I-Corps: An AI-based Physician Advisory System for Disease Management

这个i-Corps项目的更广泛的影响/商业潜力是，它有可能帮助每年患有心力衰竭的570万美国成年人，并可能节省每年用于心力衰竭护理的307亿美元中的一大部分。它还提供了医院和诊所提供的心力衰竭护理质量的量化衡量标准。心力衰竭的最佳治疗需要遵循循证临床指南。心力衰竭指南是由一个多学科专家委员会制定的，基于对有关心力衰竭管理的最佳可用临床证据的彻底审查。这一指南代表了专家们对心力衰竭的适当治疗和管理的共识。然而，内科医生和心脏病专家对临床指南的低依从性是心力衰竭管理中的主要挑战之一。这项提议旨在克服这一挑战。仅在美国，这种技术每年的潜在市场就约为1.06亿美元，还有一个更大的全球市场，并有可能开发更多的扩展来管理类似的疾病。这个i-Corps项目自动化了当前心力衰竭管理指南中的整套规则。该系统基于答案集编程，这是一种适用于模拟人类风格推理的声明性编程形式。在给定患者信息的情况下，系统会生成一组符合指南的建议。我们在20名真实心力衰竭患者和10名模拟心力衰竭患者身上进行了该系统的初步研究。结果显示，该系统推荐符合心力衰竭管理指南的治疗方法。在该系统提出的179项建议中，心脏病专家同意了其中的168项。我们的系统提出的11项建议与心脏病专家的意见不一致，这是由于对医生的笔记或指南的误解，这些将在未来的软件版本中修复。我们的研究为自动生成符合指南的建议奠定了基础。我们的计划是将该系统开发为诊所就诊的即时护理工具。该系统将实时反馈适合指南的护理建议，并成为衡量机构心力衰竭护理质量指标的质量评估工具。同样的方法也适用于哮喘和癌症等其他疾病的管理。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 I-Corps project is its potential to help the 5.7 million adult Americans who are living with heart failure every year and potentially saving a significant fraction of the $30.7 billion dollars spent each year on heart failure care. It also provides a quantitative measure of the quality of heart failure care provided in hospitals and clinics. Optimal management of heart failure requires adherence to evidence based clinical guidelines. The Heart Failure Guidelines were created by a multi-disciplinary committee of experts and is based on thorough review of best available clinical evidence on management of heart failure. This guideline represents a consensus among experts on the appropriate treatment and management of heart failure. However, low adherence to the clinical guideline by physicians and cardiologists is one of the major challenges in the management of heart failure. This proposal seeks to overcome this challenge. The addressable market for this technology is about $106 million yearly in the United States alone with an even larger global market and the potential to develop additional extensions to manage similar diseases. This I-Corps project automates the entire set of rules in the current guideline for heart failure management. The system is based on answer set programming, a form of declarative programming suited for simulating human-style reasoning. Given a patient's information, the system generates a set of guideline-compliant recommendations. We conducted a pilot study of the system on 20 real and 10 simulated patients with heart failure. The results show that the system recommends treatments that are compliant with the guidelines for heart failure management. Out of 179 recommendations made by the system, expert cardiologists agreed upon 168 of them. The 11 recommendations that our system made not in agreement with cardiologists are due to misunderstanding of the doctor's notes or guideline which will be fixed in the future version of the software. Our research has established the foundation for automating the generation of guideline-compliant recommendations. Our plan is to develop the system into a point-of-care tool for clinic visits. The system would give real-time feedback of guideline-appropriate care recommendations, and be a quality assessment tool to measure the quality metrics of heart failure care of institutions. The same method applies to the management of other diseases such as Asthma and Cancer.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.