Comparative Analyses on Methods and Tools for Medical Risk Management and Assessment in the United Kingdom,the United States,Canada,Australia and Taiwan Region
Bibliographic record
Abstract
Objective To comprehensively compare the methods and tools for medical risk management and assessment in the United Kingdom,the United States,Canada,Australia and Taiwan region(hereafter shortened as countries and one region),so as to provide evidence and recommendations for medical risk management policy in China.Methods The official websites of the healthcare risk management agencies in these four countries and one region were searched to collect materials concerning healthcare risk management and monitoring,such as laws,regulatory documents,research reports,reviews and evaluation forms,then the descriptive comparative analysis was performed on the methods and tools for risk management.Results a) A total of 146 documents were included in this study,including 2 laws,17 regulatory documents,41 guidelines,37 reviews and 49 documents about general information;b) The United Kingdom applied the integrated risk management;Australia and Taiwan adopted the classical risk management process,including risk identification,risk analysis,risk evaluation and risk control,while the United States and Canada mainly chose the prospective failure mode and effects analysis(FMEA) for clinical risk management;c) The severity of clinical risk was divided into five grades in the United Kingdom and Australia,and six in Taiwan,respectively.The frequency of medical risk was divided into five grades with four grade responses in above two countries and one region;and d) There were almost the same processes and tools about Root Cause Analysis(RCA),but a little difference in the objects of analysis in these four countries and one region.Conclusion There are three models of risk management with the same assessment tools in these four countries and one region: the prospective risk assessment,the retrospective assessment based on occurred incidents and the integrated risk management.Although the grading of risk is similar,the definition of grading is different in the United Kingdom,Australia and Taiwan.The methods and processes of analyses on the adverse events are almost the same in these four countries and one region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".