NSF Award
1720726
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a significant increase in the efficiency of hospital operations. The project focuses on the sources of inefficiencies in the planning, staffing, coordination, and execution of surgical procedures. Preliminary analysis of hospital data shows that the throughput of perioperative units can be increased significantly with the same staffing while reducing the delays that patients experience and improving the working conditions of the personnel. Such improvements have the potential of saving upwards of $1B per year. 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 tasks' duration. The timing of those messages depends on the knowledge of the state of the system and a prediction about its future evolution. Delays in gathering information and incorrect predictions of the effect of actions result in reduced efficiency. The proposed innovation improves the selection and timing of messages.<br/><br/>The proposed project is based on a machine learning approach for the optimization of real-time messaging using actual hospital data. 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. The methodology is able to evaluate the complex cascading impact of scheduling decisions and to identify the best course of action when dealing with contingencies. Timely situation awareness will contribute to improved patient flow in the hospital.
