Does the definition of preventable emergency department visit matter? An empirical analysis using 20 million visits in Ontario and Alberta
Bibliographic record
Abstract
OBJECTIVES: This study had two objectives: (1) to estimate the prevalence of preventable emergency department (ED) visits during the 2016-2020 time period among those living in 19 large urban centers in Alberta and Ontario, Canada, and (2) to assess if the definition of preventable ED visits matters in estimating the prevalence. METHODS: A retrospective, population-based study of ED visits that were reported to the National Ambulatory Care Reporting System from April 1, 2016, to March 31, 2020, was conducted. Preventable ED visits were operationalized based on the following approaches: (1) Canadian Triage and Acuity Scale (CTAS), (2) ambulatory care-sensitive conditions (ACSC), (3) family practice-sensitive conditions (FPSC), and (4) sentinel nonurgent conditions (SNC). The overall proportion of ED visits that were preventable was estimated. We also estimated the adjusted relative risks of preventable ED visits by patients' sex and age, fiscal year, province of residence, and census metropolitan area (CMA) of residence. RESULTS: There were 20,171,319 ED visits made by 8,919,618 patients ages 1 to 74 who resided in one of the 19 CMAs in Alberta or Ontario. On average, there were 2.26 visits per patient over the period of 4 fiscal years; most patients made one (44.22%) or two ED visits (20.72%). The overall unadjusted prevalence of preventable ED visits varied by definition; 35.33% of ED visits were defined as preventable based on CTAS, 12.88% based on FPSC, 3.41% based on SNC, and 2.33% based on ACSC. CONCLUSIONS: There is a substantial level of variation in prevalence estimates across definitions of preventable ED visits, and care should be taken when interpreting these estimates as each has a different meaning and may lead to different conclusions. The conceptualization and measurement of preventable ED visits is complex and multifaceted and may not be adequately captured by a single definition.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".