A proposed framework to improve the safety of medical devices in a Canadian hospital context
Bibliographic record
Abstract
PURPOSE: Medical devices are used to monitor, replace, or modify anatomy or physiological processes. They are important health care innovations that enable effective treatment using less invasive techniques, and they improve health care delivery and patient outcomes. Devices can also introduce risk of harm to patients. Our objective was to propose a surveillance system framework to improve the safety associated with the use of medical devices in a hospital. MATERIALS AND METHODS: The proposed medical device surveillance system incorporates multiple components to accurately document and assess the appropriate actions to reduce the risk of incidents, adverse events, and patient harm. The assumptions on which the framework is based are highlighted. The surveillance system was designed from the perspective of a tertiary teaching hospital that includes dedicated hospital staff whose mandate is to provide safe patient care to inpatients and outpatients and biomedical engineering services. RESULTS: The main components of the surveillance system would include an adverse medical device events database, a medical device/equipment library, education and training, and an open communication and feedback strategy. Close linkages among these components and with external medical device/equipment networks to the hospital must be established and maintained. A feedback mechanism on medical device-related incidents, as well as implementation and evaluation strategies for the surveillance system are described to ensure a seamless transition and a high satisfactory level among the hospital staff. The direct cost items of the proposed surveillance system for consideration, and its potential benefits are outlined. CONCLUSION: The effectiveness of the proposed medical device surveillance system framework can be measured after it has been implemented in a Canadian hospital facility.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".