Primary Care Patients With Chronic Conditions

The purpose of this study is to evaluate a self-management education program designed to help people with opioid use disorder (OUD), serious mental illness (SMI), and other chronic health conditions better manage their health. The program, called the Chronic Disease Self-Management Program (CDSMP), teaches self-management skills such as setting health goals, solving problems, managing symptoms, and working with health care providers. Participants will be randomly assigned to either participate in the CDSMP or receive usual primary care along with written information about managing their health. The study has two aims: Aim 1) The study will examine whether it is possible to deliver this program for primary care patients with OUD and SMI and whether patients find it helpful and acceptable. We will also look at early changes in participants' ability and confidence to manage their health conditions. Aim 2) The study will also examine how the program is delivered in each health care setting. Researchers will talk with patients and clinic staff and observe activities in the clinics to better understand what helps or makes it difficult to offer the program. This information will help researchers identify ways to improve the program and its delivery in primary care.

Mobile health technologies, such as smartwatches, wearable sensors, and digital applications, have the potential to support healthier lifestyles and improve the management of health conditions. These technologies can collect information on physical activity, sleep, heart rate, heart rate variability, respiratory rate, and other health indicators. However, integrating data from these devices into routine health care and electronic health records remains challenging, particularly in primary care settings and remote communities. The purpose of this study is to evaluate the implementation and feasibility of an integrated digital health ecosystem that combines wearable monitoring using smartwatches with a secure digital platform. The ecosystem is designed to collect, transmit, and display health information in a way that may support patients, health care providers, and researchers. Participants will use wearable technologies and complete questionnaires while health-related information is collected through the digital platform. The study will assess the usability, acceptability, interoperability, and feasibility of implementing this technology in real-world settings, including primary care clinics and remote communities. The study will also explore the potential use of wearable-derived physiological data to support the assessment, monitoring, and management of physiological and psychological stress. The knowledge gained from this study may help improve the integration of digital health technologies into routine care, support self-management and preventive health strategies, and facilitate access to health monitoring in underserved environments. Findings may also contribute to the development of digital health solutions for isolated and extreme environments, including future space exploration missions. This research aligns with priorities identified by the Canadian Space Agency to advance technologies that support safe, healthy, and productive human activities in remote and extreme environments on Earth and during future space missions beyond low Earth orbit.

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.