Validating FMEA output against incident learning data: A study in stereotactic body radiation therapy

• Published in: Medical physics (Lancaster) 2015-06, Vol.42 (6Part1), p.2777-2785
• Main Authors: Yang, F., Cao, N., Young, L., Howard, J., Logan, W., Arbuckle, T., Sponseller, P., Korssjoen, T., Meyer, J., Ford, E.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; cancer; Databases, Factual; Dosimetry; Equipment Failure Analysis; FMEA; Humans; incident learning; Machine Learning; Medical physicists; Medical treatment planning; patient safety; Quality assurance; Quality assurance in radiotherapy; quality control; Radiation monitoring, control, and safety; Radiation safety; radiation therapy; Radiation treatment; Radiosurgery - methods; Radiotherapy Planning, Computer-Assisted - methods; risk analysis; Risk Assessment; safety; SBRT planning; Stereotactic radiosurgery; Treatment planning
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4919440

Purpose: Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. Methods: FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. Results: FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near‐miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). Conclusions: FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high‐severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.