Increasing Access to Medications for Opioid Use Disorder and Complementary and Integrative Health Services in Primary Care.

OBJECTIVES: Evidence-based therapies for opioid use disorder (OUD) and chronic pain, such as medications for OUD (MOUD) and complementary and integrative health (CIH; e.g., acupuncture and meditation) therapies, exist. However, their adoption has been slow, particularly in primary care, due to numerous implementation challenges. We sought to expand the use of MOUD and CIH within primary care by using an evidence-based quality improvement (EBQI) implementation strategy. METHODS: We used EBQI to engage two facilities in the Veterans Health Administration (VHA) from June 2018 to September 2019. EBQI included multilevel stakeholder engagement, with external facilitators providing technical support, practice facilitation, and routine data feedback. We established a quality improvement (QI) team at each facility with diverse stakeholders (e.g., primary care, addiction, pain, nursing, pharmacy). We met monthly with regional stakeholders to address implementation barriers. We also convened an advisory board to ensure alignment with national priorities. RESULTS: Pre-implementation interviews indicated facility-level and provider-level barriers to prescribing buprenorphine, including strong primary care provider resistance. Both facilities developed action plans. They both conducted educational meetings (e.g., Grand Rounds, MOUD waiver trainings). Facility A also offered clinical preceptorships for newly trained primary care prescribers. Facility B used mass media and mailings to educate patients about MOUD and CIH options and dashboards to identify potential candidates for MOUD. After 15 months, both facilities increased their OUD treatment rates to the ≥ 90th percentile of VHA medical centers nationally. Exit interviews indicated an attitudinal shift in MOUD delivery in primary care. Stakeholders valued the EBQI process, particularly cross-site collaboration. IMPLICATIONS: Despite initial implementation barriers, we effectively engaged stakeholders using EBQI strategies. Local QI teams used an assortment of QI interventions and developed tools to catapult their facilities to among the highest performers in VHA OUD treatment. IMPACTS: EBQI is an effective strategy to partner with stakeholders to implement MOUD and CIH therapies.

Use of General Primary Care, Specialized Primary Care, and Other Veterans Affairs Services Among High-Risk Veterans.

Importance: Integrated health care systems increasingly focus on improving outcomes among patients at high risk for hospitalization. Examining patterns of where patients obtain care could give health care systems insight into how to develop approaches for high-risk patient care; however, such information is rarely described. Objective: To assess use of general and specialized primary care, medical specialty, and mental health services among patients at high risk of hospitalization in the Veterans Health Administration (VHA). Design, Setting, and Participants: This national, population-based, retrospective cross-sectional study included all veterans enrolled in any type of VHA primary care service as of September 30, 2015. Data analysis was performed from April 1, 2016, to January 1, 2019. Exposures: Risk of hospitalization and assignment to general vs specialized primary care. Main Outcome and Measures: High-risk veterans were defined as those who had the 5% highest risk of near-term hospitalization based on a validated risk prediction model; all others were considered low risk. Health care service use was measured by the number of encounters in general primary care, specialized primary care, medical specialty, mental health, emergency department, and add-on intensive management services (eg, telehealth and palliative care). Results: The study assessed 4 309 192 veterans (mean [SD] age, 62.6 [16.0] years; 93% male). Male veterans (93%; odds ratio [OR], 1.11; 95% CI, 1.10-1.13), unmarried veterans (63%; OR, 2.30; 95% CI, 2.32-2.35), those older than 45 years (94%; 45-65 years of age: OR, 3.49 [95% CI, 3.44-3.54]; 66-75 years of age: OR, 3.04 [95% CI, 3.00-3.09]; and >75 years of age: OR, 2.42 [95% CI, 2.38-2.46]), black veterans (23%; OR, 1.63; 95% CI, 1.61-1.64), and those with medical comorbidities (asthma or chronic obstructive pulmonary disease: 33%; OR, 4.03 [95% CI, 4.00-4.06]; schizophrenia: 4%; OR, 5.14 [95% CI, 5.05-5.22]; depression: 42%; OR, 3.10 [95% CI, 3.08-3.13]; and alcohol abuse: 20%; OR, 4.54 [95% CI, 4.50-4.59]) were more likely to be high risk (n = 351 012). Most (308 433 [88%]) high-risk veterans were assigned to general primary care; the remaining 12% (42 579 of 363 561) were assigned to specialized primary care (eg, women's health and homelessness). High-risk patients assigned to general primary care had more frequent primary care visits (mean [SD], 6.9 [6.5] per year) than those assigned to specialized primary care (mean [SD], 6.3 [7.3] per year; P < .001). They also had more medical specialty care visits (mean [SD], 4.4 [5.9] vs 3.7 [5.4] per year; P < .001) and fewer mental health visits (mean [SD], 9.0 [21.6] vs 11.3 [23.9] per year; P < .001). Use of intensive supplementary outpatient services was low overall. Conclusions and Relevance: The findings suggest that, in integrated health care systems, approaches to support high-risk patient care should be embedded within general primary care and mental health care if they are to improve outcomes for high-risk patient populations.

