Patient safety research: an overview of the global evidence
Bibliographic record
Abstract
BACKGROUND: Unsafe medical care may cause substantial morbidity and mortality globally, despite imprecise estimates of the magnitude of the problem. To better understand the extent and nature of the problem of unsafe care, the WHO World Alliance for Patient Safety commissioned an overview of the world's literature on patient safety research. METHODS: Major patient safety topics were identified through a consultative and investigative process and were categorised into the framework of structure, process and outcomes of unsafe care. Lead experts examined current evidence and identified major knowledge gaps relating to topics in developing, transitional and developed nations. The report was reviewed by internal and external experts and underwent improvements based on the feedback. FINDINGS: Twenty-three major patient safety topics were examined. Much of the evidence of the outcomes of unsafe care is from developed nations, where prevalence studies demonstrate that between 3% and 16% of hospitalised patients suffer harm from medical care. Data from transitional and developing countries also suggest substantial harm from medical care. However, considerable gaps in knowledge about the structural and process factors that underlie unsafe care globally make solutions difficult to identify, especially in resource-poor settings. INTERPRETATION: Harm from medical care appears to pose a substantial burden to the world's population. However, much of the evidence base comes from developed nations. Understanding the scope of and solutions for unsafe care for the rest of the world is a critical component of delivering safe, effective care to all of the world's citizens.
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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 | 0.080 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.039 | 0.033 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".