I-Corps: An Artificial Intelligence Agent for Healthcare Negotiation

I-Corps项目的更广泛影响在于减少了与健康保险公司谈判报销和索赔范围时所涉及的交易成本。健康保险公司在大多数医疗保健交易中发挥着作用，但不同保险公司之间和不同州之间的管理制度差异很大。这种复杂性导致对患者的覆盖水平低于最佳水平，并给消费者和提供者都造成了经济负担。从商业角度来看，这项技术可以增加供应商收到的补偿，并减少管理间接成本。最终，这意味着供应商将产生更多的收入，消费者将从医疗保健相关的金融负债中解脱出来。这个I-Corps项目进一步开发了一个综合自然语言理解、论证和机器学习功能的平台。以自然语言编写的医疗保健政策文档将被翻译成可用于人工智能（AI）自动推理的示意图、逻辑表示。该平台将通过复合模式获得信息，其中一些是通过监督机器学习发现的，数据定义涉及患者/提供者索赔的成功和失败程度，体现在保险公司的决策、保险公司对上诉的回应、患者/提供者特征以及定义政策的图式合同中；因此，与其他机器学习应用程序相比，机器学习上下文中的数据将非常丰富。患者特征和合同是上下文化信息，并为这种机器学习方法提供理论约束，这是理论指导机器学习相对较新的领域的一个新实例。总的来说，该平台将综合许多人工智能功能。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 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.