Evolving Factors in Hospital Safety: A Systematic Review and Meta-Analysis of Hospital Adverse Events
Bibliographic record
Abstract
Objective This study aimed to estimate the frequency of hospital adverse events (AEs) and explore the rate of AEs over time, and across and within hospital populations. Methods Validated search terms were run in MEDLINE and EMBASE; gray literature and references of included studies were also searched. Studies of any design or language providing an estimate of AEs within the hospital were eligible. Studies were excluded if they only provided an estimate for a specific AE, a subgroup of hospital patients or children. Data were abstracted in duplicate using a standardized data abstraction form. Study quality was assessed using the Newcastle-Ottawa Scale. A random-effects meta-analysis estimated the occurrence of hospital AEs, and meta-regression explored the association between hospital AEs, and patient and hospital characteristics. Results A total of 45,426 unique references were identified; 1,265 full-texts were reviewed and 94 studies representing 590 million admissions from 25 countries from 1961 to 2014 were included. The incidence of hospital AEs was 8.6 per 100 patient admissions (95% confidence interval [CI], 8.3 to 8.9; I 2 = 100%, P < 0.001). Half of the AEs were preventable (52.6%), and a third resulted in moderate/significant harm (39.7%). The most evaluated AEs were surgical AEs, drug-related AEs, and nosocomial infections. The occurrence of AEs increased by year (95% CI, −0.05 to −0.04; P < 0.001) and patient age (95% CI = −0.15 to −0.14; P < 0.001), and varied by country income level and study characteristics. Patient sex, hospital type, hospital service, and geographical location were not associated with AEs. Conclusions Hospital AEs are common, and reported rates are increasing in the literature. Given the increase in AEs over time, hospitals should reinvest in improving hospital safety with a focus on interventions targeted toward the more than half of AEs that are preventable.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".