Emergency department-based medication review on outpatient health services utilization: interrupted time series
Bibliographic record
Abstract
BACKGROUND: One in nine emergency department (ED) visits in Canada are caused by adverse drug events, the unintended and harmful effects of medication use. Medication reviews by clinical pharmacists are interventions designed to optimize medications and address adverse drug events to impact patient outcomes. However, the effect of medication reviews on long-term outpatient health services utilization is not well understood. This research studied the effect of medication review performed by clinical pharmacists on long-term outpatient health services utilization. METHODS: Data included information from 10,783 patients who were part of a prospective, multi-centre quality improvement evaluation from 2011 to 2013. Outpatient health services utilization was defined as total ED visits and physician contacts, aggregated to four physician specialty groups: general and family practitioners (GP); medical specialists; surgical specialists; and imaging and laboratory specialists. During triage, patients deemed high-risk based on their medical history, were systematically allocated to receive either a medication review (n = 6403) or the standard of care (n = 4380). Medication review involved a critical examination of a patient's medications to identify and resolve medication-related problems and communicate these results to community care providers. Interrupted time series analysis compared the effect of the intervention on health services utilization relative to the standard of care controlling for pre-intervention differences in utilization. RESULTS: ED-based pharmacist-led medication review did not result in a significant level or trend change in the primary outcome of total outpatient health services utilization. There were also no differences in the secondary outcomes of primary care physician visits or ED visits relative to the standard of care in the 12 months following the intervention. Our findings were consistent when stratified by age, hospital site, and whether patients were discharged on their index visit. CONCLUSION: This was the first study to measure long-term trends of physician visits following an ED-based medication review. The lack of differences in level and trend of GP and ED visits suggest that pharmacist recommendations may not have been adequately communicated to community-based providers, and/or recommendations may not have affected health care delivery. Future studies should evaluate physician acceptance of pharmacist recommendations and should encourage patient follow-up to community providers.
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".