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

这个小型企业技术转移(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，每年可为美国医疗保健系统带来8.5美元的潜在收益。预测性计划通过缩短程序之间的间隔时间，可以为每个手术室(OR)节省50万美元。同样，仪器预测辅助每分钟可节省OR估计为14美元。该项目将从手术室收集程序和工具数据，并应用人工智能来优化OR流程。这个项目有可能通过预测手术器械的需求来改善手术团队的整体功能。这个小企业技术转移(STTR)第一阶段项目通过利用手术器械跟踪收集的术中数据，推动了医学和人工智能领域的发展。这一独特的数据流提供了外科手术的第一个量化窗口之一。这个项目的目标是创建计算工具来改进手术室调度和器械供应，并用真实的临床数据进行测试。变压器网络，通常用于自然语言处理任务，将被适应于这一应用，并被用作预测感兴趣的参数的自回归工具。该系统将生成关于外科医生、手术程序和患者之间差异的数据。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.