Barriers and enablers in implementing an electronic incident reporting system in a teaching hospital: A case study from Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Widespread recognition of the impact of healthcare adverse events has triggered incident reporting system implementation to promote patient safety. The aim was to assess the effectiveness, usability, enablers, and barriers of the Electronic Occurrence Variance Reporting System (eOVR) in addition to end user satisfaction. METHODS: This study comprised a cross-sectional survey two years after implementation of the eOVR. Secondary data analysis evaluated the volume of incident reporting before and after implementing the eOVR. OUTCOME MEASURES: Primary outcome measures: satisfaction and system usability, system security, workplace safety culture, training, and reporting trends. An overall satisfaction was collected. SECONDARY OUTCOME: rate of reported OVRs per 1000 admissions. Furthermore, barriers and enablers to the reporting process were explored. RESULTS: Study findings indicate that the eOVR has been successful in terms of high satisfaction according to respondents. Most of the respondents found the system easy to access, maintained patient confidentiality and reporting anonymity. Around half the respondents indicated having a non-punitive culture of reporting in their hospital. Physicians had significantly lower scores in all primary outcomes Incident reporting increased by 33.6% (p < 0.0001) after implementing the eOVR. CONCLUSION: Successful incident reporting systems should be easy and simple to use, accessible and include features that guarantee anonymity and confidentiality. End-users should be trained prior to launching such a system. The implementation of such systems needs to be combined with promoting a just culture in the organization, timely feedback, more involvement and focus on physicians and junior staff which will improve user satisfaction and reporting rates.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".