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

使用系统动力学建模促进实时护理联系

基本信息

批准号：
10851137
负责人：
Rebekah Heckmann
金额：
$ 25.8万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-30 至 2024-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10851137
关键词：
Address Administrative Supplement Advocate Algorithms American Artificial Intelligence Authorization documentation Bioethics Consultants Caring Cessation of life Communities Connecticut Data Data Scientist Data Set Data Sources Decision Making Detection Development Disparity Doctor of Philosophy Drug Prescriptions Electronic Health Record Emergency Department patient Emergency Medicine Ensure Equity Ethics Evaluation Exhibits Feedback Fostering Funding Future Harm Reduction Health Promotion Healthcare Helping to End Addiction Long-term Individual Intervention Interview Link Machine Learning Mediation Methodology Modeling Opioid Outcome Output Overdose Parents Participant Performance Persons Population Process Provider Public Health Recommendation Recovery Support Resource Allocation Rest Services Techniques Time Toxic effect Translating United States National Institutes of Health Vulnerable Populations Work algorithmic bias augmented intelligence authority dashboard data ecosystem data infrastructure data-driven model demographics dynamic system emergency preparedness evidence base experience fighting first responder health application improved improved outcome innovation insight machine learning algorithm medication for opioid use disorder novel opioid epidemic opioid overdose parent project predictive modeling telehealth

Address Administrative Supplement Advocate Algorithms American Artificial Intelligence Authorization documentation Bioethics Consultants Caring Cessation of life Communities Connecticut Data Data Scientist Data Set Data Sources Decision Making Detection Development Disparity Doctor of Philosophy Drug Prescriptions Electronic Health Record Emergency Department patient Emergency Medicine Ensure Equity Ethics Evaluation Exhibits Feedback Fostering Funding Future Harm Reduction Health Promotion Healthcare Helping to End Addiction Long-term Individual Intervention Interview Link Machine Learning Mediation Methodology Modeling Opioid Outcome Output Overdose Parents Participant Performance Persons Population Process Provider Public Health Recommendation Recovery Support Resource Allocation Rest Services Techniques Time Toxic effect Translating United States National Institutes of Health Vulnerable Populations Work algorithmic bias augmented intelligence authority dashboard data ecosystem data infrastructure data-driven model demographics dynamic system emergency preparedness evidence base experience fighting first responder health application improved improved outcome innovation insight machine learning algorithm medication for opioid use disorder novel opioid epidemic opioid overdose parent project predictive modeling telehealth

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项目摘要

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.

项目摘要我们当前资助的HD2A创新项目的两个目标是：（1）实施一种新颖的，可扩展的，基于证据的，干预（即我们的远程医疗平台恢复板），将过量的人联系起来使用阿片类药物使用障碍（MOUD）的药物，减少伤害服务和恢复支持，（2）收集有关与此部署相关的过程和结果的高质量数据可以与我们现有的系统动力学（SD）模型集成的平台，以确定是否，何时，何时，以及将来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模型中）在连续反馈中推动我们的行动（即恢复板的提供和完善）环形。这种行政补充将允许将道德人工智能（AI）框架整合到对父母项目的远程医疗干预的完善和评估。具体来说，我们将评估数据集，模型假设，算法输入，父级项目的开发和性能潜在的偏见，特别是与脆弱的OUD相关结果的差异有关人群。通过系统的检测和缓解算法偏见，我们将增强 ai aigment Desutne措施的公平性，促进各种各样的公平治疗参与人口统计。我们获得的见解不仅会优化我们自己的恢复板平台和系统动态模型，但也将有助于医疗保健中更广泛的道德AI应用。而且，我们的工作一定要改善OUD个人的成果，并支持国家为解决阿片类药物危机的努力。具体而言，我们提出以下补充目的：1）目标1-评估SD内的偏见和公平性模型：此目的旨在将AI公平评估方法转化为迭代的精致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，临床医生和有经验的人，我们将研究关键模型投入及其潜力对敏感人群属性的模型输出的偏差含义。此外，我们打算确保表示并减轻任何算法偏差。 2）目标2-评估偏见和整体公平性恢复盖：目标2A）数据集的公平评估和简短的谈判访谈（BNI）过程恢复盖开发：我们将通过分析我们的人群级别来评估潜在的偏见机器学习算法在使用历史电子记录数据，ED患者从面对面的健康中获得了BNI 晋升倡导者。目标2b）恢复板的偏见和公平评估：我们将评估偏见和通过模拟和实时参与者的对话相遇，恢复板内的公平性，利用现有的框架，用于评估偏见和毒性的对话AI。