Modeling patient preference in an operating room scheduling problem

When a patient needs plastic surgery and there are multiple available surgeons, the patient selects the surgeon based on different criteria. Accommodating patient preference while scheduling such surgeries is important as it is related to patient satisfaction. In this study, we propose a framework for integrating patient preference in an operating room (OR) scheduling problem. To model patient preference to a surgeon, we propose nine criteria: responsive and caring, reputation, professional experiences, communication skills, same ethnicity, same gender, age, same language, and online rating. Fuzzy TOPSIS (namely, Technique for Order of Preference by Similarity to Ideal Solution) is then employed to quantify patient preference to surgeons. The outcomes of fuzzy TOPSIS are then fed into a multi-objective mixed-integer linear programming (MILP) model to optimize daily surgery schedule. The proposed study is based on a real-life case study that was conducted in a plastic surgery department at a partner hospital. The computational results show that when patient preference to surgeon is considered, more than 70% of patients are assigned to their most preferred surgeons, and less than 5% are assigned to their least preferred surgeons. However, when patient preference is not considered, less than 20% of patients are assigned to most preferred surgeons, and the others are assigned to less preferred surgeons. When it comes to the total costs, the two scenarios results are similar. This concludes that the proposed framework is robust and able to increase patient satisfaction in OR scheduling without sacrificing the total OR operational costs.