clinics with outreach, free training and technical assistance, and the opportunity to bill Medicaid for the Collaborative Care Model (CoCM) as part of its Collaborative Care Medicaid Program. This study aims to describe the characteristics of New York State primary care clinics at each step of CoCM implementation, and the barriers and facilitators to CoCM implementation for the New York State Collaborative Care Medicaid Program. METHODS: In this mixed-methods study, clinics were categorized into RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) steps. Clinics were sent a survey, which included questions related to payer mix, funding sources, billing codes used, and patient population demographics. Qualitative interviews were conducted with clinic representatives, focusing on barriers or facilitators clinics experienced affecting their progression to the next RE-AIM step. RESULTS: One thousand ninety-nine surveys were sent to primary care clinics across New York State, with 107 (9.7%) completing a survey. Significant differences were observed among the different RE-AIM steps for multiple demographic variables including primary payer, percentage of patients with a diagnose of depression or anxiety, and percent of behavioral health services that are reimbursed, in addition to others. Three main themes regarding barriers and facilitators to implementing CoCM for New York State Medicaid billing emerged from 31 qualitative interviews: (1) Billing requirements, (2) Reimbursement rates, and (3) Buy-in to CoCM. CONCLUSIONS: Survey data align with what we would expect to see demographically in NYS primary care clinics. Qualitative data indicated that CoCM billing requirements/structure and reimbursement rates were perceived as barriers to providing CoCM, particularly with New York State Medicaid, and that buy-in, which included active involvement from organizational leaders and providers that understand the Collaborative Care model were facilitators. Having dedicated staff to manage billing and data reporting is one way clinics minimize barriers, however, there appeared to be a disconnect between what clinics can bill for and the reimbursed amount several clinics are receiving, illustrating the need for stronger billing workflows and continued refinement of billing options across different payers.

Integrating Opioid Use Disorder Treatment Into Primary Care Settings.

Importance: Medication for opioid use disorder (MOUD) (eg, buprenorphine and naltrexone) can be offered in primary care, but barriers to implementation exist. Objective: To evaluate an implementation intervention over 2 years to explore experiences and perspectives of multidisciplinary primary care (PC) teams initiating or expanding MOUD. Design, Setting, and Participants: This survey-based and ethnographic qualitative study was conducted at 12 geographically and structurally diverse primary care clinics that enrolled in a hybrid effectiveness-implementation study from July 2020 to July 2022 and included PC teams (prescribing clinicians, nonprescribing behavioral health care managers, and consulting psychiatrists). Survey data analysis was conducted from February to April 2022. Exposure: Implementation intervention (external practice facilitation) to integrate OUD treatment alongside existing collaborative care for mental health services. Measures: Data included (1) quantitative surveys of primary care teams that were analyzed descriptively and triangulated with qualitative results and (2) qualitative field notes from ethnographic observation of clinic implementation meetings analyzed using rapid assessment methods. Results: Sixty-two primary care team members completed the survey (41 female individuals [66%]; 1 [2%] American Indian or Alaskan Native, 4 [7%] Asian, 5 [8%] Black or African American, 5 [8%] Hispanic or Latino, 1 [2%] Native Hawaiian or Other Pacific Islander, and 46 [4%] White individuals), of whom 37 (60%) were between age 25 and 44 years. An analysis of implementation meetings (n = 362) and survey data identified 4 themes describing multilevel factors associated with PC team provision of MOUD during implementation, with variation in their experience across clinics. Themes characterized challenges with clinical administrative logistics that limited the capacity to provide rapid access to care and patient engagement as well as clinician confidence to discuss aspects of MOUD care with patients. These challenges were associated with conflicting attitudes among PC teams toward expanding MOUD care. Conclusions and Relevance: The results of this survey and qualitative study of PC team perspectives suggest that PC teams need flexibility in appointment scheduling and the capacity to effectively engage patients with OUD as well as ongoing training to maintain clinician confidence in the face of evolving opioid-related clinical issues. Future work should address structural challenges associated with workload burden and limited schedule flexibility that hinder MOUD expansion in PC settings.

