Factors that influence the recognition, reporting and resolution of incidents related to medical devices and other healthcare technologies: a systematic review
Bibliographic record
Abstract
BACKGROUND: Medical devices have improved the treatment of many medical conditions. Despite their benefit, the use of devices can lead to unintended incidents, potentially resulting in unnecessary harm, injury or complications to the patient, a complaint, loss or damage. Devices are used in hospitals on a routine basis. Research to date, however, has been primarily limited to describing incidents rates, so the optimal design of a hospital-based surveillance system remains unclear. Our research objectives were twofold: i) to explore factors that influence device-related incident recognition, reporting and resolution and ii) to investigate interventions or strategies to improve the recognition, reporting and resolution of medical device-related incidents. METHODS: We searched the bibliographic databases: MEDLINE, Embase, the Cochrane Central Register of Controlled Trials and PsycINFO database. Grey literature (literature that is not commercially available) was searched for studies on factors that influence incident recognition, reporting and resolution published and interventions or strategies for their improvement from 2003 to 2014. Although we focused on medical devices, other health technologies were eligible for inclusion. RESULTS: Thirty studies were included in our systematic review, but most studies were concentrated on other health technologies. The study findings indicate that fear of punishment, uncertainty of what should be reported and how incident reports will be used and time constraints to incident reporting are common barriers to incident recognition and reporting. Relevant studies on the resolution of medical errors were not found. Strategies to improve error reporting include the use of an electronic error reporting system, increased training and feedback to frontline clinicians about the reported error. CONCLUSIONS: The available evidence on factors influencing medical device-related incident recognition, reporting and resolution by healthcare professionals can inform data collection and analysis in future studies. Since evidence gaps on medical device-related incidents exist, telephone interviews with frontline clinicians will be conducted to solicit information about their experiences with medical devices and suggested strategies for device surveillance improvement in a hospital context. Further research also should investigate the impact of human, system, organizational and education factors on the development and implementation of local medical device 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 | 0.014 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".