Investigating Electronic Incident Reporting Systems and Its Role Within Healthcare to Improve Patient Safety: An Integrative Literature Review
Bibliographic record
Abstract
Incident reporting is a welcomed practice in industries such as aviation for improving safety. This practice is now welcomed in healthcare in many countries (Vincent, 2010). For instance, In the UK, incident reporting is a component of individual hospital risk governance processes and a key requirement for National Health Service (NHS) Organizations (Rooksby et al., 2007). In spite of this, widespread implementation of incident reporting is still not clear even though reporting has resulted in improvements to safety. Vincent et al. (2008) cited that recent studies of incident reporting suggest that its role in managing safety has been over emphasized and there should be less emphasis on counting incidents and more emphasis on analyzing the effectiveness of incidents and institutional learning (Braithwaite et al., 2011). Most studies of incident reporting have focused on factors, such as staff willingness to report incidents, barriers to incident reporting, and the culture surrounding reporting. However, few studies have examined the effectiveness of electronic incident reporting systems in improving safety, and there is little evidence regarding how the technology contributes to safety (Anderson et al., 2013). For this reason, the purpose of this study was to investigate electronic incident reporting systems and its role within healthcare to improve patient safety. By focusing on this, the researcher was successful in highlighting a series of behaviors and perceptions around electronic incident reporting. Equally important, the researcher provided several themes that has been known to both inhibit and promote confidence within this technology offering. Thereafter, the researcher then suggested strategies for healthcare leaders to consider when adopting as a means to help bridge the gap between healthcare workers and electronic incident reporting systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".