Technology Application to Enhance Discharge Referral Decision Support

技术应用增强出院转诊决策支持

基本信息

  • 批准号:
    8710917
  • 负责人:
  • 金额:
    $ 74.96万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-09-01 至 2016-08-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Decreasing readmissions through better discharge planning (DP) and transitional care is a national healthcare priority. RightCare Solutions has leveraged over 10 years of interdisciplinary academic research led by a nurse researcher, and through our highly successful phase one SBIR grant demonstrated market value for the D2S2 product and the technical expertise of our team. The D2S2 is a six-item decision support tool installed by RightCare Solutions in the hospital EHR to assist discharge planners to identify high-risk patients upon admission allowing time and focus to target appropriate transitional and post-acute care to prevent readmissions. We have achieved outstanding outcomes from our phase 1 award and are proposing further technological developments to enhance our commercial launch. The market potential for the D2S2 tool is significant since discharge decision support is estimated to be applicable to roughly 6,500 U.S. hospitals with a census that is 60% older adults equaling 14 million discharges per year. The phase 1 results indicate a significant impact on 30 and 60 day readmissions giving us strong evidence as to the value of the product. However, the results and our experience using the software indicate there is opportunity to enhance the product's accruacy and functionality. We propose to enhance our predictive accuracy through innovative data mining and machine learning techniques and to improve the functionality by electronically connecting the acute and post-acute care settings. Due to implementation in the three hospitals of the University of Pennsylvania Health System we have data on over 6,000 patients and through the continued live implementation we will accumulate data on over 25,000 patients by the start of the phase 2 grant. Using existing, and new data generated from continued operations, this proposed SBIR grant will advance the product in two major ways to enhance the commercial benefit to its users. Aim 1: Develop, test, and scale SMART capabilities, a dynamic process for improving prediction accuracy, by using hospital-specific and patient level characteristics (D2S2 variables and additional clinical and non-clinical characteristics) and modern data mining/machine learning techniques. AIM 2: Operationalize the D2S2 recommendations by electronically connecting high-risk patients with post-acute care (PAC) providers and stakeholders, known as CONNECT capabilities. Called a "learning health system" the end- product of this SBIR grant will provide continuous evaluation and improvement to the end-user measured against their goals. Our innovative design produces a closed-loop system that will use data from the D2S2 and hospital databases over time to "get smarter." Further, our enhanced product will link acute care to post-acute providers giving them advanced warning of patients who will shortly come to them for care.
描述(由申请人提供):通过更好的出院计划(DP)和过渡护理减少再入院是国家医疗保健的优先事项。RightCare Solutions利用了由一位护士研究员领导的10多年跨学科学术研究,通过我们非常成功的第一阶段SBIR拨款,展示了D2S2产品的市场价值和我们团队的技术专长。D2S2是RightCare Solutions在医院电子病历中安装的六项决策支持工具,可帮助出院计划人员在入院时识别高危患者,从而有时间和重点针对适当的过渡和急性后护理,以防止再次入院。我们已经从第一阶段的奖项中取得了杰出的成果,并正在提出进一步的技术发展,以加强我们的商业发射。D2S2工具的市场潜力是巨大的,因为出院决策支持估计适用于大约6,500家美国医院,人口普查中60%的老年人相当于每年1400万例出院。第一阶段的结果表明,对30天和60天的再入院有重大影响,这为我们提供了产品价值的有力证据。然而,结果和我们使用该软件的经验表明,有机会提高产品的积累和功能。我们建议通过创新的数据挖掘和机器学习技术来提高我们的预测准确性,并通过电子连接急性和急性后护理环境来改善功能。由于在宾夕法尼亚大学卫生系统的三家医院实施,我们有超过6000名患者的数据,通过持续的现场实施,我们将在第二阶段拨款开始时积累超过25000名患者的数据。利用现有的和从持续运营中产生的新数据,这项拟议的SBIR拨款将以两种主要方式推进该产品,以增强其用户的商业利益。目标1:通过使用医院和患者层面的特征(D2S2变量和其他临床和非临床特征)以及现代数据挖掘/机器学习技术,开发、测试和扩展SMART功能,这是一个用于提高预测准确性的动态过程。目标2:通过电子连接高风险患者与急性后护理(PAC)提供者和利益相关者(称为CONNECT功能)来实施D2S2建议。这项SBIR赠款的最终产品被称为“学习型卫生系统”,它将根据最终用户的目标,为他们提供持续的评估和改进。我们的创新设计产生了一个闭环系统,该系统将使用来自D2S2和医院数据库的数据,随着时间的推移“变得更智能”。此外,我们的增强型产品将把急症护理与急症后护理联系起来,让他们提前警告即将来找他们治疗的患者。

项目成果

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Eric Heil其他文献

Eric Heil的其他文献

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{{ truncateString('Eric Heil', 18)}}的其他基金

Reducing heart failure re-admissions by enhancing sleep apnea treatment adherence
通过提高睡眠呼吸暂停治疗依从性来减少心力衰竭再次入院
  • 批准号:
    8986876
  • 财政年份:
    2014
  • 资助金额:
    $ 74.96万
  • 项目类别:
Reducing heart failure re-admissions by enhancing sleep apnea treatment adherence
通过提高睡眠呼吸暂停治疗依从性来减少心力衰竭再次入院
  • 批准号:
    8976905
  • 财政年份:
    2014
  • 资助金额:
    $ 74.96万
  • 项目类别:
Reducing heart failure re-admissions by enhancing sleep apnea treatment adherence
通过提高睡眠呼吸暂停治疗依从性来减少心力衰竭再次入院
  • 批准号:
    9054917
  • 财政年份:
    2014
  • 资助金额:
    $ 74.96万
  • 项目类别:
Technology Application to Enhance Discharge Referral Decision Support
技术应用增强出院转诊决策支持
  • 批准号:
    8919460
  • 财政年份:
    2014
  • 资助金额:
    $ 74.96万
  • 项目类别:
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