Development of a web-based toolkit to support improvement of care coordination in primary care.

Promising practices for the coordination of chronic care exist, but how to select and share these practices to support quality improvement within a healthcare system is uncertain. This study describes an approach for selecting high-quality tools for an online care coordination toolkit to be used in Veterans Health Administration (VA) primary care practices. We evaluated tools in three steps: (1) an initial screening to identify tools relevant to care coordination in VA primary care, (2) a two-clinician expert review process assessing tool characteristics (e.g. frequency of problem addressed, linkage to patients' experience of care, effect on practice workflow, and sustainability with existing resources) and assigning each tool a summary rating, and (3) semi-structured interviews with VA patients and frontline clinicians and staff. Of 300 potentially relevant tools identified by searching online resources, 65, 38, and 18 remained after steps one, two and three, respectively. The 18 tools cover five topics: managing referrals to specialty care, medication management, patient after-visit summary, patient activation materials, agenda setting, patient pre-visit packet, and provider contact information for patients. The final toolkit provides access to the 18 tools, as well as detailed information about tools' expected benefits, and resources required for tool implementation. Future care coordination efforts can benefit from systematically reviewing available tools to identify those that are high quality and relevant.

What are the key elements for implementing intensive primary care? A multisite Veterans Health Administration case study.

Many integrated health systems and accountable care organizations have turned to intensive primary care programs to improve quality of care and reduce costs for high-need high-cost patients. How best to implement such programs remains an active area of discussion. In 2014, the Veterans Health Administration (VHA) implemented five distinct intensive primary care programs as part of a demonstration project that targeted Veterans at the highest risk for hospitalization. We found that programs evolved over time, eventually converging on the implementation of the following elements: 1) an interdisciplinary care team, 2) chronic disease management, 3) comprehensive patient assessment and evaluation, 4) care and case management, 5) transitional care support, 6) preventive home visits, 7) pharmaceutical services, 8) chronic disease self-management, 9) caregiver support services, 10) health coaching, and 11) advanced care planning. The teams also found that including social workers and mental health providers on the interdisciplinary teams was critical to effectively address psychosocial needs of these complex patients. Having a central implementation coordinator facilitated the convergence of these program features across diverse demonstration sites. In future iterations of these programs, VHA intends to standardize staffing and key features to develop a scalable program that can be disseminated throughout the system.

Determinants of readiness for primary care-mental health integration (PC-MHI) in the VA Health Care System.

BACKGROUND: Depression management can be challenging for primary care (PC) settings. While several evidence-based models exist for depression care, little is known about the relationships between PC practice characteristics, model characteristics, and the practice's choices regarding model adoption. OBJECTIVE: We examined three Veterans Affairs (VA)-endorsed depression care models and tested the relationships between theoretically-anchored measures of organizational readiness and implementation of the models in VA PC clinics. DESIGN: 1) Qualitative assessment of the three VA-endorsed depression care models, 2) Cross-sectional survey of leaders from 225 VA medium-to-large PC practices, both in 2007. MAIN MEASURES: We assessed PC readiness factors related to resource adequacy, motivation for change, staff attributes, and organizational climate. As outcomes, we measured implementation of one of the VA-endorsed models: collocation, Translating Initiatives in Depression into Effective Solutions (TIDES), and Behavioral Health Lab (BHL). We performed bivariate and, when possible, multivariate analyses of readiness factors for each model. KEY RESULTS: Collocation is a relatively simple arrangement with a mental health specialist physically located in PC. TIDES and BHL are more complex; they use standardized assessments and care management based on evidence-based collaborative care principles, but with different organizational requirements. By 2007, 107 (47.5 %) clinics had implemented collocation, 39 (17.3 %) TIDES, and 17 (7.6 %) BHL. Having established quality improvement processes (OR 2.30, [1.36, 3.87], p = 0.002) or a depression clinician champion (OR 2.36, [1.14, 4.88], p = 0.02) was associated with collocation. Being located in a VA regional network that endorsed TIDES (OR 8.42, [3.69, 19.26], p < 0.001) was associated with TIDES implementation. The presence of psychologists or psychiatrists on PC staff, greater financial sufficiency, or greater spatial sufficiency was associated with BHL implementation. CONCLUSIONS: Both readiness factors and characteristics of depression care models influence model adoption. Greater model simplicity may make collocation attractive within local quality improvement efforts. Dissemination through regional networks may be effective for more complex models such as TIDES.