Using ISMP Canada's Framework for Failure Mode and Effects Analysis: A Tale of Two FMEAs
Bibliographic record
Abstract
Patient safety concerns in healthcare are not new or unexpected, and one goal of all healthcare organizations is to provide the safest possible care for patients and their families. With that goal in mind, Annapolis Valley Health, a rural district health authority in Nova Scotia, identified the need to develop expertise in the use of failure mode and effects analysis (FMEA) as a tool to promote quality processes within the organization. Staff members were aware of the value of this type of analysis but also recognized that real learning would best be achieved through completing an FMEA of an existing process or situation, rather than through a simulation or staff training. Annapolis Valley Health identified two high-risk situations requiring attention: transcription of medication orders for in-patients and overcrowding in the emergency department. The Institute for Safe Medication Practices Canada provided training and support to two staff teams and visited the organization eight months later for an update on progress. This article chronicles the journey of Annapolis Valley Health to improve patient safety through the application of FMEA to two high-risk processes for one of its hospital sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".