The Application of Failure Modes and Effects Analysis Methodology to Intrathecal Drug Delivery for Pain Management
Bibliographic record
Abstract
OBJECTIVE: This study aimed to utilize failure modes and effects analysis (FMEA) to transform clinical insights into a risk mitigation plan for intrathecal (IT) drug delivery in pain management. METHODS: The FMEA methodology, which has been used for quality improvement, was adapted to assess risks (i.e., failure modes) associated with IT therapy. Ten experienced pain physicians scored 37 failure modes in the following categories: patient selection for therapy initiation (efficacy and safety concerns), patient safety during IT therapy, and product selection for IT therapy. Participants assigned severity, probability, and detection scores for each failure mode, from which a risk priority number (RPN) was calculated. Failure modes with the highest RPNs (i.e., most problematic) were discussed, and strategies were proposed to mitigate risks. RESULTS: Strategic discussions focused on 17 failure modes with the most severe outcomes, the highest probabilities of occurrence, and the most challenging detection. The topic of the highest-ranked failure mode (RPN = 144) was manufactured monotherapy versus compounded combination products. Addressing failure modes associated with appropriate patient and product selection was predicted to be clinically important for the success of IT therapy. CONCLUSIONS: The methodology of FMEA offers a systematic approach to prioritizing risks in a complex environment such as IT therapy. Unmet needs and information gaps are highlighted through the process. Risk mitigation and strategic planning to prevent and manage critical failure modes can contribute to therapeutic success.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".