Adverse Incident Reporting and Staff Vigilance Leads to Early Identification of Medical Equipment Problems
Bibliographic record
Abstract
Adverse Incident reporting has become a standard of practice at SickKids. When staff experience an adverse event they are encouraged to file an Incident Report. The Incident report system generates e-mails to people who need to be aware of the incident. When medical equipment is involved, Medical Engineering receives the Incident Report. Staff are asked to clearly identify the device that and send it to Medical Engineering. In August 2015, we received two incidents where a Syringe Module stopped working with a channel error message. The incidents happened in the Paediatric Intensive Care Unit (PICU). Testing of the Syringe module indicated that the module was working properly. A third incident occurred in the same unit. The QA leader for the PICU contacted Medical Engineering's Team leader to identify possible trends. Upon investigation, it was determined that the Channel Error was only being experienced in the PICU. We received a fourth incident with the same reported problem. Biomedical Engineering Technologists performed extended testing by simulating similar infusion as reported by nurse. This time the Technologists did duplicate the Channel Error. Upon opening the Syringe Module's casing, the technologists discovered some traces of oxidation/rust in the drive mechanism. This was suspected to be the cause of the Syringe Module failure. During the month of August we continued to receive Incidents with the same Syringe Module issue. The presentation will cover our findings of oxidation on the drive train, the company’s response and the action plan to inspect and correct 800 Syringe modules.
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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 | 0.021 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".