I-Corps: Machine Learning based Clinical Decision Support Tool to Predict 30-day Hospital Readmissions for Congestive Heart Failure Patients

I-Corps项目更广泛的影响/商业潜力是开发一种临床决策支持软件工具，用于预测诊断为充血性心力衰竭（CHF）的患者30天内的再入院率。慢性心力衰竭是一种慢性心脏疾病，心脏不能充分向身体泵血。众所周知，CHF是住院和再入院的头号原因。医院再入院与患者的负面结果独立相关，医疗保险每年花费 27亿美元用于瑞士法郎再入院。最重要的是，估计70%的再入院是可以预防的，因此，医疗保险和医疗补助服务中心（CMS）开始惩罚高再入院率的医疗机构。2018年，所有医院的再入院罚款高达5.5亿美元，约2500家医院受到影响。该软件工具可以推广到CHF以外的其他疾病，并有可能帮助减少这些可避免的再入院，改善患者的预后，并为患者、支付方和医院节省成本。I-Corps项目的基础是开发一种临床决策支持工具，以预测30天内的再入院情况。这是一个基于机器学习的软件工具，由大规模临床数据集支持，用于预测入院并被诊断为充血性心力衰竭（CHF）的患者再入院的可能性。这项技术是一种基于云的软件工具，将与电子健康记录（EHRs）集成，可以帮助病例管理人员对充血性心力衰竭患者进行分类和规划。在病例管理人员和质量改进主管提供的当前信息的基础上，团队收集其他信息，以更深入地了解临床工作流程（例如，主治医生、过渡指南、病例管理人员、主任和首席护理官）、收入流（例如，医院采购委员会）、报销请求（医院和支付方）和监管要求(例如，FDA 510(k)监管审批程序)建立与行业潜在合作伙伴的联系。目标是通过采访这些涉众来构建一个基于证据的产品解决方案，并制定计划来推动技术的商业化。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 I-Corps project is the development of a clinical decision support software tool to predict 30-day hospital readmissions for patients diagnosed with congestive heart failure (CHF). CHF is a chronic cardiac disease where the heart does not adequately pump blood to the body. CHF is known to be the number one reason for hospital admissions and readmissions. Hospital readmissions are independently associated with negative patient outcomes, and Medicare spends  $2.7 billion on CHF readmissions annually. On top of that, an estimated 70% of those readmissions are preventable, and as a result, the Centers for Medicare and Medicaid Services (CMS) started to penalize health organizations for high rates of readmission. Re-admission penalties were up to $550 million across all hospitals in 2018, with ~2,500 hospitals being affected. The software tool may be generalized to other diseases beyond CHF and has the potential to help reduce these avoidable readmissions improving patient outcomes and leading to cost savings for patients, payers, and hospitals.This I-Corps project is based on the development of a clinical decision support tool to predict 30-day hospital readmissions. This is a machine learning-based software tool supported by large-scale clinical datasets to predict the likelihood of readmissions for patients admitted to the hospitals and diagnosed with congestive heart failure (CHF). This technology is a cloud-based software tool that will integrate with electronic health records (EHRs) and can assist case managers with triage and planning of congestive heart failure patients. Building on current information from case managers and quality improvement directors, the team gathers additional information to gain a deeper understanding of the clinical workflow (e.g., attending physicians, transition guides, case managers, directors, and chief nursing officers), revenue streams (e.g., hospital purchasing committees), reimbursement requests (hospitals and payers), and regulatory requirements (e.g., FDA 510(k) regulatory clearance process) to establish connections with potential partners in industry. The goal is to build an evidence-based product solution by interviewing these stakeholders and to establish plans to move the commercialization of the technology forward.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.