Beliefs and perceptions of patient safety event reporting in a Canadian Emergency Department: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: Patient safety events (PSEs) are unwanted or unexpected events that occur during medical care. High cognitive loads and frequent interruptions make emergency departments (EDs) uniquely error prone environments. Yet, frontline clinicians rarely report PSEs using incident reporting systems. The incidence, severity, and preventability of PSEs thus remain poorly understood, and contributing factors are understudied. We sought to understand ED staff beliefs and perceptions about their PSE reporting system and what features they believe are important in such a system. METHODS: We conducted a qualitative study among healthcare providers working in the ED and departmental leadership. We recruited participants via email and held a series of interviews, focus groups, and participatory workshops. We iteratively analyzed the data using the constant comparative method and used thematic analysis to establish themes. RESULTS: 50 participants attended at least one focus group, interview, or workshop. Participants perceived that PSE reporting through formal channels in the ED was challenging. Clinicians had an inherent desire to report PSEs and do so through numerous informal channels, yet underreported in formal reporting systems. The current PSE reporting system did not meet frontline staff needs and was viewed as ineffective in improving care quality and safety. We identified three key features for an improved PSE reporting system: (1) clear definitions; (2) transparency; and (3) simplicity. CONCLUSIONS: In this study, we have identified ideal features for PSE reporting processes to meet the needs of both frontline staff and departmental leadership based on perceptions of current PSE reporting practices. Improved PSE reporting processes have the potential to increase PSE reporting in the ED overall, increasing the availability of information about PSEs to support quality improvement and improve patient safety.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".