[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"primary-care-patients-with-chronic-conditions\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:primary-care-patients-with-chronic-conditions":30},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,50],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":31,"overallStatus":37,"whyStopped":4,"lastUpdateSubmitDate":38,"lastUpdatePostDateStruct":39,"startDateStruct":42,"completionDateStruct":44,"leadSponsor":46,"locationsCount":49},"100580466","the-moud-plus-pilot-counseling-and-peer-support-to-support-retention-for-medically-complex-patients-with-opioid-use-disorder-seen-in-primary-care-100580466",false,"NCT06837662","The MOUD Plus Pilot: Counseling and Peer Support to Support Retention for Medically Complex Patients With Opioid Use Disorder Seen In Primary Care","Designing Intensive Primary Care Interventions to Improve Addiction Treatment for Medically and Socially Complex Patients: The \"MOUD PLUS (MOUD+)\" Study","MOUD+","Inclusion Criteria:\n\n* Eligibility\u002FInclusion Criteria. For Aim 3, criteria we seek to recruit include:\n* Patient participants 18 years and older\n* Have an OUD-related diagnosis (e.g. Opioid Use Disorder in remission, Opioid Dependence, Opioid Abuse, Substance Use Disorder - opioids, etc.), or have used fentanyl or heroin over the past 30 days\n* Who meet at least one of the following criteria:\n\n  * Present to primary care at Central City Concern (CCC) within 8 weeks of starting or re-starting a treatment episode (defined as starting MOUD after not having received prescribed MOUD in an outpatient setting for OUD in the prior 30 days).\n  * Who present to primary care at CCC and are not seeking treatment with MOUD and have not engaged in counseling services (e.g. harm reduction counseling) in the prior 30 days\n  * Have been receiving MOUD in prior 30 days but had a return to use (used fentanyl or heroin) within the past 30 days\n* Medical complexity (e.g. self-reported or verified in patient's electronic health record)\n* Have access to phone and\u002For computer for follow-up activities\n* Desire to engage in counseling and\u002For peer services\n\nExclusion Criteria:\n\n* Patient participants who present for addiction treatment but are ineligible to receive on-going services at Central City (i.e. they have existing primary care at another location, or are currently receiving opioid use disorder treatment at another clinic, such as methadone clinic) may not participate in the study.\n* Patient participants who are not able to verbally consent may not participate in the study.\n* Patients who do not have addiction to opioids may not participate in the study.\n* Patients who participated in Aim 2 would not be eligible for Aim 3\n* Patients who lack stable phone access may not participate in the study.","ALL","18 Years",{"count":20,"type":21},70,"ESTIMATED","INTERVENTIONAL",[24],"NA","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.\n\nThe main questions it aims to answer are:\n\n* 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?\n* Does the addition of counseling and peer referral intervention in addition to usual primary care with low-threshold buprenorphine increase engagement in primary care?\n\nResearchers 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.\n\nParticipants will upon screening and enrollment:\n\n* 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\n* 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\n* Be referred to a community based peer who meets with participants outside the clinic for support and advocacy for patient directed recovery goals\n* Meet with the research coordinator at 2, 3, and 6 months to complete follow-up surveys about their care and experiences",[27,28,29,30],"Opioid Dependence","Substance Abuse Disorders","Complex Medical Patients","Primary Care Patients With Chronic Conditions",[32,33,34,35,36],"opioid use disorder","medications for opioid use disorder","recovery capital","collaborative care model","addiction treatment","RECRUITING","2026-02-23",{"date":40,"type":41},"2026-02-25","ACTUAL",{"date":43,"type":41},"2025-03-24",{"date":45,"type":21},"2026-12-01",{"name":47,"class":48},"Oregon Health and Science University","OTHER",1,{"id":51,"slug":52,"hasResults":11,"nctId":53,"briefTitle":54,"officialTitle":55,"acronym":4,"eligibilityCriteria":56,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":57,"targetDuration":4,"studyType":22,"phases":59,"briefSummary":60,"conditions":61,"keywords":4,"overallStatus":37,"whyStopped":4,"lastUpdateSubmitDate":63,"lastUpdatePostDateStruct":64,"startDateStruct":66,"completionDateStruct":68,"leadSponsor":70,"locationsCount":49},"100594413","efficacy-of-artificial-intelligence-for-gatekeeping-in-referrals-to-specialized-care-100594413","NCT07019116","Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care","Efficacy of an Artificial Intelligence Algorithm for Gatekeeping in Referrals From Primary Care to Specialized Care: a Randomized Controlled Trial","Inclusion Criteria:\n\n* All referrals from a given specialty (waitlist) will be eligible.\n* Specialties will be selected following Rio Grande do Sul Health Department priorities.\n\nExclusion Criteria:\n\n* Referrals that the AI algorithm can not evaluate. These include referrals with attachments (further information in image or PDF files) and referrals with previous rounds of discussion.\n* Referrals in which the algorithm has low confidence in the decision (i.e., informed data lead to a decision with a probability below 80%) will not be included in the study.",{"count":58,"type":21},934,[24],"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.\n\nThe 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.",[62,30],"Primary Care","2026-02-19",{"date":65,"type":41},"2026-02-20",{"date":67,"type":41},"2025-11-15",{"date":69,"type":21},"2029-12",{"name":71,"class":48},"Hospital de Clinicas de Porto Alegre"]