MOUD 2.0: a clinical algorithm and implementation evaluation protocol for sublingual and injectable buprenorphine...

Primary care is the initial contact point for most patients with opioid use disorder (OUD) but lacks tools for guiding treatment. Only a small fraction of patients access evidence-based care. Long-acting injectable buprenorphine has potential to improve medication adherence and program retention in low-barrier primary care treatment settings. We present the first clinical decision support algorithm incorporating long-acting buprenorphine (LAIB) in primary care. We include a protocol for a future evaluation of the algorithm's implementation process, "Medication for Opioid Use Disorder (MOUD) 2.0," at a housing and integrated care clinic at a Federally Qualified Health Center.

Literature review and expert consensus informed creation of the algorithm, which underwent iterative development with feedback from clinicians, staff, and patients. Patients are categorized by adherence to therapy and retention in the program, with recommendations for each category. Adherence is determined by urine screen supplemented by self-report. To ensure all patients in this high morbidity and mortality risk population are treated, we will treat patients as their own controls in the evaluation, with potential for multisite comparisons. We will present descriptive statistics for adherence proportion before and after MOUD 2.0 implementation, testing for differences using McNemar's test. We will then present pre- and post-implementation unadjusted six-month survival curves for retention.

LAIB is incorporated as an alternative or adjunctive treatment for patients refractory to sublingual buprenorphine and as an initial treatment for selected patients. We developed an algorithm with 4-, 8-, and 12-week decision points to guide treatment for patients with varying levels of response to sublingual buprenorphine and LAIB. This clinical decision tool incorporates LAIB among treatment options for OUD in primary care settings. The protocol will evaluate the algorithm's implementation, presenting a replicable method for assessing adherence and retention among high-risk patients in similar settings.