LAUSR

Purpose To analyze the total treatment time and waiting time for the patients in our radiation oncology department. Method Patient treatment and waiting times were analyzed for a total of 3937 appointments during the last 3 years in three linear accelerators (Agility, Synergy and Infinity). With Elekta’s MOSAIQ Radiation Oncology information system (version 2.60, IMPAC Medical Systems), all patient information is collected and accessible, so you can create exhaustive reports. For this study a report including arrival time, wait time, treatment time and total time in the department was created for all radiotherapy treatments. Agility and Synergy are located in the same site, but Infinity is in a satellite location. Agility machine is dedicated mostly to VMAT treatments, meanwhile Synergy carry on only 3D treatments and Infinity performs VMAT as well as 3D treatments. Results Medium total treatment time was 15.1 min; median was 13.9 min. On average, patients arrive 30.5 min before the scheduled time. Therefore mean total time in the department was 45.6 min. In addition, for each machine, results were as follow: Agility: 1214 appointments. Average (minutes): early arrival 44.7, total time 45.3, wait time 30.6 and treatment time 14.8. Only 9.6% of the appointments arrived late. Synergy: 1541 appointments. Average (minutes): early arrival 36.8, total time 45.5, wait time 29.8 and treatment time 15.7. A 9.4% of the appointments arrived late. Infinity: 1182 appointments. Average (minutes): early arrival 22.0, total time 46.1, wait time 31.3 and treatment time 14.8. A 21.9% of the appointments arrived late. Conclusions In summary, patient waiting time and total time are very similar for the three machines. Despite the fact all VMAT treatments have a daily cone beam CT for patient positioning; medium treatment time for Agility is similar to the other machines which perform less VMAT treatments. Furthermore, many patients depend on public/private transport to arrive to their appointments; Infinity machine has the higher rate of late appointments, which could be related with a problem in the transport system. Therefore these results not only provide feedback but also could allow detecting problems and consequently improving waiting times for the patients.