Health Professionals’ Attitudes toward Medications for Opioid use Disorder

Abstract

Background: Research has shown that medications, especially opioid agonist treatments, are an effective way to treat opioid use disorder (OUD); however, negative attitudes held by health professionals contribute to their underutilization. Methods: A 23-year review of studies that examined health professionals’ attitudes toward medications for OUD (MOUD) was conducted to describe the current state of knowledge and to inform future research and interventions. Results: Studies examined attitudes toward the use of methadone, buprenorphine, and naltrexone among various types of health professionals: prescribers, non-prescribing clinicians, pharmacists, and administrators. The characteristics and findings of the included studies were reviewed and synthesized. Findings indicate that attitudes toward MOUD affect access and utilization by influencing prescribing practices, referrals, and adoption within programs. Exposure, knowledge, and treatment orientation were found to be important factors related to attitudes toward MOUD across multiple studies of various types of health professionals. Conclusions: To increase access and utilization, continued efforts are needed to increase positive attitudes toward MOUD among various types of health professionals. Findings indicate that interventions should seek to increase knowledge about MOUD and foster interprofessional communication related to MOUD, especially between prescribers and behavioral health providers.

The Impact of Stigma on People with Opioid Use Disorder, Opioid Treatment, and Policy

“… 88 In turn, this may have beneficial effects on stigma enacted at the meso level, as greater exposure to OAT among prescribers is a consistent predictor of more positive attitudes towards treatment. 89 Similar findings have been reported among pharmacists, and may reflect improvements in knowledge and confidence in addition to a shift in attitudes as they begin witnessing patients’ improvements first-hand. 89 , 90 …”

“… 89 Similar findings have been reported among pharmacists, and may reflect improvements in knowledge and confidence in addition to a shift in attitudes as they begin witnessing patients’ improvements first-hand. 89 , 90 …”

“…88 In turn, this may have beneficial effects on stigma enacted at the meso level, as greater exposure to OAT among prescribers is a consistent predictor of more positive attitudes towards treatment. 89 Similar findings have been reported among pharmacists, and may reflect improvements in knowledge and confidence in addition to a shift in attitudes as they begin witnessing patients' improvements first-hand. 89,90 Expanding hospital-based models of delivery, such as low-threshold buprenorphine treatment initiated in hospital emergency departments, 91 may also help normalise OAT and reduce stigma experienced by people with opioid use disorder within these settings.…”

“…89 Similar findings have been reported among pharmacists, and may reflect improvements in knowledge and confidence in addition to a shift in attitudes as they begin witnessing patients' improvements first-hand. 89,90 Expanding hospital-based models of delivery, such as low-threshold buprenorphine treatment initiated in hospital emergency departments, 91 may also help normalise OAT and reduce stigma experienced by people with opioid use disorder within these settings. Embedding addiction medicine specialist services in hospitals has been identified as a key strategy to address inadequate or inappropriate treatment during hospitalisation and improve patient outcomes.…”

A scoping review of methodological approaches to detect bias in the electronic health record.

“…10 medications for opioid use disorder (Mackey et al, 2020). The methods reviewed in this study could help to identify how these biases manifest to close the gap between patients for whom medications for opioid use disorder may be beneficial yet are not prescribed because of a myriad of barriers, including provider bias (Brown, 2022). Manual content analysis, LIWC, and exploratory vocabulary analysis are valuable methods for identifying biased language in EHRs, and ongoing research should refine these methods for widespread implementation in stigma research.…”