Primary Care Patients With Chronic Conditions

The goal of this pilot clinical trial is to learn if a community informed designed program of addiction counseling with coordinated community peer navigator for people with Opioid Use Disorder (OUD) and other medical conditions can improve engagement in primary care and retention on buprenorphine. The main questions it aims to answer are: * Does the addition of a counseling and peer referral interventions in addition to usual primary care with low-threshold buprenorphine increase retention on medications for opioid use disorder? * Does the addition of counseling and peer referral intervention in addition to usual primary care with low-threshold buprenorphine increase engagement in primary care? Researchers will compare the MOUD "Plus" intervention compared to primary care treatment as usual low-threshold buprenorphine prescribing practice to see if MOUD "Plus" improves retention and engagement. Participants will upon screening and enrollment: * Meet with prescribers who will determine dose of buprenorphine and assess other medical issues as per treatment as usual with visits every 2-4 weeks * Meet with the integrated addictions counselor to develop rapport and support around clinic engagement, brief counseling intervention, and coordination of care in support of their MOUD * Be referred to a community based peer who meets with participants outside the clinic for support and advocacy for patient directed recovery goals * Meet with the research coordinator at 2, 3, and 6 months to complete follow-up surveys about their care and experiences

In Rio Grande do Sul, Brazil, the demand for specialty care referrals has increased sharply with the adoption of the electronic regulatory system, especially in rural areas. In 2023 alone, over 79,000 referrals were submitted monthly, totaling 1.7 million annual gatekeeping decisions. Due to workforce limitations, nearly 70% of referrals are authorized automatically, often without clinical validation. This leads to delays for high-risk patients, unnecessary specialist visits, and a growing backlog, currently over 172,000 pending referrals. To address this, an AI algorithm was developed to triage referrals based on urgency and appropriateness. The investigators propose a prospective controlled study with randomized implementation of the AI tool across selected specialty queues in the electronic referral system. The population will consist of referrals from specialties waitlists from municipalities in Rio Grande do Sul. Specialties to be included will be selected by the State Health Department prospectively according to gatekeeping needs. The intervention will be an AI-based triage algorithm. The control will be a standard gatekeeping process. The primary outcome is the proportion of referrals with a final decision (authorized or redirected to primary care) within six months; secondary outcomes include time to decision and appointment, system-level performance metrics. Referrals will be randomly assigned to algorithmic or human gatekeeping with a 1:1 ratio. The algorithm classifies referrals into two groups: not authorized (pending more data or teleconsultation), authorized. Authorization cases are further divided into routine and high-risk referrals to help the manage demand. Each AI prediction provides a probability from 0 to 1 of authorization (or deferring). The implementation threshold is set at 0.8; cases below this level will be classified as low confidence for decision and will not be included. According to the State Health Department's decisions, several referral lines are expected to be selected for the intervention. A sample size 934 (467 per arm) for each included specialty was calculated to detect a 1.2 relative risk for the primary outcome with 90% power and 5% significance.