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

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医院再入院。每8名从急性护理医院出院的患者中，就有1人在30天内再次入院。通过适当和及时的医疗干预，可以避免超过四分之一的再次入院。医疗保险和医疗补助服务中心估计，2015年，他们在可避免的重新接纳上每年花费超过170亿美元。此外，还有近100家医院每年因再住院率远高于行业平均水平而被罚款100万美元以上。保险公司、负责任的护理组织、大型医院系统等自我保险的雇主和健康计划寻求减少可预防的重新入院，以提供高质量的护理，同时管理其医疗损失率。最繁忙的医院，持续在接近或超过最大床位容量的情况下运营，会因为低敏感度、可预防的再次住院而损失收入，这会降低机构的病例组合指数。这项拟议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。这个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，以预测医院再次住院的风险，并确定对个别患者的风险最大的特定因素。这项技术的概念验证是使用一家主要学术医疗中心两年来约80,000名患者的数据构建的。它的表现比广泛使用的行业标准高出约40%。这些算法纳入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，并纳入了公平标准，以确保预测不会强化社会结构的偏见。由于这些导致风险的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的独特预测模型。建议的下一步是确定客户对这项技术应用的需求并确定其优先顺序，例如为每个客户的患者群体提供算法验证服务、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 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.