Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre

Annals of Operations Research https://doi.org/10.1007/s10479-023-05168-x ORIGINAL RESEARCH Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre Masoud Eshghali1 · Devika Kannan2,3 · Navid Salmanzadeh-Meydani2,4 · Amir Mohammad Esmaieeli Sikaroudi5 Accepted: 4 January 2023 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 Abstract As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient’s higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital’s current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule. Keywords Elective and emergency patients · Operating theater scheduling · Rescheduling · Operating room planning · Machine learning B Devika Kannan 1 Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA 2 Centre for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, 5230 Odense M, Denmark 3 School of Business, Woxsen University, Sadasivpet, Telangana, India 4 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran 5 Department of Computer Science, University of Arizona, Tucson, AZ 85721, USA 123 Annals of Operations Research 1 Introduction Health care systems constitute one of the most critical service sectors in any society. According to the World Health Organization, the average global health expenditure per capita has more than doubled (from US$480 in 2000 to US$1111 in 2018). Moreover, in March 2020, the World Health Organization proclaimed the coronavirus disease (COVID-19), a global pandemic (Ali & Kannan, 2022; Fattahi et al., 2023; Sohrabi et al., 2020; Ferreira et al., 2022). As a result, the need for hospital resource such as intensive care units (ICU) and wards has increased significantly over the world. In addition, aging phenomena put emphasis on the importance of health care systems, signaling its growing need for attention and planning. Clearly, hospitals play a critical role in health systems and operating rooms (ORs) are the most expensive services as they deploy a great number of precious resources such as surgeons, staff, and equipment (Macario, 2010; van Oostrum et al., 2008). In addition, ORs are the foremost source of annual income and have a direct effect on patient safety (Liu et al., 2011). Then, it is conceivable that any improvements in OR productivity could lead to a more productive hospital and better patient satisfaction (Durán et al., 2017). Accordingly, integrated planning and scheduling can be the capstone to managing and improving the efficiency of hospitals. In the real world, patients, after being operated in ORs, frequently stay in downstream units, such as the recovery unit or post-anesthesia care unit (PACU), ICU, CCU, and wards, which are also limited (Fügener et al., 2014). As a result, considering just ORs capacity does not result in practical scheduling for patients, since the lack of enough downstream units keeps patients from moving forward, significantly decreasing ORs utilization. For instance, when there are not enough available beds in recovery units for incoming patients after surgery, some of them must remain in ORs until one recovery bed becomes available. Elective patients can stay on a waiting list and be scheduled in advance. However, emergency patients must be operated on as soon as possible. Therefore, the arrival of emergency patients may lead to delays in elective patients surgeries because of their priority. A great number of hospitals deal with emergency surgeries problem by reserving some operating theater (OT) capacity. There are three ways to do it (Van Riet & Demeulemeester, 2015): (1) reserving some ORs’ capacity (reserved-OR), (2) use elective patients OR (shared-OR) (3) a combination of (1) and (2) (van Essen et al., 2012). In situation (1), emergency patients are operated on in the reserved OR, but they should wait for the reserved OR to become available if the reserved ORs are busy. In this situation, the utilization of ORs is chronically low. In situation (2), emergency patients are operated on in the first available OR. In this strategy, although the utilization of OR is reasonable, the high rate of elective patient surgery cancelation is undeniable. Situation (3) is a combination of situations (1) and (2), which means that the emergency patient is operated on at once if the reserved OR is available. Otherwise, they have to wait until the reserved ORs or one of the elective ORs becomes available. van Veen-Berkx et al. (2016) demonstrated that strategy 1 works better than strategy 2 in terms of utilization, overtime, and case cancellations. Due to the inherent randomness in emergency patient arrival, historical data and geographical information systems are applied to obtain the emergency patient arrival time. Therefore, proper forecasting of this process can be used as a basis for management to better allocate OTs resources. Machine learning is an artificial intelligence subtype that employs algorithms, learning large quantities of data iteratively but not explicitly programmed to do so (Obermeyer & Emanuel, 2016). They can extract schematics from various data sources, explain 123 Annals of Operations Research them, and create a predictive model (Rajkomar et al., 2019). Machine learning models can be a proper method for forecasting emergency patients’ arrivals time and surgery duration. The contributions of this paper are summarize (...truncated)