A mental workload based patient scheduling model for an oncology clinic

Abstract

The healthcare systems in the United States have faced competing challenges such as reducing costs and improving outcomes. Currently, the United States healthcare system is considered the most expensive in the world; 53% per capita more than the second-highest country. This study was focused on increasing resource productivity and efficiency in the healthcare system specifically at Bozeman Deaconess Cancer Center (BDCC) taking into consideration mental workload. The demand of the center has increased in approximately 16% each year since 2011. The BDCC strategic objectives are to improve the distribution and supply of resources, to maximize service coverage, to minimize waiting time of patients, and maximize service capacity. This research measured and validated mental workload in the infusion area of BDCC using two perceptual tools, NASA-TLX and SWAT, as well physiological responses. The purpose is to balance patient appointment and increase resource utilization. This study took into consideration the balance of human resource workload as a main part of the proposed model rather than only a mathematical solution balancing the capacity of the human resources without overloading them. A mathematical model was be developed and tested through a discrete event simulation to validate and explore the feasibility of the scheduling polices. In conclusion this thesis was able to successfully build a patient scheduling model considering nurses workload. It was proved that the model balanced patient appointments through the day by leveling the workload of nurses and pharmacists. Sensitivity analysis showed that the patient demand of the center could be increased up to 40% in some instances without negatively impacting patient service. This research is one of the first of its kind to include mental workload as a mathematical constraint in a scheduling model.