I-Corps: Real-Time Update System for Hospitals

I-Corps：医院实时更新系统

• 批准号: 1659160
• 负责人: Jean Walrand
• 金额: $ 5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2018-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659160&HistoricalAwards=false
• 关键词: Corps; Real; Time; Update; System

The broader impact/commercial potential of this I-Corps project is a significant increase in the efficiency of hospital procedure scheduling. The project analyzes the sources of inefficiencies in the planning, staffing, coordination, and execution of the various phases of surgeries and associated procedures. Preliminary analysis of hospital data shows that the throughput of surgical units can be increased significantly with the same staffing while reducing the delays and improving the working conditions of the personnel. Delays in obtaining and communicating updates on the status of surgeries and on actions that personnel should perform are major causes of inefficiency, as is the randomness of the duration of tasks. We expect the methodology to be widely applicable to the coordination of teams in services and transportation industries.This I-Corps project is based on a machine learning approach to the optimization of real-time messaging tuned using actual hospital data. The technical novelty is a formulation of the real-time policies that incorporates the messages between the main actors and enables the use of machine learning to optimize the policies. The approach combines new parametric models of real-time scheduling, stochastic gradient descent, and infinitesimal perturbation analysis. In this formulation, perturbation analysis computes the gradient of the objective function with respect to the timing of messages and results in an efficient algorithm. The algorithm discovers the best time to send messages to optimize a combination of operating room efficiency and patient waiting times.

这个I-Corps项目的更广泛的影响/商业潜力是医院程序调度效率的显著提高。 该项目分析了在手术和相关程序的各个阶段的规划、人员配备、协调和执行中效率低下的原因。 对医院数据的初步分析表明，在人员配置不变的情况下，外科单位的工作量可以大大增加，同时减少延误，改善工作人员的工作条件。在获取和传达关于手术状态和工作人员应采取的行动的最新情况方面的延迟是效率低下的主要原因，任务持续时间的随机性也是如此。我们希望该方法能够广泛应用于服务和运输行业的团队协调。这个I-Corps项目基于机器学习方法，使用实际的医院数据优化实时消息传递。技术新奇是实时策略的制定，该策略包含主要参与者之间的消息，并允许使用机器学习来优化策略。 该方法结合了新的参数模型的实时调度，随机梯度下降，和无穷小扰动分析。在这个公式中，扰动分析计算目标函数的梯度相对于消息的定时，并在一个有效的算法的结果。 该算法发现发送消息的最佳时间，以优化手术室效率和患者等待时间的组合。