Poster — Thur Eve — 19: Risk assessment of clinical radiation processes using failure modes and effect analysis
Bibliographic record
Abstract
The aim of this work was to apply failure modes and effect analysis (FMEA) to assess risk in two radiation planning and treatment processes; our on-call (out-of-clinical hours) process and our tomotherapy process. The motivation was provided by analysis of 2506 adverse incidents reported over a 5 year period, the on-call process for giving rise to a higher than expected number of incidents and our tomotherapy process for the reverse. For the on-call scenario, three separate processes were analysed: our current process, our current process incorporating a software upgrade eliminating several planning steps and a fully integrated process in which the patient is imaged, planned and treated on a single platform (TomoTherapy Hi Art, Accuray Incorporated, Sunnyvale, CA). After construction of a detailed process map for each case, a multidisciplinary group identified potential failure modes for each process step, the effects of each failure and existing controls. Risk probability numbers were determined from severity, frequency of occurrence and detectability scores assigned to each failure mode according to a standard scale. The results were analysed to identify and prioritise feasible and effective process improvements. For the on-call process, our current workflow was identified as incurring the highest risk of the three processes analysed, demonstrating quantitatively the value of the software upgrade and providing a clear rationale for the associated expense. In summary, we have found FMEA to be a feasible tool for assessing relative risk in a clinical process. However, operational and resource issues must be considered separately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".