Literature Collection

BACKGROUND: Opioid use disorder (OUD) in pregnancy disproportionately impacts rural and American Indian (AI) communities. With limited data available about access to care for these populations, this study's objective was to assess clinic knowledge and new patient access for OUD treatment in three rural U.S. counties. MATERIAL AND METHODS: The research team used unannounced standardized patients (USPs) to request new patient appointments by phone for white and AI pregnant individuals with OUD at primary care and OB/GYN clinics that provide prenatal care in three rural Utah counties. We assessed a) clinic familiarity with buprenorphine for OUD; b) appointment availability for buprenorphine treatment; c) appointment wait times; d) referral provision when care was unavailable; and e) availability of OUD care at referral locations. We compared outcomes for AI and white USP profiles using descriptive statistics. RESULTS: The USPs made 34 calls to 17 clinics, including 4 with publicly listed buprenorphine prescribers on the Substance Abuse and Mental Health Services Administration website. Among clinical staff answering calls, 16 (47%) were unfamiliar with buprenorphine. OUD treatment was offered when requested in 6 calls (17.6%), with a median appointment wait time of 2.5 days (IQR 1-5). Among clinics with a listed buprenorphine prescriber, 2 of 4 (50%) offered OUD treatment. Most clinics (n = 24/28, 85.7%) not offering OUD treatment provided a referral; however, a buprenorphine provider was unavailable/unreachable 67% of the time. The study observed no differences in appointment availability between AI and white individuals. CONCLUSIONS: Rural-dwelling AI and white pregnant individuals with OUD experience significant barriers to accessing care. Improving OUD knowledge and referral practices among rural clinics may increase access to care for this high-risk population.

IMPORTANCE: Telemedicine visits can offer patients convenient access to a clinician, but it is unclear whether treatment differs from that with in-person visits or how often patients require in-person follow-up. OBJECTIVE: To examine whether physician prescribing and orders differ between telemedicine and office visits, whether physicians conducting telemedicine visits are more likely to require in-person follow-up, and whether telemedicine visits are associated with more health events. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included all patients who scheduled primary care appointments through the patient portal of a large integrated health care delivery system newly implementing patient-scheduled video telemedicine visits from January 2016 to May 2018. MAIN OUTCOMES AND MEASURES: Adjusted rates of any medication prescribed or laboratory tests or imaging ordered and rates of follow-up health care utilization (in-person visits, emergency department visits, and hospitalizations) within 7 days after the index visit, stratified by index primary care visit type, were generated using multivariable adjustment for patient, access, and clinical characteristics. RESULTS: This study included 1 131 722 patients (611 821 [54%] female; mean [SD] age, 43 [22] years) with 2 178 440 total appointments (307 888 [14%] telemedicine), of which 13.5% were for patients younger than 18 years, 22.2% were for patients 65 years or older, and 54.9% were for female patients. After adjustment, 38.6% (95% CI, 38.0%-39.3%) of video visits, 34.7% (95% CI, 34.5%-34.9%) of telephone visits, and 51.9% (95% CI, 51.8%-52.0%) of office visits had any medication prescribed; laboratory tests or imaging were ordered for 29.2% (95% CI, 28.5%-29.8%) of video visits, 27.3% (95% CI, 27.1%-27.5%) of telephone visits, and 59.3% (95% CI, 59.3%-59.4%) of clinic visits. After adjustment, follow-up visits within 7 days occurred after 25.4% (95% CI, 24.7%-26.0%) of video visits, 26.0% (95% CI, 25.9%-26.2%) of telephone visits, and 24.5% (95% CI, 24.5%-24.6%) of office visits. Adjusted emergency department visits and rates of hospitalizations were not statistically significantly different by primary care index visit type. CONCLUSIONS AND RELEVANCE: In this cohort study of patient self-scheduled primary care telemedicine visits within ongoing patient-physician relationships, prescribing and orders were significantly lower for telemedicine visits than for clinic visits, with slightly higher follow-up office visits for telemedicine but no difference in health events (emergency department visits or hospitalizations). Video or telephone visits may be a convenient and efficient way to access primary care and address patient needs.

Background: A cascade of care (CoC) model may improve understanding of gaps in addiction treatment availability and quality over current single measure methods. Despite increased funding, opioid overdose rates remain high. Therefore, it is critical to understand where the health-care system is failing to provide appropriate care for people with opioid use disorder (OUD) diagnoses, and to assess disparities in receipt of medication for OUD (MOUD).Objective: Using a CoC framework, assess treatment quality and outcomes for OUD in the Florida Medicaid population in 2017/2018 by demographics and primary vs. secondary diagnosis.Methods: Data from Florida Medicaid claims for 2017 and 2018 were used to calculate the number of enrollees who were diagnosed, began MOUD, were retained on medication for a minimum of 180 days, and who died.Results: Only 28% of those diagnosed with OUD began treatment with an FDA approved MOUD (buprenorphine, methadone, or injectable naltrexone). Once on medication, 38% of newly diagnosed enrollees were retained in treatment for180 days. Those who remained on MOUD for 180 days had a hazard ratio of death of 0.226 (95% CI = 0.174 to 0.294) compared to those that did not initiate MOUD, a reduction in mortality from 10% without MOUD to 2% with MOUD.Conclusions: Initiating medication after OUD diagnosis offers the greatest opportunity for intervention to reduce overdose deaths, though efforts to increase retention are also warranted. Analyzing claims data with CoC identifies system functioning for specific populations, and suggests policies and clinical pathways to target for improvement.