Critical vulnerabilities for diversion of controlled substances in the emergency department: Observations and healthcare failure mode and effect analysis
Bibliographic record
Abstract
Objectives Drug theft by healthcare workers is a recognized problem in emergency departments (EDs) that can lead to patient, healthcare worker, and organization harm. Diversion takes various forms, including tampering with syringes, pilfering from waste containers and falsely documenting drug administration. Before implementing risk-mitigating interventions, we need a detailed understanding of the vulnerabilities in ED medication-use processes. This study sought to identify the critical failure modes (CFMs) within EDs that increase diversion risk and characterize the system factors contributing to CFMs. Methods Between June 2018 and February 2019, we conducted observations in two Ontario EDs. Observers recorded tasks carried out by nurses, pharmacists, and physicians. We performed a Healthcare Failure Mode and Effect Analysis, informed by the observation data, to proactively identify CFMs in the medication-use processes. Failure modes were coded for their effects on diversion risk and the contributing system factors. Results We identified 28 CFMs that increase diversion risk by enabling inappropriate access to controlled substances or compromising documentation. CFMs are multifactorial, stemming primarily from factors related to person (e.g., intent to divert) and tools/technology (e.g., limited automatic reconciliation of records), followed by organization (e.g., practices that diffuse accountability), environment (e.g., workspaces that obscure illicit behaviours), and task (e.g., unstructured processes leading to lapses). Conclusion The study findings inform opportunities to revise vulnerable processes and bolster safeguards, decreasing diversion risk and protecting patients and healthcare workers.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".