Alcohol Medical Intervention Clinic: A Rapid Access Addiction Medicine Model Reduces Emergency Department Visits
Why this work is in the frame
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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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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 it