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DISC Rapid Data Infrastructure Modernization Support (DIMS)

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 2023, four projects were awarded the Rapid DIMS funding:

Fatal Overdose Review Teams – Research to Enhance Surveillance Systems (FORTRESS)

Principal Investigator(s): Matt Aalsma, Khairi Reda, Bradley Ray

Two HD2A project research teams, CADENCE (USF) and FORTRESS (IU), have hypothesized a common mechanism of policy and process change: acceptance of harm reduction principles and practices among decision-makers. Using these funds, our teams apply social network modeling within each project to learn more about how key system players share and encourage the uptake of harm reduction principles among other stakeholders across their networks. We anticipate that identifying these important individuals, "champions," will 1) guide how our research teams tailor the planned CADENCE and FORTRESS interventions and 2) help communities affect policy and practice change. Through the current proposal, our research teams will share resources to consult with an expert in social network modeling (Vermeer) to develop a social network survey. Vermeer's subsequent analyses and visualizations will help the teams identify connections among study participants and assess study participant attitudes toward harm reduction principles. Research teams will tailor their planned R33 phase interventions based on survey results. The social network survey items will then be incorporated into planned study surveys administered at regular intervals to assess changes in both champions and diffusion of harm reduction principles over time.

CADENCE: Continuous and Data-Driven Care

Principal Investigator(s): Jennifer Marshall, Tanner Wright

Our long-term goal is to leverage high-quality and timely local data to improve opioid use disorder (OUD) outcomes before, during, and after pregnancy. This funding expands testing and integration of our new algorithm into electronic medical records (EMR) making it accessible to clinicians in real-time. Patients identified are offered entrance into our integrated care pathway, CADENCE. First, we will determine the reliability of the developed algorithm to detect maternal opioid use disorder in a state-wide EMR dataset. We hypothesize that our algorithm will be able to be used in multiple different EMRs to identify pregnant patients with high positive predictive value. Testing our algorithm in multiple systems will determine if it can be used statewide or would need to be customized to every healthcare system for a high positive predictive value. Second, we will integrate the developed algorithm into the TGH/USF Epic EMR to identify patients with maternal OUD in real-time and track patient outcome measures. We aim to leverage this technology to develop real-time integration of our algorithm into the medical record. This dashboard and report system will allow clinicians to detect pregnant patients with OUD and reach out to those patients to provide needed services.

Using System Dynamics Modeling to Foster Real-Time Connections to Care

Principal Investigator(s): Rebekah Heckmann, Nasim Sabounchi

Our two main objectives are: (1) implement our telehealth platform RecoveryPad which connects people who have overdosed with access to medication for opioid use disorder (MOUD) and harm reduction services, and (2) gather high-quality data about the processes and outcomes of this platform to integrate with our existing system dynamics (SD) model to improve intervention implementation. Our SD model input and output data drives our action to provide and refine RecoveryPad in a continuous feedback loop. We identified specific needs to access data related to pharmacy-based naloxone and MOUD dispensing and prescribing, support ongoing key data streams for the SD model, and revise our recruitment protocol to recruit a more diverse and representative cohort of participants. Given these needs, this funding supports three specific aims: (1) purchase data from the IQVIA data set; (2) support Dr.Grau's efforts to curate a state-wide report of opioid-involved accidental or undetermined fatal drug overdoses; and (3) develop and maintain a QR code-based enrollment platform. Our long-term goal is to implement our novel SD modeling and telehealth strategies throughout Connecticut and disseminate them nationally if effectiveness is demonstrated, with the ultimate aim of improving access to MOUD and reducing overdose events and fatalities.

Improving Pain Management and Opioid Safety Through a Systemwide Data-Driven Evaluation of the CDC Opioid Prescribing Guideline Best Practices and the Use of Clinical Decision Support

Principal Investigator(s): Jason Hoppe

Effectively treating pain while safely prescribing opioid analgesics is a public health priority. A patient-centered approach to understanding the impact of opioid prescribing practices is needed to fully understand the consequences, intended and unintended, of prescribing decisions. This funding supports the integration of patient-reported outcomes (PROs) into the EHR to complement our clinical decision support (CDS) to promote 2022 CDC guideline recommended practices. The objective of this data infrastructure modernization proposal is to enhance pain management, opioid safety, and successful treatment of opioid use disorder (OUD) by systematically adding data collection and integration of PROs. This is an important step to eventually using PROs in CDS to improve delivery and evaluation evidence-based practices like measurement-based care. These funds support collaboration with our system informatics core to facilitate the systematic development of PRO data collection and build capacity for new data linkage and utilization for CDS to include PROs in our HEAL funded study. This aligns with NIDA's strategic plan to implement evidence-based interventions in real -world settings by bringing measurement-based care using PROs. We will generate initial feasibility data from primary care settings to inform future CDS development and patient outcome measures in other settings.