Characteristics and Prescribing Patterns of Clinicians Waivered to Prescribe Buprenorphine for Opioid Use Disorder Before and After Release of New Practice Guidelines.

Importance: In April 2021, the US Department of Health and Human Services (HHS) released practice guidelines exempting educational requirements to obtain a Drug Addiction Treatment Act (DATA) waiver to treat up to 30 patients with opioid use disorder with buprenorphine. Objective: To compare demographic and practice characteristics of clinicians who received traditional DATA waivers before and after release of the education-exempted HHS practice guidelines and those who were approved under the guidelines. Design, Setting, and Participants: This survey study was conducted electronically from February 1 to March 1, 2022. Eligible survey recipients were US clinicians who obtained an initial DATA waiver between April 2020 and November 2021. Exposure: DATA waiver approval pathway. Main Outcome and Measures: The outcomes were clinician demographic and practice characteristics, buprenorphine prescribing barriers, and strategies to treat patients with opioid use disorder, measured using χ2 tests and z tests to assess for differences among the waivered groups. Results: Of 23 218 eligible clinicians, 4519 (19.5%) responded to the survey. This analysis was limited to 2736 respondents with a 30-patient limit at the time of survey administration who identified their DATA waiver approval pathway. Among these respondents, 1365 (49.9%; female, 831 [61.9%]; male, 512 [38.1%]) received their DATA waiver prior to the education-exempted practice guidelines (prior DATA waiver), 550 (20.1%; female, 343 [63.4%]; male, 198 [36.6%]) received their waiver after guidelines were released but met education requirements (concurrent DATA waiver), and 821 (30.0%; female, 396 [49.2%]; male, 409 [50.8%]) received the waiver under the education-exempted guidelines (practice guidelines). Among practice guidelines clinicians, 500 (60.9%) reported that traditional DATA waiver educational requirements were a reason for not previously obtaining a waiver. Demographic and practice characteristics differed by waiver approval type. Across all groups, a large minority had not prescribed buprenorphine since obtaining a waiver (prior DATA waiver, 483 [35.7%]; concurrent DATA waiver, 226 [41.2%]; practice guidelines, 359 [44.3%]; P < .001). Clinicians who prescribed buprenorphine in the past 6 months reported treating few patients in an average month: 27 practice guidelines clinicians (6.0%) prescribed to 0 patients and 338 (75.1%) to 1 to 4 patients compared with 16 (2.2%) and 435 (59.9%) for prior and 11 (3.6%) and 166 (55.0%) for concurrent DATA waiver clinicians, respectively (P < .001). Across waiver types, clinicians reported multiple challenges to buprenorphine prescribing. Conclusions and Relevance: In this survey of DATA-waivered clinicians, clinician- and systems-level challenges that limit buprenorphine prescribing were observed, even among clinicians approved under the education-exempted guidelines pathway. The findings suggest that as implementation of legislation removing the DATA waiver begins, addressing these barriers could be essential to increasing buprenorphine access.

Polysubstance use during pregnancy: The importance of screening, patient education, and integrating a harm reduction perspective.

BACKGROUND: Substance use during pregnancy is associated with poor health outcomes. This study assessed substance use, polysubstance use, and use of select prescription medications during pregnancy. METHODS: We analyzed 2019 data from the Pregnancy Risk Assessment Monitoring System in 25 United States jurisdictions that included questions on prescription medications, tobacco, and illicit substance use during pregnancy. Alcohol and electronic cigarette use were assessed during the last three months of pregnancy, and all other substances and medications were assessed throughout pregnancy. Weighted prevalence estimates and 95% confidence intervals (CIs) were calculated. RESULTS: Nearly one-fifth of respondents who reported use of any substance reported use of at least one other substance during pregnancy. Cigarettes (8.1%; 95% CI 7.6-8.7%) and alcohol (7.4%; 95% CI 6.7-8.1%) were the most frequently reported substances, followed by cannabis (4.3%; 95% CI 3.9-4.7%). Substance use was higher among individuals who reported having depression or using antidepressants during pregnancy compared with those who did not report depression or antidepressant use. Illicit drug use prevalence was low (0.5%, 95% CI 0.4-0.7%); however, respondents reporting heroin use also frequently reported use of illicit stimulants (amphetamines: 51.7%, 95% CI 32.1-71.3% or cocaine: 26.5%, 95% CI 11.9-41.1%). Although prenatal clinician screening for alcohol and cigarette use was approximately 95%, fewer respondents (82.1%) reported being screened for cannabis or illicit substance use during pregnancy. CONCLUSIONS: One in five individuals who reported use of any substance during pregnancy engaged in polysubstance use, highlighting the importance of comprehensive screening and evidence-based interventions including harm reduction.

Impact of health information technology optimization on clinical quality performance in health centers: A national cross-sectional study.

BACKGROUND: Delivery of preventive care and chronic disease management are key components of a high functioning primary care practice. Health Centers (HCs) funded by the Health Resources and Services Administration (HRSA) have been delivering affordable and accessible primary health care to patients in underserved communities for over fifty years. This study examines the association between health center organization's health information technology (IT) optimization and clinical quality performance. METHODS AND FINDINGS: Using 2016 Uniform Data System (UDS) data, we performed bivariate and multivariate analyses to study the association of Meaningful Use (MU) attestation as a proxy for health IT optimization, patient centered medical home (PCMH) recognition status, and practice size on performance of twelve electronically specified clinical quality measures (eCQMs). Bivariate analysis demonstrated performance of eleven out of the twelve preventive and chronic care eCQMs was higher among HCs attesting to MU Stage 2 or above. Multivariate analysis demonstrated that Stage 2 MU or above, PCMH status, and larger practice size were positively associated with performance on cancer screening, smoking cessation counseling and pediatric weight assessment and counseling eCQMs. CONCLUSIONS: Organizational advancement in MU stages has led to improved quality of care that augments HCs patient care capacity for disease prevention, health promotion, and chronic care management. However, rapid technological advancement in health care acts as a potential source of disparity, as considerable resources needed to optimize the electronic health record (EHR) and to undertake PCMH transformation are found more commonly among larger HCs practices. Smaller practices may lack the financial, human and educational assets to implement and to maintain EHR technology. Accordingly, targeted approaches to support small HCs practices in leveraging economies of scale for health IT optimization, clinical decision support, and clinical workflow enhancements are critical for practices to thrive in the dynamic value-based payment environment.

The Primary Care Spend Model: a systems approach to measuring investment in primary care.

Increased investment in primary care is associated with lower healthcare costs and improved population health. The allocation of scarce resources should be driven by robust models that adequately describe primary care activities and spending within a health system, and allow comparisons within and across health systems. However, disparate definitions result in wide variations in estimates of spending on primary care. We propose a new model that allows for a dynamic assessment of primary care spending (PC Spend) within the context of a system's total healthcare budget. The model articulates varied definitions of primary care through a tiered structure which includes overall spending on primary care services, spending on services delivered by primary care professionals and spending delivered by providers that can be characterised by the '4Cs' (first contact, continuous, comprehensive and coordinated care). This unifying framework allows a more refined description of services to be included in any estimate of primary care spend and also supports measurement of primary care spending across nations of varying economic development, accommodating data limitations and international health system differences. It provides a goal for best accounting while also offering guidance, comparability and assessments of how primary care expenditures are associated with outcomes. Such a framework facilitates comparison through the creation of standard definitions and terms, and it also has the potential to foster new areas of research that facilitate robust policy analysis at the national and international levels.