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

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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将是提供患者准备公式，该算法将通过让患者从入院到出院参与自己的医疗保健来改善结果。社会影响包括 (1) 减少与充血性心力衰竭、急性心肌梗死和肺炎再入院相关的费用，美国纳税人每年总共支付 58 亿美元，(2) 将每次住院费用减少 5-1 万美元通过使用经过验证的治疗和出院计划模型，每个医疗保险受益人，(3) 减轻急性期后提供者的负担（初级保健医生、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）为患者群体（研究表明谁渴望参与）提供一种积极参与自己的治疗决策的方法。拟议的项目将创建一种专有算法产生针对患者的再入院风险评分。该评分基于算法对所有已知再入院风险因素的动态调整，这些风险因素由患者的独特概况（包括临床和其他因素）确定。该项目的目标是使用动态算法准确预测患者的再入院风险。算法的结果将通过设施的索赔系统进行验证。第一阶段要解决的技术挑战包括已知风险因素的编译、算法的开发以及创建多道数据输入方法以获取实时数据。研究和开发计划将侧重于识别风险、开发算法以及确定捕获执行算法所需的实时数据的方法。