Improved Techniques for Optimizing Hospital Critical Care Operation

摘要

A large regional hospital in northeastern Texas is experiencing issues with overcrowding due to high waiting times for critical care treatment. This is caused by numerous reasons, including but not limited to nonoptimal clinical staff scheduling, lack of clinical resources, and fluctuating peaks in patients arriving. Overcrowding, long patients wait times, and less than optimal treatment times are all common challenges that large hospitals encounter daily. An integrated multifaceted decision support tool is presented to improve the hospital critical care efficiency and cost-savings. The decision support tool consists of three integrated techniques; (1) data analytics utilizing MINITAB software, (2) a clinician staff scheduling optimization technique utilizing LINGO mathematical programming software, and (3) simulation of data analytics and clinical staff optimization results utilizing STELLA system dynamics software with build-in closed loop feedback control mechanisms. With three months' critical care raw data from the large hospital, the integrated decision support tool is employed to evaluate the critical care service delivery operational performance. The unified decision support tool for improving the hospital's critical care operation has offered a 23.2% critical care staffing cost reduction and 6% patients out improvement.