How can we improve the recognition, reporting and resolution of medical device-related incidents in hospitals? A qualitative study of physicians and registered nurses
Bibliographic record
Abstract
BACKGROUND: To explore factors that influence and to identify initiatives to improve the recognition, reporting and resolution of device-related incidents. METHODS: Semi-structured telephone interviews with 16 health professionals in two tertiary care hospitals were conducted. Purposive sampling was used to identify appropriate study participants. Transcribed interviews were read independently by one individual to identify, define and organize themes and verified by another reviewer. RESULTS: Themes related to incident recognition were the hospital staff's knowledge and professional experience, medical device performance and clinical manifestations of patients, while incident reporting was influenced by error severity, personal attitudes of clinicians, feedback received on the error reported. Physicians often discontinued using medical devices if they malfunctioned. Education and training and the implementation of registries were discussed as important initiatives to improve medical device surveillance in clinical practice. CONCLUSIONS: Results from the telephone interviews suggest that multiple factors that influence participation in medical device surveillance activities are consistent with results for medical errors as reported in previous studies. The study results helped to propose a conceptual framework for a medical device surveillance system in a hospital context that would enhance patient safety and health care delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".