Failure Mode and Effects Analysis: A Tool for Identifying Risk in Community Pharmacies
Bibliographic record
Abstract
CANADIAN HEALTH CARE LEADERS HAVE BEGUN TO LOOK AT SAFE practices in other industries to identify those with applicability to health care. A key characteristic of high-reliability industries, such as nuclear power, aviation, automobile manufacturing, and chemical processing, is acceptance of the fact that errors will occur, that the impact of errors can be devastating, and that efforts should be made to discover system weaknesses before harm occurs. A tool that has been a cornerstone of safety efforts in these organizations is a proactive risk assessment process called failure mode and effects analysis (FMEA). Using FMEA, multidisciplinary teams first identify potential failures and their effects, and then develop strategies for improvement. FMEA focuses on how and when a system will fail, not if it will fail. The US Veterans Affairs (VA) National Center for Patient Safety has developed an FMEA model for health care environments called Healthcare Failure Mode and Effect Analysis (HFMEA). 1 As part of its role in the Canadian Medication Incident Reporting and Prevention System, the Institute for Safe Medication Practices Canada (ISMP Canada) has adapted the VA model to develop a similar FMEA framework for use in Canada.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".