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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力侧重于使用分析和技术来推动医疗保健领域更高的效率和效果。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走向更大的问责。该第一阶段项目具体包括自动评估患者风险从而降低出院后再住院率的技术。这个第一阶段的项目有可能通过提供符合成本效益的重新接纳控制解决方案来应对新的立法压力，从而支持提供商和支付方的广泛客户。在商业潜力方面，美国国家科学院医学研究所估计，每年浪费的医疗支出中，可预防的再次住院占200亿美元。拟议的患者风险分层的第一阶段概念验证以支持再入院控制的潜在市场约为1亿美元。未来，这项研究项目将成为支持更广泛的人口健康分析的基础，其潜在市场超过500 mm/年，并以每年24%的速度增长。拟议的项目旨在开发一个数据挖掘系统，从电子病历中捕获和分析信息，以便在患者出院后对他们进行风险分层。利用联邦法规所要求的互操作性标准，该系统将以供应商无关的方式无缝地从多个电子病历系统收集数据。一个定制的分析引擎将检测紧急模式，并得出关于每个患者的推断-S再次住院的风险。如果成功，这项研究将验证端到端的概念，并表明这种方法在应对人口健康方面的一些最大挑战方面具有更广泛的适用性。

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.