V Babashov1, I Aivas2, M A Begen3, J Q Cao2, G Rodrigues4, D D'Souza2, M Lock2, G S Zaric5. 1. Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada. 2. Department of Radiation Oncology, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada. 3. Ivey Business School, Western University, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada. Electronic address: mbegen@ivey.uwo.ca. 4. Department of Radiation Oncology, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada. 5. Ivey Business School, Western University, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
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.
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.
Authors: Gustavo Teodoro Gabriel; Afonso Teberga Campos; Aline de Lima Magacho; Lucas Cavallieri Segismondi; Flávio Fraga Vilela; José Antonio de Queiroz; José Arnaldo Barra Montevechi Journal: PeerJ Comput Sci Date: 2020-08-10
Authors: Bruno Vieira; Derya Demirtas; Jeroen B van de Kamer; Erwin W Hans; Wim van Harten Journal: BMC Med Inform Decis Mak Date: 2019-10-24 Impact factor: 2.796