Identifying Vulnerabilities for Diversion of Controlled Substances to Inform The Safeguards Needed in Canadian Hospitals to Protect Patients and Healthcare Workers
Bibliographic record
Abstract
Introduction: Diversion or theft of controlled substances (CS) is a recognized problem affecting healthcare systems globally. Diversion from hospitals can occur through falsifying orders, tampering with medications, and pilfering stock. The impacts of diversion are serious, causing severe harm to patients, healthcare workers, hospitals, and the public. Selecting effective interventions for safeguarding against diversion cannot occur without first having a thorough understanding of how the hospital system is susceptible to diversion. Aims: To establish a detailed understanding of how hospital medication-use processes are vulnerable to CS diversion, characterize the vulnerabilities, and develop a framework to conceptualize how system factors set the stage or shape processes for diversion to take place. Study Design/Approach: Guided by the System’s Engineering Initiative for Patient Safety (SEIPS) Human Factors model, I used a multimethod study design comprised of observations that informed a Healthcare Failure Mode and Effect Analysis (HFMEA) to identify critical failure modes (CFMs) in two Ontario hospitals in the inpatient pharmacy (Study 1), emergency department (ED) (Study 2), and intensive care unit (ICU) (Study 3). The prospective analysis was followed by a framework analysis to build a conceptual framework for diversion. Results: Study 1 demonstrated that CFMs provide detailed descriptions of vulnerabilities which exist in every medication-use process involving CS. Studies 2 and 3 built on these finding and showed that all CFMs stemmed in part from person-related factors (e.g., intentional actions to falsify documentation), but were always also precipitated by technology-, organization-, environment-, or task-related deficits in how healthcare systems are designed and organized. I developed a novel framework based on findings from Studies 1-3 to conceptualize the relationship between system factors and how they interact and coalesce to enable pilfering and/or forgery which leads to diversion-related harms. Conclusion: My thesis findings further our understanding of diversion as a systems issue, whereby vulnerabilities stem from failures not only by individual actions, but in the interaction between processes, technologies, environments, and organization factors. My framework can be used to inform broader efforts to develop guidelines and technological solutions for Canadian hospitals.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".