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

SBIR 第二阶段：通过电子健康记录分析进行患者风险分层的系统

• 批准号: 1534781
• 负责人: Thaddeus Fulford-Jones
• 金额: $ 75万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534781&HistoricalAwards=false
• 关键词: SBIR; Phase; II; System; Patient

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is significant; transitions of care impact millions of Americans every year. The healthcare system bears substantial cost and inefficiency
on account of suboptimal care transitions and overspending. This Phase II project will support progress towards a "learning healthcare system" and will extend the capabilities of data mining and machine learning in healthcareThe proposed project seeks to improve data mining technologies for healthcare decision support. This project will focus on the analysis of a broad variety
of data types that are common in healthcare settings. The anticipated improvements would allow frontline care staff, operational managers, and healthcare executives to assess and make stronger evidence-driven decisions regarding quality, cost, and access as patients move through the healthcare system. The enhanced data mining system would utilize state-of-the-art pattern recognition and machine learning techniques to dynamically process and interpret clinical, claims, and other types of healthcare data. If successful, this research will impact the state-of-the-art in healthcare analytics.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响/商业潜力非常重要；护理过渡每年影响数百万美国人。由于次优的过渡和超支，医疗保健系统具有巨大的成本和效率低下的效率。该第二阶段项目将支持朝着“学习医疗系统”的进步，并将扩大医疗服务中数据挖掘和机器学习的能力，旨在改善数据挖掘技术以提供医疗保健决策支持。该项目将重点介绍对医疗保健环境中常见数据类型的广泛分析。预期的改进将使一线护理人员，运营经理和医疗保健高管能够评估并在患者通过医疗保健系统时，就质量，成本和访问权限做出更强大的循证驱动决定。增强的数据挖掘系统将利用最先进的模式识别和机器学习技术来动态处理和解释临床，主张和其他类型的医疗保健数据。如果成功，这项研究将影响医疗保健分析的最先进。