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

基本信息

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

项目摘要

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使需要康复护理的患者受益于从医院出院后完全好起来。从急性（医院）到急性后（短期康复和熟练护理）环境的护理过渡会影响数百万美国人。由于自然与年龄相关的变性以及需要更长的恢复时期的需求，老年人对急性后护理的利用过度指数。通过改善整个连续性的医疗后护理后协调，临床结果和患者满意度可以改善美国的老龄化人口。如果成功，该项目将有助于减少医疗保健提供者，付款人和政府/社会的护理成本。拟议的项目旨在纳入并改进医疗保健中以用户为中心的数据捕获的方法。我们提出的平台将将高级机器学习技术与以患者/家庭为中心的业务模型相结合。该创新将利用多个医疗保健数据流，例如电子健康记录，并索赔急性和急性后护理设置的数据。如果成功，这项研究将影响医疗保健分析和成果测量的最先进。