Documentation and investigation of missing health care equipment: The need to safeguard high priced devices in health care institutions
Bibliographic record
Abstract
Objective: Missing medical equipment in health care settings decrease productivity, increase spending to replace losses, and potentially endanger patient safety. By documenting and investigating the causes of these incidents, strategies to prevent future occurrences can be developed.Methods: As an example of this approach, we describe the inadvertent disposal of an expensive medical device during a randomized controlled trial (RCT) conducted within a medical facility. The incident was carefully documented and investigated shortly after it occurred. This information was used to develop targeted interventions to prevent further occurrences.Results: The device was a mattress overlay connected to a computer monitor that generated a continuous pressure image for use by nursing staff in the prevention of pressure injuries. An Environmental Services staff member disposed of one of these devices when the room of an enrolled patient was cleaned following their transfer to another unit. Miscommunication (or a misunderstanding of communicated information) and lack of awareness were identified as the main causes of this error.Discussion: By using the loss as a learning opportunity, the investigation of the incident led to strategies for preventing future occurrences. These included frequent training sessions for staff and improvements in signage. A detailed, factual and timely investigation of the events around the loss of armamentarium coupled with analysis on how to prevent future occurrences should be considered for all incidents involving high cost equipment.Conclusions: A standardized, non-judgmental approach to documenting and investigating the causes of costly equipment loss can lead to strategies for improved asset management and the prevention of further incidents of this nature.
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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 | 0.140 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".