The utilization of patients' information to improve the performance of radiotherapy centers: A data-driven approach. (October 2022)

• Record Type: Journal Article
• Authors: Moradi, Shahryar; Najafi, Mehdi; Mesgari, Sara; Zolfagharinia, Hossein
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Patient appointment scheduling -- Data-driven optimization -- Mixed integer linear programming -- Patients' unpunctuality -- Radiotherapy clinics -- Data-mining techniques; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108547
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Data-mining techniques are used to predict patients' unpunctuality. We present a double-stage method to prioritize patients for receiving treatments. Patients' gradual health improvement and treatment prolongation are addressed. We propose a data-driven approach to schedule a series of appointments for patients. The proposed approach is applied to a real-world radiotherapy center. Abstract: The high demand for radiotherapy services, combined with the limited capacity of available resources, patient unpunctuality, and series of appointments, makes Patient Appointment Scheduling (PAS) in radiotherapy centers very challenging. Although most centers use a First-Come-First-Serve (FCFS) policy for appointment scheduling, this approach does not consider patients' behaviors, and consequently, it performs poorly. This type of inappropriate scheduling usually leads to inefficiency at the center and/or patient dissatisfaction. This study provides a data-driven approach to patient appointment scheduling to deal with the challenges mentioned above, and it considers patients' histories of unpunctuality, including the amount of time they are usually late and whether they will miss the appointment. This study first employs data-mining techniques to predict patients' behaviors and then incorporates them into PAS. In addition, it presents a novel double-stage prioritization method that considers both patients' gradual health improvement during the treatment process and any treatment prolongation that occurs. These predictions and priorities are then utilized in the developed Mixed Integer Linear Programming (MILP) model to determine the optimal sequence of patients for treatment. The developed model also considers no-show patients and rearranges their makeup session(s) to meet their service requirements. Lastly, the proposed approach is applied to two business configurations (i.e., single-server and multi-server radiotherapy centers) to highlight its advantages and demonstrate its performance against the current policy. The results reveal that employing the developed model improves the center's total cost by up to 30%.