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I-Corps: Novel File Exchange Software Platform to Increase Patient Volume and Hospital System Efficiency

I-Corps: Novel File Exchange Software Platform to Increase Patient Volume and Hospital System Efficiency
I-Corps:新型文件交换软件平台,可增加患者数量和医院系统效率
批准号:
2204754
负责人:
Nicole Jiam
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-04-01 至 2023-09-30

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中文摘要
翻译
这个I-Corps项目更广泛的影响/商业潜力是开发转诊处理软件,以提高医院系统的工作流程效率,而不影响患者数量。新患者预约是重要的医疗保健绩效指标,因为它们是重要的收入来源,对所有医疗保健系统的战略增长至关重要。据估计,每年的转诊市场为23亿美元。在美国,每年有三分之一的患者被转介去看专科医生。然而,多达50%的患者转诊从未完成-导致估计每年损失3700亿美元的收入。这个I-Corps项目的基础是开发一个处理病人转诊的软件平台,为两个不同的医院系统之间交换医疗文件提供一个更透明的闭环解决方案。该技术采用机器学习算法,为医院领导层提供数据分析和运营效率洞察。此外,该信息可用于提供实践和行为建议,以满足机构定义的绩效指标。通过这样做,所提出的技术解决了新患者调度延迟的主要原因,并增加了医疗保健系统的收入。它还可以减少病人护理的延误,避免由于不完整的医疗记录而重复不必要的程序和测试。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact/commercial potential of this I-Corps project is the development of referral processing software to increase hospital system workflow efficiency without compromising patient volume. New patient appointments are important healthcare performance metrics because they are a significant revenue source and critical for strategic growth for all healthcare systems. The referral market is estimated at 2.3 billion dollars annually. In the United States, 1 out of 3 patients are referred to see a specialist every year. However, as many as 50% of these patient referrals are never completed – resulting in an estimated annual loss of $370 billion in revenue. This I-Corps project is based on the development of a software platform to process patient referrals to provide a more transparent closed-loop solution towards exchanging medical documents between two different hospital systems. The proposed technology employs machine learning algorithms to provide data analytics and insight into operational efficiency for hospital leadership. In addition, this information may be used to provide practice and behavioral recommendations to meet performance metrics defined by the institution. In doing so, the proposed technology addresses the major contributor to new patient scheduling delays and increases revenue for healthcare systems. It also may decrease delays in patient care and avoid repeating unneeded procedures and tests because of incomplete medical records.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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