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

项目摘要 我们目前资助的HD 2A创新项目的两个目标是：（1）实现一个新颖的，可扩展的， 基于证据的干预（即，我们的远程医疗平台RecoveryPad）， 获得阿片类药物使用障碍（MOUD）药物，减少伤害服务和康复支持， 和（2）收集有关与部署此相关的过程和结果的高质量数据， 平台，可以与我们现有的系统动力学（SD）模型集成，以确定是否，在哪里，何时， 以及今后应采取哪些干预措施。通过这种方式，我们的数据（即，输入和输出 从SD模型）驱动我们的动作（即，在持续反馈中提供和改进RecoveryPad 循环.这一行政补充将允许将伦理人工智能（AI）框架整合到 完善和评价母项目的远程保健干预措施。具体来说，我们将评估 数据集、模型假设、算法输入、开发和母项目的性能， 潜在的偏见，特别是与弱势群体之间OUD相关结果的差异加剧有关 人口。通过系统地检测和减轻算法偏差，我们将提高 人工智能增强干预措施的公平性，促进不同领域的公平治疗参与 人口统计学我们获得的见解不仅将优化我们自己的RecoveryPad平台和系统 动态模型，但也将有助于更广泛的道德人工智能在医疗保健中的应用。此外，我们的工作 支持改善OUD患者的结果，并支持国家努力解决阿片类药物危机。 具体而言，我们提出了以下补充目标：1）目标1 -评估SD内的偏倚和公平性 模型：这一目标旨在将人工智能公平性评估方法转化为对 现有的系统动力学模型。通过利用我们的综合团队，包括生物伦理学家，人工智能专家，数据 科学家、临床医生和有生活经验的人，我们将研究关键的模型输入及其潜力 对敏感人口统计属性的模型输出的偏差影响。此外，我们打算确保 代表性和减轻任何算法偏见。2)目标2 -评估偏倚和总体公平性 RecoveryPad：目标2a）数据集的公平性评价和简短的协商面试（BNI）过程中 RecoveryPad开发：我们将通过分析我们的人群水平是否 机器学习算法对不同群体的MOUD参与表现出不同的预测， 历史电子健康记录数据，其中艾德患者已经从现场健康中心接收了BNI 宣传倡导者。目标2b）Recovery Pad的偏倚和公平性评估：我们将评估偏倚， 通过模拟和实时参与者对话，利用 评估对话式人工智能的偏见和毒性的现有框架。

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.