Integrating Routine Screening for Opioid Use Disorder into Primary Care Settings: Experiences from a National Cohort of Clinics.

BACKGROUND: The U.S. Preventive Services Task Force recommends routine population-based screening for drug use, yet screening for opioid use disorder (OUD) in primary care occurs rarely, and little is known about barriers primary care teams face. OBJECTIVE: As part of a multisite randomized trial to provide OUD and behavioral health treatment using the Collaborative Care Model, we supported 10 primary care clinics in implementing routine OUD screening and conducted formative evaluation to characterize early implementation experiences. DESIGN: Qualitative formative evaluation. APPROACH: Formative evaluation included taking detailed observation notes at implementation meetings with individual clinics and debriefings with external facilitators. Observation notes were analyzed weekly using a Rapid Assessment Process guided by the Consolidated Framework for Implementation Research, with iterative feedback from the study team. After clinics launched OUD screening, we conducted structured fidelity assessments via group interviews with each site to evaluate clinic experiences with routine OUD screening. Data from observation and structured fidelity assessments were combined into a matrix to compare across clinics and identify cross-cutting barriers and promising implementation strategies. KEY RESULTS: While all clinics had the goal of implementing population-based OUD screening, barriers were experienced across intervention, individual, and clinic setting domains, with compounding effects for telehealth visits. Seven themes emerged characterizing barriers, including (1) challenges identifying who to screen, (2) complexity of the screening tool, (3) staff discomfort and/or hesitancies, (4) workflow barriers that decreased screening follow-up, (5) staffing shortages and turnover, (6) discouragement from low screening yield, and (7) stigma. Promising implementation strategies included utilizing a more universal screening approach, health information technology (HIT), audit and feedback, and repeated staff trainings. CONCLUSIONS: Integrating population-based OUD screening in primary care is challenging but may be made feasible via implementation strategies and tailored practice facilitation that standardize workflows via HIT, decrease stigma, and increase staff confidence regarding OUD.

Population-based implementation of behavioral health detection and treatment into primary care: early data from New York state.

BACKGROUND: The Collaborative Care Model is a well-established, evidence-based approach to treating depression and other common behavioral health conditions in primary care settings. Despite a robust evidence base, real world implementation of Collaborative Care has been limited and very slow. The goal of this analysis is to better describe and understand the progression of implementation in the largest state-led Collaborative Care program in the nation-the New York State Collaborative Care Medicaid Program. Data are presented using the RE-AIM model, examining the proportion of clinics in each of the model's five steps from 2014 to 2019. METHODS: We used the RE-AIM model to shape our data presentation, focusing on the proportion of clinics moving into each of the five steps of this model over the years of implementation. Data sources included: a New York State Office of Mental Health clinic tracking database, billing applications, quarterly reports, and Medicaid claims. RESULTS: A total of 84% of clinics with which OMH had an initial contact [n = 611clinics (377 FQHCs and 234 non-FQHCs)] received some form of training and technical assistance. Of those, 51% went on to complete a billing application, 41% reported quarterly data at least once, and 20% were able to successfully bill Medicaid. Of clinics that reported data prior to the first quarter of 2019, 79% (n = 130) maintained Collaborative Care for 1 year or more. The receipt of any training and technical assistance was significantly associated with our implementation indices: (completed billing application, data reporting, billing Medicaid, and maintaining Collaborative Care). The average percent of patient improvement for depression and anxiety across 155 clinics that had at least one quarter of data was 44.81%. Training and technical assistance source (Office of Mental Health, another source, or both) and intensity (high/low) were significantly related to implementation indices and were observed in FQHC versus non-FQHC samples. CONCLUSIONS: Offering Collaborative Care training and technical assistance, particularly high intensity training and technical assistance, increases the likelihood of implementation. Other state-wide organizations might consider the provision of training and technical assistance when assisting clinics to implement Collaborative Care.