Emergency Department Utilization by Older Adults: a Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Emergency Departments (EDs) are playing an increasingly important role in the care of older adults. Characterizing ED usage will facilitate the planning for care delivery more suited to the complex health needs of this population. METHODS: In this retrospective cross-sectional study, administrative and clinical data were extracted from four study sites. Visits for patients aged 65 years or older were characterized using standard descriptive statistics. RESULTS: We analyzed 34,454 ED visits by older adults, accounting for 21.8% of the total ED visits for our study time period. Overall, 74.2% of patient visits were triaged as urgent or emergent. Almost half (49.8%) of visits involved diagnostic imaging, 62.1% involved lab work, and 30.8% involved consultation with hospital services. The most common ED diagnoses were symptom- or injury-related (25.0%, 17.1%. respectively). Length of stay increased with age group (Mann-Whitney U; p < .0001), as did the proportion of visits involving diagnostic testing and consultation (χ(2); p < .0001). Approximately 20% of older adults in our study population were admitted to hospital following their ED visit. CONCLUSIONS: Older adults have distinct patterns of ED use. ED resource use intensity increases with age. These patterns may be used to target future interventions involving alternative care for older adults.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".