Impact of an addiction medicine consult team intervention in a Canadian inner city hospital on acute care utilization: a pragmatic quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Inner city patients have a higher illness burden and need for care, but experience more unmet care needs. Hospital Addiction Medicine Consult Teams (AMCTs) are a promising emerging intervention. The objective of this study was to assess the impact of a Canadian AMCT-like intervention for inner city patients on reduction in high emergency department (ED) use, hospital admission, and inpatient length of stay. METHODS: Using a community-engaged, two-arm, pre-post, longitudinal quasi-experimental study design, 572 patients reporting active substance use, unstable housing, unstable income, or a combination thereof (302 at intervention site, 270 at control sites) were enrolled. Survey and administrative health service data were collected at baseline, six months post-enrolment, and 12 months post-enrolment. Multivariable regression models tested the intervention effect, adjusting for clinically important covariables (inpatient status at enrolment, medical complexity, age, gender, Indigenous identity, shelter use, opioid use). RESULTS: Initial bivariable analyses demonstrated an intervention effect on reduction in admissions and length of stay, however, this effect was no longer significant after adjusting for covariables. There was no evidence of reduction in high ED use on either bivariable or subsequent multivariable analysis. CONCLUSIONS: After adjusting for covariables, no AMCT intervention effect was detected for reduction in high ED use, inpatient admission, or hospital length of stay. Further research is recommended to assess other patient-oriented intervention outcomes.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".