Enhancing efficiency and workflow in oncology outpatient services through simulation-based optimization

Abstract Outpatient chemotherapy clinics face rising demand and growing patient dissatisfaction, creating pressures to improve efficiency while sustaining quality care. This study uses an agent-based simulation of a National Cancer Institute (NCI)-designated cancer center’s infusion clinic to evaluate the effectiveness of three proposed operational changes: setting up an on-site laboratory, adapting nurse staffing based on anticipated demand increases, and balancing appointment scheduling via simulation optimization. The model, calibrated with historical data, tracks patient visit length and nurse utilization. Our study shows that combining an on-site lab with optimized scheduling reduces average visit length by about 75–90 minutes (approximately 30%) under various demand levels, smoothing workflow peaks. The study also shows that modest increases in nursing staff ensure timely patient throughput with 7–10% decreases in nurse utilization. Joint implementation of these operational changes outperforms individual changes, highlighting important synergies and the value of an integrated operations strategy. Overall, the study demonstrates a generalizable simulation-based framework to guide oncology process redesign and strategic decisions such as capacity expansion or facility relocation.