Alcohol Medical Intervention Clinic: A Rapid Access Addiction Medicine Model Reduces Emergency Department Visits
Bibliographic record
Abstract
OBJECTIVES: Problematic alcohol use accounts for a large proportion of Emergency Department (ED) visits and revisits. We developed the Alcohol Medical Intervention Clinic (AMIC), a Rapid Access Addiction Medicine (RAAM) service, to reduce alcohol-related ED re-utilization and improve care for individuals with alcohol problems. This article describes the AMIC model and reports on an evaluation of its impact on patients and the ED system. METHODS: Individuals presenting to The Ottawa Hospital Emergency Departments (TOH-ED) for an alcohol-related issue were referred to AMIC. Using data collected via medical chart review, and also self-report questionnaires, we assessed ED visits, revisits, and changes in alcohol use and mental health symptoms in patients before and after receiving services in AMIC. The incidence of alcohol-related ED visits and re-visits from 12-month periods before and after the introduction of AMIC were compared using data from TOH Data Warehouse. Connections made to additional services and patient satisfaction was also assessed. RESULTS: For patients served by AMIC, from May 26, 2016 to June 30, 2017 (n = 194), there was an 82% reduction in 30-day visits and re-visits (P < 0.001). An 8.1% reduction in total alcohol-related 30-day TOH-ED revisit rates and a 10% reduction in total alcohol-related TOH-ED visits were found. After receiving AMIC services, clients reported reductions in alcohol use, depression, and anxiety (P < 0.001). CONCLUSIONS: AMIC demonstrated positive impacts on patients and the healthcare system. AMIC reduced ED utilization, connected people with community services, and built system capacity to serve people with alcohol problems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".