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Context The American opioid epidemic has necessitated the search for safe and effective means of treatment for opioid use disorder (OUD). Medication-assisted treatment (MAT) encompasses select medications that are proven effective treatments for OUD. Understanding the mechanisms of action, indications, and implementation of MAT is paramount to increasing its availability to all individuals struggling with opioid addiction. Objectives This review is based on an educational series that aims to educate healthcare providers and ancillary healthcare members on the use of MAT for the treatment of OUD. Methods The database PubMed was utilized to retrieve articles discussing the implementation of MAT. Boolean operators and Medical Subject Headings (MeSHs) were applied including: MAT and primary care, MAT and telehealth, methadone, buprenorphine, naltrexone, MAT and osteopathic, MAT and group therapy, and MAT and COVID-19. Results Three medications have been approved for the treatment of OUD: methadone, naltrexone, and buprenorphine. Identifying ways to better treat and manage OUD and to combat stigmatization are paramount to dismantling barriers that have made treatment less accessible. Studies suggest that primary care providers are well positioned to provide MAT to their patients, particularly in rural settings. However, no study has compared outcomes of different MAT models of care, and more research is required to guide future efforts in expanding the role of MAT in primary care settings. Conclusions The coronavirus disease 2019 (COVID-19) pandemic has led to changes in the way MAT care is managed. Patients require a novel point-of-care approach to obtain care. This review will define the components of MAT, consider the impact of MAT in the primary care setting, and identify barriers to effective MAT. Increasing the availability of MAT treatment will allow for greater access to comprehensive treatment and will set the standard for accessibility of novel OUD treatment in the future.

Background The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Objective Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. Methods We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype’s features and functionalities. Results Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users’ individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. Conclusions The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.