I-Corps: An Artificial Intelligence Agent for Healthcare Negotiation

I-Corps：用于医疗保健谈判的人工智能代理

• 批准号: 1936317
• 负责人: Douglas Fisher
• 金额: $ 5万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936317&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; Agent; Healthcare; Corps; Artificial; Intelligence; Agent; Healthcare

The broader impact of this I-Corps project is in reducing transaction costs involved when negotiating reimbursements and claims coverage with health insurers. Health insurers play a role in the majority of healthcare transactions, but administrative regimes vary widely between insurance providers and across state lines. This complexity results in less than optimal levels of coverage for patients and creates a financial burden for consumers and providers alike. From a commercial standpoint, this technology can increase the reimbursements received by providers and reduce administrative overhead costs. Ultimately, this means providers will generate increased revenues and consumers will experience relief from healthcare related financial liabilities.This I-Corps project further develops a platform that synthesizes natural language understanding, argumentation, and machine learning functionalities. Health care policy documents in natural language are to be translated into schematic, logical representations that are amenable to artificial intelligence (AI) automated inferencing. The platform will be informed by composite patterns, some found through supervised machine learning, with data definitions involving the degree of success and failure of patient/provider claims as manifest in insurer decisions, insurer responses to appeals, patient/provider characteristics, and schematized contracts that define policy; thus a datum in the machine learning context will be extraordinarily rich compared to other machine learning applications. Patient characteristics and contracts are contextualizing information and provide theoretical constraints with this machine learning methodology a novel instance of the relatively new field of theory-guided machine learning. Collectively, the platform will synthesize a number of AI functionalities.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.

该I-Corps项目的更广泛的影响是在与健康保险公司进行报销和索赔承保范围时降低交易成本。健康保险公司在大多数医疗保健交易中发挥了作用，但是在保险提供商和跨州方面的行政制度差异很大。这种复杂性导致患者的覆盖范围不足，并为消费者和提供者带来了经济负担。从商业角度来看，这项技术可以增加提供商收到的报销，并降低行政间接费用。最终，这意味着提供商将产生更多的收入，消费者将减轻与医疗保健相关的金融负债的缓解。此I-Corps项目进一步开发了一个平台，该平台综合了自然语言理解，论证和机器学习功能。自然语言的卫生保健政策文件应转化为可容纳人工智能（AI）自动推论的逻辑表示。 该平台将通过综合模式告知，其中一些是通过监督机器学习发现的，其中数据定义涉及保险人决策，保险公司对上诉，患者/提供者特征的响应，以及定义保单的案件的合同；因此，与其他机器学习应用程序相比，机器学习环境中的基准将非常丰富。患者的特征和合同是将信息进行上下文化的信息，并使用此机器学习方法学提供理论约束，这是理论指导机器学习的相对新领域的新实例。该平台总的来说，该平台将综合许多AI功能。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。