I-Corps: Software platform for predicting hospital patient re-admissions

I-Corps：用于预测医院患者重新入院的软件平台

• 批准号: 2147482
• 负责人: Ryan Buckley
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147482&HistoricalAwards=false
• 关键词: Corps; Software; platform; predicting; hospital

The broader impact/commercial potential of this I-Corps project is the development of predictive modeling tools to help reduce preventable hospital readmissions. One in every eight patients discharged from acute care hospitals is readmitted within 30 days. Over a quarter of these readmissions could be avoided with appropriate and timely healthcare interventions. The Centers for Medicare and Medicaid Services estimate that they spent over $17 billion per year on avoidable readmissions in 2015. In addition, there are nearly 100 hospitals that are fined over $1M annually for having hospital readmission rates much higher than industry averages. Insurers, accountable care organizations, self-insured employers such as large hospital systems, and health plans seek to decrease preventable readmissions to provide high quality care while managing their medical loss ratios. The busiest hospitals, consistently operating near or over maximum bed capacity lose revenue from low acuity preventable readmissions that reduce the institution’s case-mix index. The goal for the proposed technology is to provide better care to patients while simultaneously lowering costs for hospitals and insurers alike.This I-Corps project is based on the development of a software platform that includes machine learning algorithms to predict hospital readmission risk and identify specific factors contributing most to that risk for individual patients. The proof-of-concept for this technology was built using data from approximately 80,000 patient encounters over two years at a major academic medical center. It outperformed widely used industry standards by approximately 40%. These algorithms incorporate both modifiable and unmodifiable risk factors including various social determinants of health and incorporate fairness criteria to ensure predictions don’t reinforce biases of societal structures. Since these contributing risk factors may vary widely from one population to the next, each healthcare system or insurer requires their own unique predictive model based on their data. The proposed next steps are to identify and prioritize customer needs for the application of this technology such as algorithm validation services for each customer’s patient population, electronic health record interoperability, and user interface design.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.

更广泛的影响/通讯是通过适当和及时的医疗保健介绍避免使用预测建模工具。高于行业的平均值。 Sile Sile Sile Sile siltaneusls的成本都是医院的成本。该I-Corps项目是在软件平台上开发的，其中包括机器学习算法，以预测医院的Readmississk并确定对单个患者的最大贡献对于大约两年来，在一个主要的学术医疗中心使用大约tient的数据来建立技术。社会结构。健康记录互操作性和用户界面设计。此ARD使用Foundation MPACTS审查标准反映了NSF的值得进行的舒适评估。