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.

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响/商业潜力是巨大的；护理的转变每年都会影响数百万美国人。由于护理过渡不理想和超支，医疗保健系统承受着巨大的成本和低效率。该第二阶段项目将支持“学习型医疗保健系统”的进展，并将扩展医疗保健领域数据挖掘和机器学习的能力。拟议项目旨在改进用于医疗保健决策支持的数据挖掘技术。该项目将重点分析医疗保健环境中常见的各种数据类型。预期的改进将使一线护理人员、运营经理和医疗保健管理人员能够在患者通过医疗保健系统时评估质量、成本和可及性，并做出更有力的证据驱动决策。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、索赔和其他类型的医疗数据。如果成功，这项研究将影响医疗保健分析领域的最新技术。