National and Institutional Trends in Adverse Events Over Time: A Systematic Review and Meta-analysis of Longitudinal Retrospective Patient Record Review Studies
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine if the implementation of large-scale patient safety initiatives have been successful in reducing overall and preventable adverse event rates in hospital inpatients. DESIGN: The design used in this study was systematic review and meta-analysis. DATA RESOURCES: We followed our published protocol (PROSPERO [CRD42019140058]) and searched the following databases: PubMed, CINAHL, PsycINFO, Cochrane Library, and Embase from inception to February 2020. The reference lists of eligible studies were also searched. ELIGIBILITY: All longitudinal retrospective record review studies that examined adverse event rates before and after the introduction of patient safety initiatives in hospital inpatients were included. DATA EXTRACTION: Data extraction, quality, and risk of bias assessment were carried out by 2 independent reviewers. Information on study design, setting, demographics, interventions, and safety outcome measures was extracted. RESULTS: A total of 3894 articles were screened, and 7 articles met the eligibility criteria for our systematic review with 5 of these providing sufficient information for inclusion in the meta-analysis. The degree of heterogeneity was high among studies. The meta-analysis demonstrated a minimal risk reduction in overall adverse event rates of 0.017 (95% confidence interval, 0.002-0.032) when the lower-quality studies were excluded, with one adverse event being prevented for every 59 hospital admissions. CONCLUSIONS: These findings are significant when the large numbers of admissions to a hospital every year are considered. Given the low numbers of large-scale implementation studies, there is a need for more research on the effectiveness of patient safety initiatives to further assess the impact of such initiatives on adverse events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".