Using prospective clinical surveillance to identify adverse events in hospital
Bibliographic record
Abstract
BACKGROUND To improve patient safety, organisations must systematically measure avoidable harms. Clinical surveillance-consisting of prospective case finding and peer review-could improve identification of adverse events (AEs), preventable AEs and potential AEs. The authors sought to describe and compare findings of clinical surveillance on four clinical services in an academic hospital. METHODS Clinical surveillance was performed by a nurse observer who monitored patients for prespecified clinical events and collected standard information about each event. A multidisciplinary, peer-review committee rated causation for each event. Events were subsequently classified in terms of severity and type. RESULTS The authors monitored 1406 patients during their admission to four hospital services: Cardiac Surgery Intensive Care (n=226), Intensive Care (n=211), General Internal Medicine (n=453) and Obstetrics (n=516). The authors detected 245 AEs during 9300 patient days of observation (2.6 AEs per 100 patient days). 88 AEs (33%) were preventable. The proportion of patients experiencing at least one AE, preventable AE or potential AE was 13.7%, 6.1% and 5.3%, respectively. AE risk varied between services, ranging from 1.4% of Obstetrics to 11% of Internal Medicine and Intensive Care patients experiencing at least one preventable AE. The proportion of patients experiencing AEs resulting in permanent disability or death varied between services: ranging from 0.2% on Obstetrics to 4.9% on Cardiac Surgery Intensive Care. No services shared the most frequent AE type. CONCLUSIONS Using clinical surveillance, the authors identified a high risk of AE and significant variation in AE risks and subtypes between services. These findings suggest that institutions will need to evaluate service-specific safety problems to set priorities and design improvement strategies.
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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 | 0.042 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".