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.

该I-Corps项目的更广泛的影响/商业潜力是开发预测建模工具，以帮助减少可预防的医院再入院。从急诊医院出院的八名患者中有1例在30天内阅读。可以通过适当，及时的医疗干预措施来避免使用这些再入院的四分之一。 Medicare和Medicaid服务中心估计，他们每年在2015年避免再入院费用超过170亿美元。此外，由于医院再入院率高于行业平均水平，每年有将近100家医院每年最终确定100万美元以上。保险公司，负责任的护理组织，自保雇主（例如大型医院系统）和健康计划寻求减少可预防的再入院，以提供高质量的护理，同时管理其医疗损失比率。最繁忙的医院始终在接近或超过最大床位上运作，从而导致低敏锐的可预防再入院率会减少该机构的案例混合指数。拟议技术的目标是为患者提供更好的护理，同时简单地降低医院的成本并提供确保。该I-Corps项目基于一个软件平台的开发，该软件平台包括机器学习算法以预测医院再入院风险，并确定对个人患者的最大影响的特定因素。该技术的概念概念是在两年内在一个主要的学术医学中心遇到的大约80,000名患者遇到的数据构建的。它的表现优于广泛使用的行业标准约40％。这些算法纳入了可修改和不可修改的风险因素，包括健康的各种社会决定者和纳入公平标准，以确保预测不会增强社会结构的偏见。由于这些造成的风险因素可能会因一个人群而异，因此每个医疗保健系统或保险公司都需要根据其数据自己的独特预测模型。提出的下一步是确定和优先考虑客户对该技术应用的需求，例如为每个客户的患者群体，电子健康记录互操作性和用户界面设计等算法验证服务。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来通过评估来进行评估的支持。