Diversion of Controlled Drugs in Hospitals: A Scoping Review of Contributors and Safeguards
Bibliographic record
Abstract
Drug losses and theft from the healthcare system are accelerating; hospitals are pressured to implement safeguards to prevent drug diversion. Thus far, no reviews summarize all known risks and potential safeguards for hospital diversion. Past incidents of hospital drug diversion have impacted patient and staff safety, increased hospital costs, and resulted in infectious disease outbreaks. We searched MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and Web of Science databases and the gray literature for articles published between January 2005 and June 2018. Articles were included if they focused on hospital settings and discussed either: (1) drug security or accounting practices (any drug) or (2) medication errors, healthcare worker substance use disorder, or incident reports (only with reference to controlled drugs). We included 312 articles and extracted four categories of data: (1) article characteristics (eg, author location), (2) article focus (eg, clinical areas discussed), (3) contributors to diversion (eg, factors enabling drug theft), and (4) diversion safeguards. Literature reveals a large number of contributors to drug diversion in all stages of the medication-use process. All health professions and clinical units are at risk. This review provides insights into known methods of diversion and the safeguards hospitals must consider to prevent them. Careful configuration of healthcare technologies and processes in the hospital environment can reduce the opportunity for diversion. These system-based strategies broaden the response to diversion beyond that of individual accountability. Further evidence is urgently needed to address the vulnerabilities outlined in this review and prevent harm.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".