Literature Collection

IMPORTANCE: With increasing rates of opioid use disorder (OUD) and overdose deaths in the US, increased access to medications for OUD (MOUD) is paramount. Rigorous effectiveness evaluations of large-scale implementation initiatives using quasi-experimental designs are needed to inform expansion efforts. OBJECTIVE: To evaluate a US Department of Veterans Affairs (VA) initiative to increase MOUD use in nonaddiction clinics. DESIGN, SETTING, AND PARTICIPANTS: This quality improvement initiative used interrupted time series design to compare trends in MOUD receipt. Primary care, pain, and mental health clinics in the VA health care system (n = 35) located at 18 intervention facilities and nonintervention comparison clinics (n = 35) were matched on preimplementation MOUD prescribing trends, clinic size, and facility complexity. The cohort of patients with OUD who received care in intervention or comparison clinics in the year after September 1, 2018, were evaluated. The preimplementation period extended from September 1, 2017, through August 31, 2018, and the postimplementation period from September 1, 2018, through August 31, 2019. EXPOSURES: The multifaceted implementation intervention included education, external facilitation, and quarterly reports. MAIN OUTCOMES AND MEASURES: The main outcomes were the proportion of patients receiving MOUD and the number of patients per clinician prescribing MOUD. Segmented logistic regression evaluated monthly proportions of MOUD receipt 1 year before and after initiative launch, adjusting for demographic and clinical covariates. Poisson regression models examined yearly changes in clinician prescribing over the same time frame. RESULTS: Overall, 7488 patients were seen in intervention clinics (mean [SD] age, 53.3 [14.2] years; 6858 [91.6%] male; 1476 [19.7%] Black, 417 [5.6%] Hispanic; 5162 [68.9%] White; 239 [3.2%] other race [including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and multiple races]; and 194 [2.6%] unknown) and 7558 in comparison clinics (mean [SD] age, 53.4 [14.0] years; 6943 [91.9%] male; 1463 [19.4%] Black; 405 [5.4%] Hispanic; 5196 [68.9%] White; 244 [3.2%] other race; 250 [3.3%] unknown). During the preimplementation year, the proportion of patients receiving MOUD in intervention clinics increased monthly by 5.0% (adjusted odds ratio [AOR], 1.05; 95% CI, 1.03-1.07). Accounting for this preimplementation trend, the proportion of patients receiving MOUD increased monthly by an additional 2.3% (AOR, 1.02; 95% CI, 1.00-1.04) during the implementation year. Comparison clinics increased by 2.6% monthly before implementation (AOR, 1.03; 95% CI, 1.01-1.04), with no changes detected after implementation. Although preimplementation-year trends in monthly MOUD receipt were similar in intervention and comparison clinics, greater increases were seen in intervention clinics after implementation (AOR, 1.04; 95% CI, 1.01-1.08). Patients treated with MOUD per clinician in intervention clinics saw greater increases from before to after implementation compared with comparison clinics (incidence rate ratio, 1.50; 95% CI, 1.28-1.77). CONCLUSIONS AND RELEVANCE: A multifaceted implementation initiative in nonaddiction clinics was associated with increased MOUD prescribing. Findings suggest that engagement of clinicians in general clinical settings may increase MOUD access.

To assess whether primary care medical homes (PCMHs) are accurately identified for patients receiving care in a specialty mental health clinic within an integrated public delivery system. This study reviewed the electronic records of patients in a large urban mental health clinic. The study defined 'matching PCMH' if the same primary care clinic was listed in both the mental health and medical electronic records. This study designated all others as 'PCMH unknown.' This study assessed whether demographic factors predicted PCMH status using chi-square tests. Among 229 patients (66% male; mean age 49; 36% White, 30% Black, and 17% Asian), 72% had a matching PCMH. Sex, age, race, psychiatric diagnosis, and psychotropic medication use were not associated with matching PCMH. To improve care coordination and health outcomes for people with severe mental illness, greater efforts are needed to ensure the accurate designation of PCMHs in all mental health patient electronic records.

To assess whether primary care medical homes (PCMHs) are accurately identified for patients receiving care in a specialty mental health clinic within an integrated public delivery system. This study reviewed the electronic records of patients in a large urban mental health clinic. The study defined 'matching PCMH' if the same primary care clinic was listed in both the mental health and medical electronic records. This study designated all others as 'PCMH unknown.' This study assessed whether demographic factors predicted PCMH status using chi-square tests. Among 229 patients (66% male; mean age 49; 36% White, 30% Black, and 17% Asian), 72% had a matching PCMH. Sex, age, race, psychiatric diagnosis, and psychotropic medication use were not associated with matching PCMH. To improve care coordination and health outcomes for people with severe mental illness, greater efforts are needed to ensure the accurate designation of PCMHs in all mental health patient electronic records.