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STTR Phase I: Accelerating the dissemination of healthcare interventions that improve care for high-need/high-cost patients

STTR Phase I: Accelerating the dissemination of healthcare interventions that improve care for high-need/high-cost patients
STTR 第一阶段:加速医疗保健干预措施的传播,改善对高需求/高成本患者的护理
批准号:
1746142
负责人:
Linda Cummings
金额:
$22.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-01-01 至 2019-02-28

项目摘要

项目成果

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中文摘要
翻译
这项小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力包括:通过使高需求/高成本患者的信息立即可获得并为医疗保健提供者量身定制,加快医疗保健改善的步伐;将卫生干预数据库转变为关心高需求/高费用患者的积极和动态学习社区;减轻安全网提供者对向高需求/高成本患者提供护理的数十个信息来源进行分类和筛选的负担。卫生信息网络工具(HINT)将是定制算法的独特组合,将填补知识方面的关键空白,特别是在卫生差距、健康的社会决定因素和服务不足的条件方面;增加安全网提供者与同行联系的机会,共同解决问题;减少安全网医疗保健组织中错误和失败干预措施的重复和重复;提高公众对制定有希望的干预措施的安全网机构的认识;并使机器学习的技术进步能够为高需求/高成本患者提供护理模式。对HINT的商业影响包括:降低向高需求/高成本人群提供护理的成本并提高其质量;并为安全网机构和提供者创造机会,推销他们在照顾复杂的、得不到充分服务的患者方面的专业知识。提出的项目是基于这样一种信念:在整个安全网卫生系统中,每天都有照顾社会最弱势群体的巧妙解决方案;HINT将通过提供前所未有的资源来传播大量不断变化的医疗保健信息,从而加速创新;拟议的信息网络将减少目前发生的信息碎片化和重复的错误和失败的干预;最后,释放和传播来自同行的创新和建议——在类似的机构和照顾类似的病人——将加速成功的实践,改善高需求/高成本病人的健康。HINT提出开发独特的爬行、聚类、文本挖掘、协同过滤、二部匹配和排序算法。该团队将把这六种算法集成到一个定制的社交内容管理系统中,并解决设计和功能方面的挑战。
英文摘要
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase 1 project includes: accelerating the pace of healthcare improvement by making information on high-need/high-cost patients instantly accessible and individually tailored to health care providers; transforming health intervention databases into active and dynamic learning communities about caring for high-need/high-cost patients; reducing the burden on safety net providers to sort and sift through dozens of information sources about delivering care to high-need/high-cost patients. Health Information Networking Tool (HINT) will be a unique combination of customized algorithms that will fill in critical gaps in knowledge, especially around health disparities, the social determinants of health and underserved conditions; boost opportunities for safety net providers to connect with peers to engage in collaborative problem-solving; reduce duplication and repetition of errors and failed interventions across safety net healthcare organizations; increase public recognition for safety net institutions that develop promising interventions; and enable technical advancements in machine learning to suggest models of care for high-need/high-cost patients. The commercial impact for HINT includes: reducing the cost and improving the quality of care delivered to high-need/high cost populations; and creating opportunities for safety net institutions and providers to market their expertise on caring for complex, underserved patients.The proposed project was conceived on the belief that ingenious solutions in caring for society's most vulnerable populations occur daily across the safety net health system; that HINT will accelerate innovation by bringing an unprecedented resource to disseminate voluminous and constantly changing healthcare information; that the proposed information network will reduce the fragmentation of information and duplication in errors and failed interventions that currently occur; and finally that unlocking and disseminating innovations and advice from peers - in similar institutions and caring for similar patients - will accelerate successful practices to improve the health of high-need/high-cost patients. HINT proposes to develop unique crawling, clustering, text mining, collaborative filtering, bipartite matching, and ranking algorithms. The team will integrate the six algorithms into a customized social content management system, and address challenges around design and functionality.
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