SBIR Phase I: Thrivee vMAT-AI: AI Driven Platform to Drive Success in Opioid Addiction Programs

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将解决当前影响医疗保健、社会福利和美国经济的阿片类药物危机。目前，超过200万美国人患有阿片类药物使用障碍（OUD），但只有不到10%的人接受了有效的治疗。2017年，超过47,000名美国人死于处方阿片类药物、海洛因和非法生产的芬太尼，估计处方阿片类药物滥用的负担每年为785亿美元。与传统药物辅助治疗相关的重大后勤和患者负担造成了对结果和可及性产生负面影响的障碍。康复项目的一个主要挑战是提供者和治疗师的工作量，以及缺乏自动化工具来监控和提高患者的参与度。该提案将开发利用人工智能（AI）和预测分析的软件，以大大提高阿片类药物添加计划的成功率，以扩大高质量，高效的治疗。这个小企业创新研究（SBIR）第一阶段项目有三个目标。技术挑战是开发和验证有针对性的人工智能（AI）工具，使用已经在患者工作流程中捕获的数据，以便早期预测可能导致计划退出的患者问题。该平台将与工具集成，允许跨多个领域和干预点进行低负担、复杂的数据收集。例如，指标将存储患者参与情况，跟踪对规定事件的依从性，以及在治疗期间进行语言学/情绪状态分析。原型将部署在现场数据收集研究中，以确定可用性，并为算法开发提供丰富的数据集。然后，收集的数据将用于训练和测试人工智能算法，以早期发现患者辍学。我们将测量几个量作为训练和测试算法的潜在数据集。我们将评估几种模型，包括用于变量选择的线性回归，以及用于预测的聚类、深度学习网络和递归神经网络生存模型。目的是提前识别潜在的辍学，具有高灵敏度和特异性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will address the current opioid crisis impacting healthcare, social welfare, and the US economy. Currently over 2 million Americans suffer from an opioid use disorder (OUD), but less than 10 percent receive effective treatment. Over 47,000 Americans died in 2017 from prescription opioids, heroin, and illegally produced fentanyl, and the estimated burden of prescription opioid misuse is $78.5 billion per year. Significant logistics and patient burden associated with traditional medication-assisted treatment create barriers that negatively impact outcomes and accessibility. A major challenge in recovery programs is the workload of providers and therapists and a lack of automated tools to monitor and improve patient engagement. This proposal will develop software leveraging artificial intelligence (AI) and predictive analytics to greatly improve success rates in opioid addition programs to expand high quality, efficient treatment. This Small Business Innovation Research (SBIR) Phase I project targets three objectives. The technical challenge is to develop and validate targeted artificial intelligence (AI) tools using data already captured in patient workflow for early prediction of patient issues that may lead to program dropout. The platform will be integrated with tools allowing low burden, complex data collection across multiple domains and intervention points. For example, metrics will store patient engagement, track compliance with prescribed events, and linguistics/emotional state analytics during therapy sessions. A prototype will be deployed in a field data collection study to determine usability and a provide a rich set of data for algorithm development. Collected data will then be used to train and test AI algorithms for early detection of patient dropout. We will measure several quantities as potential data sets for training and testing algorithms. We will evaluate several models including linear regression for variable selection, as well as clustering, deep learning networks, and recurrent neural network survival models for prediction. The objective is to identify potential dropouts in advance, with high sensitivity and specificity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.