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

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Project Summary The two objectives of our currently funded HD2A Innovation Project are: (1) to implement a novel, scalable, evidence-based, intervention (i.e., our telehealth platform RecoveryPad) 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 system dynamics (SD) model to determine if, where, when, and what interventions should be implemented in the future. In this manner, our data (i.e., input and output from the SD model) drives our action (i.e., provision and refinement of RecoveryPad) in a continuous feedback loop. This administrative supplement will allow integration of an ethical artificial intelligence (AI) framework into the refinement and evaluation of the telehealth intervention of the parent project. Specifically, we will evaluate datasets, model assumptions, algorithmic inputs, development, and performance of the parent project for potential biases, particularly in relation to exacerbating disparity of OUD-related outcomes among vulnerable populations. Through the systematic detection and mitigation of algorithmic biases, we will enhance the fairness of AI-augmented interventions, promoting equitable treatment engagement across diverse demographics. The insights we gain will not only optimize our own RecoveryPad platform and system dynamics model but will also contribute to wider ethical AI applications in healthcare. Moreover, our work stands to improve outcomes for individuals with OUD and support national efforts to address the opioid crisis. Specifically, we propose the following supplemental aims: 1) Aim 1 - to assess bias and fairness within the SD model: This aim seeks to translate AI fairness assessment methodologies into iterative refinement of the existing system dynamics model. By leveraging our integrated team that includes a bioethicist, AI experts, data scientists, clinicians, and people with lived experience, we will examine key model inputs and their potential bias implications on model outputs for sensitive demographic attributes. Furthermore, we intend to ensure representation and mitigate any algorithmic bias. 2) Aim 2 - to assess bias and overall fairness of RecoveryPad: Aim 2a) Fairness evaluation of datasets and the brief negotiated interview (BNI) process during RecoveryPad Development: We will assess potential biases by analyzing whether our population-level machine learning algorithms exhibit differential predictions for MOUD engagement across diverse groups using historical electronic health record data, where ED patients have received a BNI from an in-person health promotion advocate. Aim 2b) Bias and Fairness Assessment of RecoveryPad: We will evaluate bias and fairness within RecoveryPad through simulated and real-time participant conversational encounters, leveraging existing frameworks for assessing conversational AI for bias and toxicity.