Effect of electronic medication reconciliation at the time of hospital discharge on inappropriate medication use in the community: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: It is unclear if enhanced electronic medication reconciliation systems can reduce inappropriate medication use and improve patient care. We evaluated trends in potentially inappropriate medication use after hospital discharge before and after adoption of an electronic medication reconciliation system. METHODS: We conducted an interrupted time-series analysis in 3 tertiary care hospitals in London, Ontario, using linked health care data (2011-2019). We included patients aged 66 years and older who were discharged from hospital. Starting between Apr. 13 and May 21, 2014, physicians were required to complete an electronic medication reconciliation module for each discharged patient. As a process outcome, we evaluated the proportion of patients who continued to receive a benzodiazepine, antipsychotic or gastric acid suppressant as an outpatient when these medications were first started during the hospital stay. The clinical outcome was a return to hospital within 90 days of discharge with a fall or fracture among patients who received a new benzodiazepine or antipsychotic during their hospital stay. We used segmented linear regression for the analysis. RESULTS: We identified 15 932 patients with a total of 18 405 hospital discharge episodes. Before the implementation of the electronic medication reconciliation system, 16.3% of patients received a prescription for a benzodiazepine, antipsychotic or gastric acid suppressant after their hospital stay. After implementation, there was a significant and immediate 7.0% absolute decline in this proportion (95% confidence interval [CI] 4.5% to 9.5%). Before implementation, 4.1% of discharged patients who newly received a benzodiazepine or antipsychotic returned to hospital with a fracture or fall within 90 days. After implementation, there was a significant and immediate 2.3% absolute decline in this outcome (95% CI 0.3% to 4.3%). INTERPRETATION: Implementation of an electronic medication reconciliation system in 3 tertiary care hospitals reduced potentially inappropriate medication use and associated adverse events when patients transitioned back to the community. Enhanced electronic medication reconciliation systems may allow other hospitals to improve patient safety.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".