Flow Through the Emergency Department for Patients Presenting with Substance Use Disorder in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Since 2016 the province of Alberta, Canada, has seen a significant increase in substance use disorder (SUD) presentations to the emergency department (ED) with a large surge during the COVID-19 pandemic. In this retrospective study we deconstruct the total length of stay (LOS) in the ED into stages for patients presenting with SUD and estimate the effects of covariates on the time to transition between stages. METHODS: Modification, codes F10.0-F19.9 and T36.0-T50.9, we extracted data from the National Ambulatory Care Reporting System between April 1, 2019-March 31, 2020 on all ED presentations for SUD by Alberta residents. We used a multistate model to deconstruct the EDLOS into eight mutually exclusive states and determine which factors affected the time spent in each state. RESULTS: We analyzed 66,880 presentations (37,530 patients). The mean age was 37.2 years, and 61% were male. The median total LOS in the ED was 6 hours 13 minutes. Patients presenting with methamphetamines (METH) intoxication and patients from low-income neighborhoods had significantly increased transition times between all states. Opposite this, opiate use was associated with faster transition times between almost all states. Metro EDs experienced slower transitions when attempting to discharge or admit patients when compared to urban or rural EDs. Emergency department crowding also had a dramatic effect on physician initial assessment times, while discharge and admission times in patients presenting with SUD were also significantly affected. CONCLUSION: Patients with SUD experience a variety of delays during their ED stay. Those with METH intoxication and those from the lowest income neighborhoods were most likely to experience slower transitions from state to state in the ED and may benefit from a focused approach to improve ED flow.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".