Applying Failure Mode Effects Analysis (FMEA) to Improve Choking Risk Prevention in a Mental Health Setting: Analysis Outcomes and Lessons Learned on Human Factors Collaboration
Bibliographic record
Abstract
This paper describes the collaborative work performed as part of a patient safety and quality improvement choking risk prevention initiative in a specialty mental health hospital in Ontario, Canada. In 2021, Ontario Shores Centre for Mental Health Sciences (Ontario Shores), in collaboration with the Healthcare Insurance Reciprocal of Canada (HIROC), conducted a Failure Modes and Effects Analysis (FMEA) to identify potential failure modes for their choking risk prevention process. “Failure modes” refer to states in a process that have the potential for unintended consequences. The interdisciplinary project team developed and validated a current-state process map, through which identified all opportunities for process improvement. A thematic analysis of the barriers revealed 14 distinct failure modes, each of which were rated along three scales (Severity, Occurrence, and Detectability) to form a ranked list based on Risk Priority Number. As part of a prospective analysis, several system-based and people-based mitigations were generated for each of the failure modes. As a result of the FMEA, Ontario Shores developed, and is in the process of, implementing a choking risk prevention and risk mitigation strategies action plan. In addition, the authors offer some reflections on the collaborative work between the two organizations, in recognition of the opportunity for healthcare organizations to benefit from human factors expertise and principles of applied safety science, usability engineering, and user-centered design.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".