SBIR Phase I: System for Patient Risk Stratification through Electronic Health Record Analytics

SBIR 第一阶段：通过电子健康记录分析进行患者风险分层的系统

• 批准号: 1416215
• 负责人: Thaddeus Fulford-Jones
• 金额: $ 15万
• 依托单位: Radial Analytics, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1416215&HistoricalAwards=false
• 关键词: SBIR; Phase; System; Patient; Risk

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on using analytics and technology to drive greater efficiency and effectiveness in healthcare. Recent legislative changes are driving all players within the healthcare ecosystem toward greater accountability. This Phase I project specifically includes technologies to automatically assess patient risk and thereby reduce post-discharge readmissions rates. This Phase I project has the potential to support a broad range of customers across both the provider and the payer landscape, by providing cost-effective readmissions control solutions that respond to new legislative pressures. In terms of commercial potential, the Institute of Medicine of the National Academies has estimated that preventable hospital readmissions account for $20 billion/year in wasteful healthcare spending. The addressable market for the proposed Phase I proof-of-concept for patient risk stratification to support readmission control is approximately $100MM. In the future, this research project will serve as a foundation to support broader population health analytics, the addressable market for which exceeds $500MM/year and is growing at a rate of 24% annually.The proposed project aims to develop a data mining system to capture and analyze information from electronic medical records in order to risk-stratify patients after they have been discharged from hospital. Leveraging interoperability standards that are required by federal regulation, the system will seamlessly aggregate data from multiple electronic medical record systems in a vendor-agnostic manner. A custom analytics engine will detect emergent patterns and draw inferences about each patient?s risk of readmission. If successful, this research will validate the end-to-end concept and suggest the broader applicability of this approach to some of the greatest challenges in population health.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力着重于使用分析和技术来提高医疗保健方面的效率和有效性。最近的立法变化正在推动医疗保健生态系统中的所有参与者提高问责制。该阶段I项目专门包括自动评估患者风险的技术，从而降低了入院后再入院率。该阶段I项目有可能通过提供具有成本效益的重新入境控制解决方案来支持提供商和付款人格局的广泛客户，以应对新的立法压力。在商业潜力方面，国家学院医学研究所估计，可预防的医院再入院率占浪费医疗保健支出的200亿美元。拟议的I期概念证明的可寻址市场支持患者风险分层，以支持再启动控制。将来，该研究项目将成为支持更广泛的人口健康分析的基础，该市场的可寻址市场超过500mm/年，并且每年以24％的速度增长。拟议的项目旨在开发数据挖掘系统，以捕获和分析来自电子病历的信息，以便在住院后出院后，以使患者出院。利用联邦法规要求的互操作性标准，该系统将以供应商 - 敏捷的方式无缝从多个电子病历系统中汇总数据。自定义分析引擎将检测出紧急模式，并提出有关每个患者再入院风险的推断。如果成功，这项研究将验证端到端的概念，并提出这种方法在人口健康中最大的挑战中更广泛的适用性。