Clinical stakeholders' perceptions of patient engagement in outpatient medication treatment for opioid use disorder: A qualitative study.

INTRODUCTION: Medications for opioid use disorder (MOUD) reduce risk of opioid overdose and promote recovery from opioid use disorder, but poor retention in MOUD limits these positive effects. This study explored patient engagement in MOUD from the perspective of clinical stakeholders within an outpatient addiction medicine program to identify program factors influencing patient engagement with treatment. METHODS: We conducted a qualitative case study of a multi-clinic outpatient addiction medicine program embedded within an integrated health system that serves a geographically diverse area of Pennsylvania. Collectively, the program's clinics provide MOUD (primarily buprenorphine) to ~2000 patients annually. From January to March 2021, we conducted semi-structured telephone/video interviews with three stakeholder groups involved in delivering MOUD: administrators (n = 4), providers (n = 7), and addiction care coordinators (n = 5). Data analysis utilized the framework method. RESULTS: We identified five themes related to patient engagement. First, participants described health system integration as enhancing quality and offering opportunities for addressing patients' comprehensive health care needs. However, lack of knowledge about addiction and stigma among health system providers was felt to limit patient benefits from this integration, including access to MOUD. Second, participants viewed patient engagement as central to the program's policies, practices, and clinical environment. Adoption of a harm reduction approach and maintenance of a non-stigmatizing clinic environment were described as essential facilitators of engagement. Third, while clinics followed uniform operations, physician leads expressed differing philosophical approaches to treatment, which participants associated with variations in clinical practice and patient engagement. Fourth, participants identified key services that bolstered engagement in MOUD, including psychosocial services, psychiatric care, and telemedicine. Finally, staff well-being emerged as a key consideration for patient engagement. CONCLUSIONS: Understanding perceptions of those who administer and deliver care is critical for identifying barriers and facilitators to patient engagement in MOUD. Findings suggest potential opportunities for addiction treatment programs to improve patient engagement and ultimately MOUD retention, including integration with other healthcare services to meet comprehensive healthcare needs; adoption of a harm reduction approach; creation of non-stigmatizing clinical environments; investment in psychosocial services, psychiatric care, and telemedicine; and prioritization of staff wellness.

Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.

BACKGROUND: We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population. METHODS: Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms. RESULTS: In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis. DISCUSSION: Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.

Application of the COM-B model to patient barriers and facilitators of retention in medication treatment for opioid use disorder in rural Northeastern United States: A qualitative study.

Introduction: Medications for opioid use disorder (MOUD) reduce illicit opioid use and overdose mortality, but effectiveness remains limited by poor treatment retention. Understanding multilevel barriers and facilitators to retention from the patient perspective can guide intervention strategies to improve retention. Methods: We conducted semi-structured telephone interviews to elicit perspectives of individuals with opioid use disorder (OUD) currently (n = 19) and formerly (n = 16) receiving treatment from a multi-clinic outpatient MOUD program in Pennsylvania in July 2020 to January 2021. The Capability, Opportunity, Motivation, Behavior model provided a theoretical framework for analysis. Results: Based on interview themes, physical, rather than psychological, capability was more salient to MOUD engagement, and pertained to individual-level factors such as side effects, withdrawal, and the degree to which MOUD addressed participants' need for pain management. Co-existing mental health conditions also challenged participants' physical ability to attend appointments. The opportunity domain contained both physical and social aspects. Physical opportunity for MOUD engagement centered on community-level factors related to MOUD access (e.g., distance, transportation) and clinical-level factors including program policies. Themes related to social opportunity included interpersonal influences-such as therapeutic and social support-and stigma associated with OUD and MOUD. Motivation emerged as the dominant domain for patients. Reflective motivation factors included individual-level factors such as participants' recognition of their addiction and "readiness" to quit illicit opioid use, attitudes toward MOUD, future treatment expectations, motivation to engage in MOUD, and perceived consequences of disengagement. Automatic motivation factors included the degree to which MOUD created a sense of normalcy for participants and the use of illicit drugs to numb emotions. Conclusions: Factors at the individual, interpersonal, clinical, community, and societal levels influenced patients' capability, opportunity, and motivation to engage in MOUD. Understanding such factors can inform implementation strategies to improve retention.