ESTRO 2025 - Abstract Book

S2295

Interdisciplinary – Health economics & health services research

ESTRO 2025

2099

Digital Poster Evaluating the impact of equipment downtime on patient care and economic efficiency in radiotherapy departments Antonio V. Merola 1 , Giuseppe Bonfitto 2 , Giovannella Salvadori 1 , Moreno Zanardo 3 , Nadia Di Muzio 1 , Diego Catania 2 1 Radiotherapy, IRCCS Ospedale San Raffaele, Milano, Italy. 2 Radiology, IRCCS Ospedale San Raffaele, Milano, Italy. 3 Radiology, IRCCS Policlinico San Donato, Milano, Italy Purpose/Objective: To evaluate the organizational impact of downtime in a radiotherapy department by examining how equipment interruptions affect treatment delays, patient waiting times, and overall productivity. The second end point also aims to estimate potential economic losses and identify primary factors contributing to operational disruptions. Material/Methods: A retrospective analysis was conducted from 2019 to 2024 at IRCCS Ospedale San Raffaele, focusing on three types of radiation therapy equipment, a total of five devices. Clinical engineering reports were reviewed to assess “unscheduled” downtime, analyzing failure and maintenance data to determine patterns and causes of interruptions. The analysis included quantification of potentially lost therapy sessions and calculating associated economic impacts.

Results:

A total of 1,538 hours of inactivity were recorded, which in some equipment accounted for approximately 4% of annual inactivity. This translated, for the entire equipment fleet, into 4,098 potentially lost therapy sessions. The estimated potential economic loss due to downtime over the study period was an average of 11,116,708€. Key factors influencing downtime included equipment type, parts prone to wear and tear—particularly the Multileaf Collimator assembly, responsible for up to 53% of outage hours—and facility management inefficiency, which contributed up to 34% of the total downtime hours. Additionally, the degree of equipment complexity and the frequency of required upgrades played a significant role in the variation of downtime across devices.