Risk of Frequent Emergency Department Use Among an Ambulatory Care Sensitive Condition Population
Bibliographic record
Abstract
BACKGROUND: A small fraction of patients use a disproportionately large amount of emergency department (ED) resources. Identifying these patients, especially those with ambulatory care sensitive conditions (ACSC), would allow health care professionals to enhance their outpatient care. OBJECTIVE: The objectives of the study were to determine predictive factors associated with frequent ED use in a Quebec adult population with ACSCs and to compare several models predicting the risk of becoming an ED frequent user following an ED visit. RESEARCH DESIGN: This was an observational population-based cohort study extracted from Quebec's administrative data. SUBJECTS: The cohort included 451,775 adult patients, living in nonremote areas, with an ED visit between January 2012 and December 2013 (index visit), and previously diagnosed with an ACSC but not dementia. MEASURES: The outcome was frequent ED use (≥4 visits) during the year following the index visit. Predictors included sociodemographics, physical and mental comorbidities, and prior use of health services. We developed several logistic models (with different sets of predictors) on a derivation cohort (2012 cohort) and tested them on a validation cohort (2013 cohort). RESULTS: Frequent ED users represented 5% of the cohort and accounted for 36% of all ED visits. A simple 2-variable prediction model incorporating history of hospitalization and number of previous ED use accurately predicted future frequent ED use. The full model with all sets of predictors performed only slightly better than the simple model (area under the receiver-operating characteristic curve: 0.786 vs. 0.759, respectively; similar positive predictive value and number needed to evaluate curves). CONCLUSIONS: The ability to identify frequent ED users based only on previous ED and hospitalization use provides an opportunity to rapidly target this population for appropriate interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".