Vulnerabilities for Drug Diversion in the Handling, Data Entry, and Verification Tasks of 2 Inpatient Hospital Pharmacies: Clinical Observations and Healthcare Failure Mode and Effect Analysis
Bibliographic record
Abstract
Objectives Inpatient hospital pharmacies have a central role in managing controlled substances (CS) throughout the hospital medication use process (MUP). Our objectives were to identify vulnerabilities for diversion in the MUPs of 2 inpatient pharmacies, explore differences between the sites, and characterize the types of vulnerabilities identified. Methods We conducted clinical observations in 2 pharmacies to map their MUPs and performed a healthcare failure mode and effect analysis to proactively identify (1) the critical failure modes (CFMs) that make them vulnerable to diversion and (2) the controls that prevent, mitigate, or enhance the detectability of CFMs. Results We conducted 99 hours of observations between May–June and September–October 2018. We observed 36 pharmacy technicians, 4 pharmacists, and 1 clerk as they conducted tasks involving 4 processes common to both sites: procuring CS, receiving CS deliveries to the pharmacy, unit-dose packaging CS oral solids, and distributing CS to hospital units. The tasks and subtasks we mapped in the process flow diagrams led to the identification of 220 failure modes. Of these, 34 were deemed CFMs and were categorized as related to handling CS, data entry, or verification tasks. Three of the CFMs were unique to one site, given that the other site had a control for the CFM. Conclusions Multiple vulnerabilities for diversion exist in inpatient pharmacy processes. Our results provide some much needed detail about how specific vulnerabilities in MUP tasks and subtasks lead to an increased risk of diversion.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".