Preventing and Mitigating Radiology System Failures: A Guide to Disaster Planning
Bibliographic record
Abstract
Disaster planning is a core facet of modern health care practice. Owing to complex infrastructure requirements, radiology departments are vulnerable to system failures that may occur in isolation or during a disaster event when the urgency for and volume of imaging examinations increases. Planning for systems failures helps ensure continuity of service provision and patient care during an adverse event. Hazards to which a radiology department is vulnerable can be identified by applying a systematic approach with recognized tools such as the Hazard, Risk, and Vulnerability Analysis. Potential critical weaknesses within the department are highlighted by the Failure Mode and Effects Analysis tool. Recognizing the potential latent conditions and active failures that may impact systems allows implementation of strategies to prevent failure or to build resilience and mitigate the effects if they happen. Inherent system resilience to an adverse event can be estimated, and the ability of a department to operate during a disaster and the subsequent recovery can be predicted. The main systems at risk in a radiology department are staff, structure, stuff (supplies and/or equipment), and software, although individual issues and solutions within these are department specific. When medical imaging or examination interpretation needs cannot be met in the radiology department, the use of portable imaging modalities and teleradiology can augment the disaster response. All phases of disaster response planning should consider both sustaining operations and the transition back to normal function. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.033 |
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".