Clinical observations and a Healthcare Failure Mode and Effect Analysis to identify vulnerabilities in the security and accounting of medications in Ontario hospitals: a study protocol
Bibliographic record
Abstract
INTRODUCTION: An increasing number of opioids and other controlled substances are being stolen from healthcare facilities, diverting medications from their intended medical use to be used or sold illicitly. Many incidents of medication loss from Canadian hospitals are reported as unexplained losses. Together, this suggests not only that vulnerabilities for diversion exist within current medication-use processes (MUPs), but that hospitals lack robust mechanisms to accurately track and account for discrepancies and loss in inventory. There is a paucity of primary research investigating vulnerabilities in the security and accounting of medications across hospital processes. The purpose of this study is to map hospital MUPs, systematically identify risks for diversion or unintentional loss and proactively assess opportunities for improvements to medication accounting and security. METHODS AND ANALYSIS: We will conduct human factors-informed clinical observations and a Healthcare Failure Mode and Effect Analysis (HFMEA). We will observe hospital personnel in the intensive care unit, emergency department and inpatient pharmacy in two hospitals in Ontario, Canada. Observations will capture how participants complete tasks, as well as gather contextual information about the environment, technologies and processes. A multidisciplinary team will complete an HFMEA to map process flow diagrams for the MUPs in the observed clinical units, identify and prioritise potential methods of medication loss (failure modes) and describe mechanisms or actions to prevent, detect and trace medication loss. ETHICS AND DISSEMINATION: We received province-wide research ethics approval via Clinical Trials Ontario Streamlined Research Review System, and site-specific approvals from each participating hospital. The results from this study will be presented at conferences and meetings, as well as published in peer-reviewed journals. The findings will be shared with hospitals; professional, regulatory and accreditation organisations; patient safety and healthcare quality organisations and equipment and drug manufacturers.
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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 | 0.052 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".