An integrared approach to determine hospital outpatient staffing needs

Prolonged waiting time in medical settings can cause dissatisfaction with the medical care, poor compliance with health care providers' recommendations and deterioration of severely ill patients. Numerous researchers and hospital managers are interested in how to solve this problem by utilising human resources optimally. However, few studies that provide integrated approaches for solving this queue problem have been proposed or implemented. Therefore, in this study we use a decision support system to develop an integrated approach. The approach incorporates a genetic algorithm into queuing network theory to formulate a mathematical program, which provides users with a systematic method to efficiently obtain the optimal or near-optimal staffing plan. The proposed approach was implemented in a real-world case situation to determine the average outpatient waiting time, the traffic intensity and the optimal number of staff at each service facility. Although the findings and the recommendations of this study are specific to the proposed case the study provides an example, demonstrating that the proposed approach is a valuable tool for analysing the waiting problem and designing the staff level in similar facilities.