Understanding Patterns of Emergency Department (ED) Use over time in Ontario to plan new EDs for the future
Bibliographic record
Abstract
IntroductionThe Applied Health Research Question (AHRQ) portfolio is an initiative funded by the Ontario Ministry of Health and Long-Term Care, leveraging the linked data and scientific expertise at ICES to answer questions that directly impact healthcare policy, planning or practice. Objectives and ApproachThe objective of this project was to evaluate historical patterns of emergency department (ED) use to better plan for a new emergency Department in Kingston and to better understand the factors contributing to increasing ED utilization. Emergency departments across Ontario continue to see consistent increases in volume at rates exceeding expected volume growth due to population growth alone. Some hospitals across the province observe significantly higher volume increases compared to the provincial average. ResultsFrom 2006/07 to 2016/17, rate and volume of emergency department visits in Ontario increased 8.82% and 19.87% respectively. Throughout the same period, emergency department visit rate and volume at Kingston General Hospital increased 20.70%, and 27.2%. Using historical data and projected population growth by age and sex, we were able to estimate that emergency department volume would increase at least 11.94% by 2025 due to estimated shifts in population size and distribution (by age and sex) alone. From 2006/07 to 2016/17, the greatest rate of increase in reason for ED visits was mental/behavioral problems. Throughout this period the increase in volume and rate of ED visits due to mental/behavioural problems was 274.46% and 259.59% respectively. Conclusion/ImplicationsPopulation-specific volume projections and historical trends in ED use can be utilized for planning ED operations to improve efficiency and patient care quality. This has been used to inform the redesign of the ED at the Kingston Health Sciences Centre to ensure it will meet the needs of the community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".