International comparative analyses of incidents reporting systems for healthcare risk management
Bibliographic record
Abstract
OBJECTIVE: To compare administration of incidence reporting systems for healthcare risk management in the United Kingdom, the United States, Canada, Australia, and Taiwan, and to provide evidence and recommendations for healthcare risk management policy in China. METHODS: We searched the official websites of the healthcare risk management agencies of the four countries and one district for laws, regulatory documents, research reports, reviews, and evaluation forms concerned with healthcare risk management and assessment. Descriptive comparative analysis was performed on relevant documents. RESULTS: (1) A total of 142 documents were included in this study. The United States had the most relevant documents (68). (2) The type of incidents from reporting systems has expanded from medication errors and hospital-acquired infections to near-misses, and now includes all patient safety incidents. (3) The incidence-reporting systems can be grouped into two models: government-led and legal/regulatory/NGO-collaborative. (4) In two cases, reporting systems were established for specific incident types: One for death or serious injury events (the sentinel events database in Britain, SIRL), and one for healthcare-associated infections (NHSN in America). (5) Compared to the four countries, Taiwan's system put more emphasis on public welfare, confidentiality, and information sharing. The contents of reporting there covered every aspect of risk management to create a more secure environment. CONCLUSION: (1) Britain's national reporting and learning system was representative of a government-led model; (2) The United States was the earliest country to have a reporting system, which included a limited range of incident types. Management of incidents became more reliable with increased application of laws, regulations, and guidances; (3) Both the Canadian and the Australian systems drew from the American experience and are still developing; (4) The Taiwanese system was comprehensive and is an instructional case.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.054 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".