Introduction: Opioids prescribed in hospital are a key risk factor for harm in the community. This study aimed to gain an in-depth understanding of factors affecting post-operative opioid prescribing amongst clinicians using the capability, opportunity, motivation generate behaviour framework, more commonly known as COM-B.
Methods: Focus groups and semi-structured interviews were used to gain an in-depth understanding of factors affecting optimal practice when prescribing opioids for post-operative patients at discharge. A topic guide was written using the COM-B behaviour change model to ensure the full range of possible factors influencing prescribing behaviours were explored.
Results: We found barriers and facilitators of optimal opioid prescribing practice across all three domains of capability, opportunity and motivation. Capability among junior doctors could be increased in the areas of risk assessment and prescribing appropriate discharge analgesia, though education and training were not key barriers to improving practice. Findings indicated that opportunity to practice optimal prescribing was hindered by a lack of time at discharge and technology. Beliefs about one's own and others' responsibilities also impacted motivation to practice optimal prescribing behaviours. Pharmacists were identified as key supports for patient education and appropriate prescribing.
Conclusions: Educating prescribers about opioid risks and clinical practice guidelines are necessary interventions, however, our findings indicate that if implemented in isolation, they may not have the desired impact. Interventions also need to address discharge time pressures and presumptions that GPs are aware of whether opioids should be ceased or continued after surgical discharge.
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http://dx.doi.org/10.1093/ijpp/riad024 | DOI Listing |
J Neural Transm (Vienna)
January 2025
Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
The majority of patients with cannabis use disorder (CUD) regularly take medication. Cannabinoids influence metabolism of some commonly prescribed drugs. However, little is known about the characteristics and frequency of potential cannabis-drug (CDIs) and drug-drug interactions (DDIs) in patients with CUD.
View Article and Find Full Text PDFInt J Drug Policy
January 2025
Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA. Electronic address:
Background: Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical.
Methods: Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020).
Pain
January 2025
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
Rapid declines in opioid analgesics dispensed in American communities since 2011 raise concerns about inadequate access to effective pain management among patients for whom opioid therapies are appropriate, especially for those living in racial/ethnic minority and socioeconomically deprived communities. Using 2011 to 2021 national data from the Automated Reports and Consolidated Ordering System and generalized linear models, this study examined quarterly per capita distribution of oxycodone, hydrocodone, and morphine (in oral morphine milligram equivalents [MMEs]) by communities' racial/ethnic and socioeconomic profiles. Communities (defined by 3-digit-zip codes areas) were classified as "majority White" (≥50% self-reported non-Hispanic White population) vs "majority non-White.
View Article and Find Full Text PDFCurr Pain Headache Rep
January 2025
Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
Recent Findings: Machine learning-based predictive models for identifying risk for persistent postoperative opioid use have been reported for spine surgery, knee arthroplasty, hip arthroplasty, arthroscopic joint surgery, outpatient surgery, and mixed surgical populations.
J Addict Med
January 2025
From the Division of General Internal Medicine, San Francisco General Hospital, Department of Medicine, University of California San Francisco, San Francisco, CA (LWS); San Francisco Department of Public Health, San Francisco, CA (POC); Vital Strategies, New York, NY (KB, DC); Network for Public Health Law, Edina, MN (CSD); and New York University Grossman School of Medicine, New York, NY (CSD).
Stimulant use disorder (StUD) is a rapidly growing concern in the United States, with escalating rates of death attributed to amphetamines and cocaine. No medications are currently approved for StUD treatment, leaving clinicians to navigate off-label medication options. Recent studies suggest that controlled prescription psychostimulants such as dextroamphetamine, methylphenidate, and modafinil are associated with reductions in self-reported stimulant use, craving, and depressive symptoms.
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