STTR Phase I: Applying real-time data streams to predict operating room resource allocation with neural networks

STTR 第一阶段：应用实时数据流通过神经网络预测手术室资源分配

• 批准号: 2015012
• 负责人: Westin Hill
• 金额: $ 22.5万
• 依托单位: MENTE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015012&HistoricalAwards=false
• 关键词: STTR; Phase; Applying; real; time

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the cost of surgical operations. Instrument tracking enables hospitals to optimize the supply chain, with a potential annual benefit to the US healthcare system of $8.5 B. Predictive scheduling can save $500k per operating room (OR) by closing gap times between procedures. Similarly, instrument prediction assistance can save an OR an estimated $14 per minute. The project will gather procedure and tool data from the operating room and apply artificial intelligence to optimize OR processes. This project has the potential to improve the overall function of the surgical team by anticipating surgical instrument needs. This Small Business Technology Transfer (STTR) Phase I project advances the fields of medicine and artificial intelligence by leveraging intraoperative data gathered by surgical instrument tracking. This unique data stream offers one of the first quantitative windows into a surgical operation. The objective of this project is to create computational tools to improve operating room scheduling and instrument supply, and test them with real clinical data. Transformer networks, commonly used in natural language processing tasks, will be adapted for this application and leveraged as an autoregressive tool to predict parameters of interest. The system will generate data regarding variations among surgeons, procedures, and patients.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.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是降低外科手术的成本。仪器跟踪使医院能够优化供应链，每年为美国医疗保健系统带来的潜在效益为B美元。预测调度可以通过缩短手术之间的间隔时间，为每个手术室（OR）节省50万美元。类似地，仪器预测辅助可以为手术室节省大约每分钟14美元。该项目将从手术室收集程序和工具数据，并应用人工智能优化手术流程。该项目有可能通过预测手术器械需求来改善手术团队的整体功能。这个小企业技术转让（STTR）第一阶段项目通过利用手术器械跟踪收集的术中数据推进医学和人工智能领域。这种独特的数据流为外科手术提供了第一个定量窗口。本项目的目标是创建计算工具，以改善手术室调度和仪器供应，并用真实的临床数据对其进行测试。通常用于自然语言处理任务的Transformer网络将适用于此应用，并用作自回归工具来预测感兴趣的参数。该系统将生成关于外科医生、手术和患者之间差异的数据。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。