Modeling and simulation of oncology clinic operations in discrete event system specification. (February 2018)

• Record Type: Journal Article
• Authors: Alvarado, Michelle M; Cotton, Tanisha G; Ntaimo, Lewis; Pérez, Eduardo; Carpentier, William R
• Is Part Of: Simulation. Volume 94:Number 2(2018)
• Page Start: 105
• Page End: 121
• Publication Date: 2018-02
• Subjects: Discrete event simulation -- system modeling -- healthcare -- oncology clinic operations -- chemotherapy scheduling -- discrete event system specification; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717708246
• Languages: English
• ISSNs: 0037-5497

Abstract

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy under limited resources, such as nurses and chemotherapy chairs. Chemotherapy is a cancer treatment method that is administered orally or intravenously at an outpatient oncology clinic. Chemotherapy patients require a treatment regimen, which is a series of appointments over several weeks or months prescribed by the oncologist. The timing of these appointments is critical to the effectiveness of the chemotherapy treatment on cancer. This motivates the need for new methods for making efficient appointment schedules and for assessing clinic operation performance from both patient and management perspectives. This work uses a classic modeling approach based on systems theory to develop a discrete event system specification (DEVS) simulation model for oncology clinic operations called DEVS-CHEMO. DEVS-CHEMO is configurable to any oncology clinic and provides several capabilities for oncology clinic managers. For example, it can simulate scheduling of chemotherapy patients, clinic resources, and the arrival process of the patients to the clinic on the day of their appointment. This model simulates oncology clinic operations as patients receive chemotherapy treatments and thus allows for assessing scheduling algorithms using both patient and management perspectives. DEVS-CHEMO has been tested and validated using historical data from a real outpatient oncology clinic and the simulation results reported in this paper provide several insights regarding oncology clinic operations management.