Defining potentially preventable emergency department visits for older adults
Bibliographic record
Abstract
Objective: As older adults become increasingly reliant on emergency departments (EDs) for care, there is an interest in determining what types of ED visits by this population may be preventable, or amenable to other forms of care. The aim of this project was to explore the concept of preventable ED visits by older adults.Methods: We conducted a literature search to identify definitions of “preventable” or “avoidable” ED visits. We then applied a definition of preventable ED visits to an administrative data set consisting of ED visit data extracted from four sites in Halifax, Nova Scotia, Canada. Visits for patients 65 years of age or older were eligible for inclusion. Visits were categorized using triage level and discharge diagnosis.Results: Four methods of defining preventable ED visits were identified in our literature search: 1) Ambulatory Care Sensitive Conditions (ACSCs) (N = 7), 2) Low Acuity/low intensity visits (N = 5), 3) New York University (NYU) (Billings) Algorithm (N = 3) and 4) hospital admission vs. non-admission (N = 1). We categorized 34,454 ED visits from our dataset using a modified definition of preventable ED visits that included ACSCs (15.3%) as well as low acuity visits that required no testing or hospital admission (9.9%).Conclusions: Our results suggest that approximately 25% of ED visits by older adults may be preventable or amenable to other forms of care. This data may be useful in the planning of care delivery appropriate for the needs of this population.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".