Clinical Research Directory

The MOUD Plus Pilot: Counseling and Peer Support to Support Retention for Medically Complex Patients With Opioid Use Disorder Seen In Primary Care

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

Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care

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.

Developing a Learning Health System for Primary Care in Thailand

Research question: Can a Learning Health System (LHS) approach improve delivery of care and reduce inequalities in outcomes for people with hypertension and related non-communicable diseases (NCDs) compared to routine care in primary care settings in Thailand? Background: NCDs account for 74% of all deaths in Thailand. Electronic health record data is used in Thailand to monitor how well whole regions deliver care, but is not directly available to healthcare teams in an actionable format which allows them to identify individuals in need of earlier, or more active management. LHS' are an effective framework for empowering healthcare teams to drive quality improvement (QI), reduce inequalities, and translate electronic health record data into actionable clinical insight. Aims and objectives: We will conduct a stratified cluster randomized controlled trial to compare the LHS approach to routine care in two Thai provinces. We will randomize 16 primary care units to the intervention over three phases: targeting management of people with hypertension in phase 1, type 2 diabetes in phase 2 and chronic kidney disease (CKD) in phase 3. In each phase, we will: 1. Co-design a LHS with healthcare teams, policymakers, researchers and the public 2. Train healthcare and analytic teams to deliver the LHS and establish local champions to support it 3. Trial the LHS approach for 12 months 4. Compare performance between intervention and control practices and evaluate the benefits and costs of implementing the LHS 5. Identify provider and patient barriers and facilitators to inform long-term QI for NCDs Methods: We will create four strata of primary care units according to practice size and case-mix. Within each stratum, we will randomize four practices to the intervention arm. In each of the three phases of the intervention, we will hold a series of stakeholder workshops to co-design quality improvement pathways, training materials, and computerised decision support tools (Aim 1); We will train multidisciplinary healthcare, analytic and research teams to implement the LHS and establish clinical and community champions to support it (Aim 2); We will trial the LHS for 12 months. Monthly data on key metrics will be used to monitor progress and iterate the LHS based on data analytics and shared learning across healthcare teams (Aim 3). We will conduct formal statistical comparisons between intervention and control arms, undertake health economic and mixed-methods realist evaluations to understand what works in promoting change and associated costs and benefits. (Aims 4 \& 5). Timeline: Trial setup (months 0-6), Hypertension (months 3-21), Diabetes (months 15-33), CKD (months 24-45), Evaluation (months 24-48) Impact and dissemination: Results will be disseminated via publication in high-impact journals, conference presentations, stakeholder meetings, and the media. We will co-produce locally relevant educational materials and clinical guidelines. Impact will include the generation of longitudinal epidemiological data on management and outcomes of NCDs, including factors which facilitate continuous QI, and associated costs and benefits. The decision support tools, training resources, and economic evaluative frameworks will be made freely available by the Thai Ministry of Health and the regional WHO office. Capacity building will ensure the next generation of clinical, community, and research leaders promulgate this way of working across the region.