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.

这个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再次入院。 从急症护理医院出院的每八名病人中就有一名在30天内再次入院。超过四分之一的再入院可以通过适当和及时的医疗干预来避免。医疗保险和医疗补助服务中心估计，他们在2015年每年花费超过170亿美元用于可避免的再入院。此外，有近100家医院每年因再入院率远高于行业平均水平而被罚款超过100万美元。保险公司、负责任的医疗机构、大型医院系统等自我保险的雇主以及健康计划都在寻求减少可预防的再入院，以提供高质量的医疗服务，同时管理其医疗损失率。最繁忙的医院，一贯接近或超过最大床位容量的运营损失收入从低敏度可预防的再入院，减少机构的病例组合指数。 该技术的目标是为患者提供更好的护理，同时降低医院和保险公司的成本。该I-Corps项目基于一个软件平台的开发，该平台包括机器学习算法，用于预测医院再入院风险，并识别对个体患者风险影响最大的特定因素。该技术的概念验证是使用一家大型学术医疗中心两年来约80，000名患者的数据构建的。它比广泛使用的行业标准高出约40%。这些算法结合了可修改和不可修改的风险因素，包括各种健康的社会决定因素，并结合了公平标准，以确保预测不会强化社会结构的偏见。由于这些风险因素可能因人群而异，因此每个医疗保健系统或保险公司都需要基于其数据建立自己独特的预测模型。建议的下一步是确定和优先考虑客户对该技术应用的需求，如针对每个客户的患者群体的算法验证服务、电子健康记录互操作性和用户界面设计。该奖项反映了NSF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。