Challenges at Care Transitions: Failure to Follow Medication Changes Made at Hospital Discharge
Bibliographic record
Abstract
BACKGROUND: In-hospital medication reconciliation has not demonstrated reductions in adverse health outcomes, possibly because patients do not follow the changes made to their preadmission medications. Our objective was to determine the incidence of and variables associated with failure to follow newly prescribed therapies, discontinued medications, and dose changes. METHODS: A prospective cohort study of patients admitted to hospitals in Montreal, Quebec between 2014 and 2016 was conducted. Failure to follow medication changes 30 days post discharge was measured by comparing prescribed and dispensed medications. Multivariable logistic regression was used to determine characteristics associated with failure to follow changes. RESULTS: Among 2655 patients, mean age was 69.5 years (SD 14.7), and 1581 (60%) were males. There were 10,068 medication changes made at hospital discharge and 24% were not followed in the 30 days post discharge. Thirty percent of dose modifications were filled at the incorrect dose, 27% of new medications were not filled, and 12% of discontinued medications were filled. A number of factors increased the risk of failure to follow medication changes, including increasing out-of-pocket medication costs (adjusted odds ratio [aOR] 1.12; 95% confidence interval [CI], 1.07-1.18), discharge to long-term care facility (aOR 2.29; 95% CI, 1.63-3.08), and not having medications dispensed prior to admission (aOR 4.67; 95% CI, 3.75-5.90). CONCLUSION: One in 4 hospital medication changes was not followed post discharge. Health policy aimed at eliminating out-of-pocket medication costs and investigation of factors influencing failure to follow changes for those not dispensed medications prior to admission and for long-term care residents are important next steps to address this issue.
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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 | 0.014 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".