SU-F-T-247: Collision Risks in a Modern Radiation Oncology Department: An Efficient Approach to Failure Modes and Effects Analysis.

PURPOSE: Collisions between treatment equipment and patients are potentially catastrophic. Modern technology now commonly involves automated remote motion during imaging and treatment, yet a systematic assessment to identify and mitigate collision risks has yet to be performed. Failure modes and effects analysis (FMEA) is a method of risk assessment that has been increasingly used in healthcare, yet can be resource intensive. This work presents an efficient approach to FMEA to identify collision risks and implement practical interventions within a modern radiation therapy department.

METHODS: Potential collisions (e.g. failure modes) were assessed for all treatment and simulation rooms by teams consisting of physicists, therapists, and radiation oncologists. Failure modes were grouped into classes according to similar characteristics. A single group meeting was held to identify implementable interventions for the highest priority classes of failure modes.

RESULTS: A total of 60 unique failure modes were identified by 6 different teams of physicists, therapists, and radiation oncologists. Failure modes were grouped into four main classes: specific patient setups, automated equipment motion, manual equipment motion, and actions in QA or service mode. Two of these classes, unusual patient setups and automated machine motion, were identified as being high priority in terms severity of consequence and addressability by interventions. The two highest risk classes consisted of 33 failure modes (55% of the total). In a single one hour group meeting, 6 interventions were identified. Those interventions addressed 100% of the high risk classes of failure modes (55% of all failure modes identified).

CONCLUSION: A class-based approach to FMEA was developed to efficiently identify collision risks and implement interventions in a modern radiation oncology department. Failure modes and interventions will be listed, and a comparison of this approach against traditional FMEA methods will be presented.