Use of an electronic information system to identify adverse events resulting in an emergency department visit
Bibliographic record
Abstract
OBJECTIVE: There is limited information about the nature of adverse events (AEs) that necessitate an emergency department (ED) visit. The objective of the current study was to demonstrate the feasibility of using routinely collected electronic data to identify AEs in patients presenting to EDs in one Canadian health authority. METHODS: This retrospective cross-sectional study occurred in EDs in two community hospitals, an outpatient community health centre and a tertiary care facility in the Capital District Health Authority in Nova Scotia, Canada between 1 November 2007 and 31 October 2008. The primary outcome was identification of an AE as the main reason for the ED visit. AEs were identified from electronic diagnostic data using previously validated screening criteria. RESULTS: There were 142,433 patient visits to the four EDs during the study period. A total of 1870 (1.3%) AEs were identified using the screening criteria. This included 1133 (0.8%) procedure-related, 673 (0.5%) drug-related, 63 (0.04%) device-related and one radiation-related AE. The AEs identified using this method were most likely the manifestation of treatment decisions made prior to the ED visit and/or related to care in other settings (eg, primary or long-term care, acute hospital care) including previous ED visits. INTERPRETATION: Although the use of electronic data significantly underestimates AEs treated in the ED, for relatively low cost, it provides new information on AEs arising from a variety of care settings that may otherwise not be captured. Significant and clinically important differences in healthcare utilisation underscore the value in identifying these AEs.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".