SBIR Phase I: An Algorithm that Predicts the Readmission Risk of a Hospital Patient

SBIR 第一阶段：预测医院患者再入院风险的算法

• 批准号: 1746535
• 负责人: Mary Cott
• 金额: $ 22.5万
• 依托单位: HOME CARE BUSINESS SERVICES, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746535&HistoricalAwards=false
• 关键词: SBIR; Phase; Algorithm; Predicts; Readmission; SBIR; Phase; Algorithm; Predicts; Readmission

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the availability of a patient readiness formula, an algorithm that will improve outcomes by engaging patients in their own healthcare from hospital admission through discharge. The societal impact includes (1) reducing the cost related to readmissions for Congestive Heart Failure, Acute Myocardial Infarction, and pneumonia, a total of $5.8 billion annually paid by U.S. taxpayers, (2) decreasing the cost of each hospital stay by $5-10K per Medicare beneficiary by using proven treatment and discharge planning models, (3) reducing the burden on post-acute providers (primary care physician, long-term care, home health, ambulatory care) by achieving optimized clinical stability prior to discharge, and (4) providing a method for a patient population (studies show who are eager to participate) to actively engage in their own treatment decisions.The proposed project will create a proprietary algorithm that produces a patient-specific hospital readmission-risk score. The score is based on the algorithm's dynamic adjustment of all known readmission risk factors determined by the patient's unique profile that includes clinical and other factors. The goal of the project is to accurately predict a patient's readmission-risk using the dynamic algorithm. The results of the algorithm will be verified via the facility's claims system. Technical challenges to be addressed by Phase I include the compilation of known risk factors, development of the algorithm, and creating multi-tract data input methods to obtain live data. The research and development plan will focus on identifying risks, development of the algorithm, and determining methods for capturing live data needed to execute the algorithm.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力将是患者就绪公式的可用性，这是一种算法，通过使患者从医院入院到出院，可以通过使患者参与自己的医疗保健来改善结果。社会影响包括（1）降低与复杂性心力衰竭，急性心肌梗塞和肺炎有关的成本，每年总计58亿美元，美国纳税人每年支付的费用，（2）将每个医院的住院费用减少到每份医疗保险范围5-10k的费用上，以使用预期的医疗保险范围（3）重新培养（3） - （3）补充（3）（3）（3）补充（3）（3）（3）补充（3）医师，长期护理，家庭健康，门诊护理）通过在出院前实现优化的临床稳定性，以及（4）为患者人群提供一种方法（研究表明，渴望参与的研究）积极参与其自己的治疗决策。拟议的项目将创建一个可产生患者的医院读书的专有算法。该分数基于算法对所有已知的再入院风险因素的动态调整，该风险因素由患者的独特概况（包括临床和其他因素）确定。该项目的目的是使用动态算法准确预测患者的再入院风险。该算法的结果将通过该设施的索赔系统验证。第一阶段将要解决的技术挑战包括已知风险因素的汇编，算法的开发以及创建多卷数据输入方法以获取实时数据。研发计划将着重于确定风险，算法的发展以及确定捕获执行算法所需的实时数据的方法。