Purpose: The Ontario Ministry of Health and Long Term Care has established wait time targets for cancer patients who are in need of radiation therapy. We investigated methods to reduce wait times and improve efficiency in a radiation oncology clinic.
Method: We developed a discrete event simulation model using Simul8. The model includes a process flow chart that is representative of a typical patient encounter at a tertiary cancer centre, from referral to radiation oncology to the point of treatment. The model tracks patients routing through all important process steps including consultation, dosimetry, CT simulation, and treatment. The distribution of referrals and patient demographic factors are based on data from 2009 patient tracking records. Event durations were obtained from scheduling data and interviews with radiation therapists. Schedules for treatment and the time required by dosimetry and physics were obtained from in-house software tracking system. We used the model to investigate a number of process flow improvements including changes to physician and technician schedules and increases in the number of staff hours available. Wait times for each process configuration were estimated using the simulation model.
Result: The historical data of patients treated in 2009 shows an average wait time from referral to treatment date to first treatment of 19.545 days. The model was simulated for 365 days, accounting for weekends and holidays, and was warmed up for a period of 5 years. Adding an extra dosimetrist does not make a significant difference in a patient’s wait time. Adding an extra day of new patient consults reduces a patient’s wait time by 3%. Increasing the amount of time devoted by radiation oncologists to plan treatments by 0.5 days decreases a patient’s wait time by an average of 9%. Changing the treatment planning and contouring done by radiation oncologists, from designated planning slots to planning as needed anytime during the work week, does not significantly affect a patient’s wait time for treatment.
Conclusion: This model is helping to identify areas of improvement in the current system, to reduce patients’ wait times for radiotherapy and to make more efficient use of resources. In an environment of soaring health care expenditure and rising incidence of cancer, this sort of planning process will be valuable in obtaining optimal value for money.
Candidate for the Lee B. Lusted Student Prize Competition