Bibliographic record
Abstract
BACKGROUND: The "To Err is Human" report published by the Institute of Medicine (IOM) in 1999 called for a national effort to make health care safer. Although the report has been widely credited with spawning efforts to study and improve safety in health care, there has been limited objective assessment of its impact. We evaluated the effects of the IOM report on patient safety publications and research awards. METHODS: We searched MEDLINE to identify English language articles on patient safety and medical errors published between 1 November 1994 and 1 November 2004. Using interrupted time series analyses, changes in the number, type, and subject matter of patient safety publications were measured. We also examined federal (US only) funding of patient safety research awards for the fiscal years 1995-2004. RESULTS: A total of 5514 articles on patient safety and medical errors were published during the 10 year study period. The rate of patient safety publications increased from 59 to 164 articles per 100,000 MEDLINE publications (p<0.001) following the release of the IOM report. Increased rates of publication were observed for all types of patient safety articles. Publications of original research increased from an average of 24 to 41 articles per 100,000 MEDLINE publications after the release of the report (p<0.001), while patient safety research awards increased from 5 to 141 awards per 100,000 federally funded biomedical research awards (p<0.001). The most frequent subject of patient safety publications before the IOM report was malpractice (6% v 2%, p<0.001) while organizational culture was the most frequent subject (1% v 5%, p<0.001) after publication of the report. CONCLUSIONS: Publication of the report "To Err is Human" was associated with an increased number of patient safety publications and research awards. The report appears to have stimulated research and discussion about patient safety issues, but whether this will translate into safer patient care remains unknown.
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 | 0.087 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".