Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning). Issue 6 (June 2017)

• Record Type: Journal Article
• Authors: Babashov, V.; Aivas, I.; Begen, M.A.; Cao, J.Q.; Rodrigues, G.; D'Souza, D.; Lock, M.; Zaric, G.S.
• Is Part Of: Clinical oncology. Volume 29:Issue 6(2017)
• Page Start: 385
• Page End: 391
• Publication Date: 2017-06
• Subjects: Discrete-event simulation -- modelling -- radiotherapy -- waiting time; Oncology -- Periodicals; Tumors -- Periodicals; Cancer -- Treatment -- Periodicals; Radiotherapy -- Periodicals; Neoplasms -- Periodicals; Cancer -- Radiotherapy; Cancer -- Treatment; Oncology; Medical radiology; Radiotherapy; Tumors; Electronic journals; Periodicals; 616.994
• DOI: 10.1016/j.clon.2017.01.039
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: Aims: We analysed the radiotherapy planning process at the London Regional Cancer Program to determine the bottlenecks and to quantify the effect of specific resource levels with the goal of reducing waiting times. Materials and methods: We developed a discrete-event simulation model of a patient's journey from the point of referral to a radiation oncologist to the start of radiotherapy, considering the sequential steps and resources of the treatment planning process. We measured the effect of several resource changes on the ready-to-treat to treatment (RTTT) waiting time and on the percentage treated within a 14 calendar day target. Results: Increasing the number of dosimetrists by one reduced the mean RTTT by 6.55%, leading to 84.92% of patients being treated within the 14 calendar day target. Adding one more oncologist decreased the mean RTTT from 10.83 to 10.55 days, whereas a 15% increase in arriving patients increased the waiting time by 22.53%. The model was relatively robust to the changes in quantity of other resources. Conclusions: Our model identified sensitive and non-sensitive system parameters. A similar approach could be applied by other cancer programmes, using their respective data and individualised adjustments, which may be beneficial in making the most effective use of limited resources. Highlights: Modelling of an entire radiotherapy planning process at a cancer treatment facility. Bottleneck analysis and resource level impact quantification to reduce waiting times. Additional dosimetrist improved the mean waiting time by 6.5%. Removing one physicist dramatically worsened the waiting time. Applicability and transportability of the model to other facilities.