Please cite this article in press as: A. Huggins, D. Claudio, A mental workload based patient scheduling model for a Cancer Clinic, Operations Research for Health Care (2018), https://doi.org/10.1016/j.orhc.2018.10.003. Operations Research for Health Care ( ) – Contents lists available at ScienceDirect Operations Research for Health Care journal homepage: www.elsevier.com/locate/orhc A mental workload based patient scheduling model for a Cancer Clinic Anali Huggins, David Claudio * Department of Mechanical and Industrial Engineering, Montana State University, Bozeman, MT 59717-3800, USA article info Article history: Received 11 October 2017 Received in revised form 7 August 2018 Accepted 18 October 2018 Available online xxxx Keywords: Workload NASA-TLX Optimization model Physiological responses abstract This study focused on increasing productivity and efficiency in a Cancer Clinic (CC) taking into consider- ation mental workload. The demand of the clinic has increased and the clinic recognized the importance of improving the distribution of the resources. Addressing these objectives have a positive impact in operations, however, it also requires managing the human elements of the system in an efficient way. Previous studies have considered human resources as a number representing a fix quantity of available entities without considering their mental capabilities. This research measured mental workload using a perceptual tool, NASA-TLX, as well as physiological responses. The purpose was to balance patient appointments and increase resource utilization while taking into consideration the balance of human workload as a constraint in the mathematical model. Mental workload was included to assure a balance in the capacity of the human resources without overloading them. The mathematical model was able to successfully build a patient scheduling model considering nurses’ workload. It was shown that the model balanced patient appointments throughout the day by leveling the workload of nurses. Sensitivity analysis showed that the patient demand of the center could be increased by up to 50% without negatively impacting patient service. © 2018 Published by Elsevier Ltd. 1. Introduction Optimization models have been used often to evaluate the efficiency of healthcare systems. For example, Van Houdenhoven et al. [1] were able to increase operation room (OR) utilization by applying mathematical algorithms. They also argued that mathe- matical algorithms were not enough; hence, they needed to lower the organizational barriers that were limiting departments to schedule surgeries in certain rooms. Similarly, Ozkarahan [2] used goal programming to allocate surgeries to ORs. Denton el al. [3] combined mathematical optimization with simulation to minimize wait times, idle times and overtime in an OR. Most of the articles found in the literature are focused either in ORs or emergency rooms (ER). For example, Reuter-Oppermann el al. [4] summarized several logistical problems arising from emer- gency medical services. They established connections between de- mand, response time, and workload which are typically considered separately in the literature. Baesler and Sepulveda [5,6] are among the few articles not related to ORs or ERs. They presented a case study on combining simulation and multi-objective optimization heuristics to target four objectives at a Cancer Center such as minimizing patient’s waiting time, maximizing chair utilization, minimizing closing time, and maximizing nurses’ utilization. They were able to increase inpatient throughput by 30% with the same * Corresponding author. E-mail address: david.claudio@montana.edu (D. Claudio). resources [5,6]. In fact, Swisher et al. [7] demonstrated that un- der certain conditions staffing reductions could be made without sacrificing patient throughput or increasing staff overtime. They experimented with several models with different patient mixes. They also showed that scheduling more of a certain type of patient (patients that require extensive physician interaction; longer ser- vice time) in the morning reduces employee overtime significantly. Harper and Gamlin [8] tested several different appointment schedules and showed how patient wait times can be signifi- cantly reduced through improved planning of the schedule and management engagement. Furthermore, Rohleder and Klassen [9] studied the use of rolling horizon appointment scheduling and considered two common management policies; Overload Rules (OLR) and Rule Delay (RD). The results showed that managers of appointment scheduling systems must carefully consider which measures are most important to them since the best choices of OLR and RD vary substantially by measure. Ahmadi-Javid et al. [10] concluded that in the last decade outpatient appointment systems have become more complex and more difficult to solve partic- ularly when integrating environmental factors in the model. Lin et al. [11] developed a model incorporating nurse fatigue as a way to building a nurse scheduling system. They used survey-based and circadian function-based fatigue models. The objective was to obtain a Pareto-optimal schedule where the nurse fatigue levels are reduced according to the nurse preference. In this research we implemented different techniques to measure staff workload taking into consideration mental and physical workload and in- tegrated them into a mathematical model. We then took into https://doi.org/10.1016/j.orhc.2018.10.003 2211-6923/© 2018 Published by Elsevier Ltd.