Factors influencing the reporting of adverse medical device events: qualitative interviews with physicians about higher risk implantable devices
Bibliographic record
Abstract
BACKGROUND: Postmarket surveillance of medical devices is reliant on physician reporting of adverse medical device events (AMDEs). Little is known about factors that influence whether and how physicians report AMDEs, an essential step in developing behaviour change interventions. This study explored factors that influence AMDE reporting. METHODS: Qualitative interviews were conducted with physicians who differed by specialties that implant cardiovascular and orthopaedic devices prone to AMDEs, geography and years in practice. Participants were asked if and how they reported AMDEs, and the influencing factors. Themes were identified inductively using constant comparative technique, and reviewed and discussed by the research team on four occasions. RESULTS: Twenty-two physicians of varying specialty, region, organisation and career stage perceived AMDE reporting as unnecessary, not possible or futile due to multiple factors. Physicians viewed AMDEs as an expected part of practice that they could manage by switching to different devices or developing work-around strategies for problematic devices. Physician beliefs and behaviour were reinforced by limited healthcare system capacity and industry responsiveness. The healthcare system lacked processes and infrastructure to detect, capture, share and act on information about AMDEs, and constrained device choice through purchasing contracts. The device industry did not respond to reports of AMDEs from physicians or improve their products based on such reports. As a result, participants said they used devices that were less than ideal for a given patient, leading to suboptimal patient outcomes. CONCLUSIONS: There may be little point in solely educating or incentivising individual physicians to report AMDEs unless environmental conditions are conducive to doing so. Future research should explore policies that govern AMDEs and investigate how to design and implement postmarket surveillance systems.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".