The DISC Rapid DIMS (Data Infrastructure Modernization Support) provides funding for activities that address gaps in knowledge or other barriers to using data for action. The Rapid DIMS supports data modernization at the interface of chronic pain and opioid use disorder to advance research in important areas of focus: data infrastructure, data acquisition, data visualization, data collaborations, or related data activities. In 2024, one project was awarded the Rapid DIMS funding:
Using System Dynamics Modeling to Foster Real-Time Connections to Care
Principal Investigator(s): Robert Heimer, Nasim Sabounchi, Rebekah Heckmann
Our HD2A project, “Using System Dynamics Modeling to Foster Real-time Connections to Care,” has two main objectives: (1) to implement a novel, scalable, evidence-based intervention (i.e., our telehealth platform) at the time of an opioid overdose that links people who have overdosed with access to medication for opioid use disorder (MOUD), harm reduction services, and recovery supports, and (2) to collect high-quality data about the processes and outcomes associated with deployment of this platform that can be integrated with our existing SD model to determine if, where, when, and what interventions should be implemented in the future. As we begin recruitment, we have identified select stakeholders that have graciously contributed to our project, far surpassing the initial effort that we requested. In return, we would like to compensate these partners for additional work that they intend to perform directly on behalf of our R61 project. Additionally, through conversations with our partners and team, we have identified the need for geospatial tagging to identify the location of prospective participants when they access our QR enrollment code. This feature will help our telehealth platform and community partners to locate where individuals with OUD are located at the time of enrollment; and, together, we can synergistically improve our interventions, expand county-level outreach, and contribute to state-level data. Finally, the automation of all aspects of our enrollment process will reduce any errors due to time lags or manual data entry that could hinder the enrollment and/or engagement of participants. Given these needs, we propose the following specific aims: (1) support for select stakeholders, (2) development of geospatial tagging within our QR-based platform, and (3) automation of our enrollment process. Our long-term goal is to implement our novel SD model and telehealth intervention throughout Connecticut to improve access to MOUD and reduce overdose events.