A116 PROMOTING ALCOHOL CESSATION IN THE INPATIENT GASTROENTEROLOGY WARD
Bibliographic record
Abstract
Abstract Background Alcohol use disorder (AUD) is a significant global health issue, ranking as the third leading cause of death and disability, with a financial burden exceeding $16 billion annually for the Canadian healthcare system. A recent review of patient discharges from the inpatient Gastroenterology ward at University Hospital revealed that less than 8% of patients with AUD were discharged with anti-craving medications or addiction referrals, highlighting a gap in care coordination Aims Increase the prescription rate of anti-craving medications by 20% for patients admitted with alcohol-related conditions to the Gastroenterology ward within the next 6 months, to improve post-discharge care and addiction support Methods The project began by identifying key stakeholders and surveying 22 residents rotating through gastroenterology and 11 consultant physicians. Results revealed that 34.7% of residents and 36.4% of consultants were uncomfortable prescribing medications for alcohol use disorder. Root cause analysis identified several issues: limited knowledge of anticraving medications, lack of addiction resources, time constraints, and no standardized process for identifying high-risk alcohol use disorder patients. Multiple Plan-Do-Study-Act (PDSA) cycles were implemented, targeting trainee education with handouts, early detection of high-risk patients using the AUDIT-C questionnaire at admission, and increasing referrals to addiction services and social work Results Between January and July 2024, a total of 57 patients with alcohol-related admissions were identified. During the intervention period, educational handouts for trainees and a Gastroenterology educational grand rounds presentation were implemented, demonstrating some success in increasing rates of anticraving medications. However, these results were not sustained, highlighting the need for a long-term solution. We are developing an EMR-based admission order set to standardize AUDIT-C completion, facilitating the identification of high-risk inpatients and automatic consultations with social work and addiction services through informatics principles Conclusions Although educational interventions temporarily increased rates of anticraving medications, the results were not sustainable. The project is ongoing and will transition to exploring EMR-based interventions Comfort of Prescribing Anti-Craving Medication among GI staff Funding Agencies None
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".