SBIR Phase I: User-Centered System for Improved Coordination across the Continuum of Care

SBIR 第一阶段：以用户为中心的系统，以改善整个护理连续性的协调

• 批准号: 1621996
• 负责人: Thaddeus Fulford-Jones
• 金额: $ 22.5万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1621996&HistoricalAwards=false
• 关键词: SBIR; Phase; User; Centered; System

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on using analytics and technology to benefit patients who require recovery care to get fully well after being discharged from the hospital. Transitions of care from acute (hospital) to post-acute (short-term rehabilitation and skilled nursing) settings impact millions of Americans every year. Seniors overindex on utilization of post-acute care, as a consequence of natural age-related degeneration and the need for longer recovery periods. By improving post-hospital coordination of care across the continuum, clinical outcomes and patient satisfaction stand to improve for America?s aging population. If successful, this project will help reduce costs of care for healthcare providers, payers, and government/society.The proposed project aims to incorporate and improve upon methods for user-centered data capture in healthcare. Our proposed platform will combine advanced machine learning techniques with a patient/family-centered business model. The innovation will harness multiple streams of healthcare data, such as electronic health records and claims data from both acute and post-acute care settings. If successful, this research will impact the state-of-the-art in healthcare analytics and outcomes measurement.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力集中在使用分析和技术，使需要康复护理的患者在出院后完全康复。从急性（医院）到急性后（短期康复和熟练护理）的护理过渡每年都会影响数百万美国人。老年人对急性期后护理的利用率过高，这是自然的年龄相关退化和需要更长的恢复期的结果。通过改善整个连续体的院后护理协调，临床结果和患者满意度将改善美国？人口老龄化。如果成功，该项目将有助于降低医疗保健提供者，支付者和政府/社会的护理成本。拟议的项目旨在整合和改进以用户为中心的医疗保健数据采集方法。我们提出的平台将联合收割机先进的机器学习技术与以患者/家庭为中心的商业模式相结合。这项创新将利用多个医疗数据流，例如来自急性和急性后护理环境的电子健康记录和索赔数据。如果成功，这项研究将影响最先进的医疗保健分析和结果测量。