Interventions to address medication-related causes of hospital readmissions: A scoping review
Bibliographic record
Abstract
Unplanned readmissions pose a tremendous burden on patients, providers, and payers. A significant proportion of readmissions are medication-related. Despite the availability of literature regarding hospital-level strategies to reduce readmissions, little has been written about strategies aimed at medication-related readmissions. We sought to identify successful readmission reduction strategies by performing a scoping literature review of research published between 2000 and 2017. We identified 21 studies that met our inclusion criteria. From these studies, we identified 7 components frequently employed as a part of interventions to reduce medication-related readmissions: discharge planning, discharge education, post-discharge telephone calls, the use of a professional coordinator with clinical training to administer the intervention, patient education efforts, provider training efforts, and medication reconciliation. Thirty-eight percent of all the interventions identified were associated with a statistically significant reduction in readmissions. Of the 7 common intervention components we identified, none were consistently associated with intervention success in the full sample. However, interventions implemented by inpatient hospitals, in particular academic medical centers, had a higher success rate than interventions implemented by other providers. We examined a subsample of larger studies and found that discharge planning and medication reconciliation components were included in most of the successful interventions. Future research should look beyond simply identifying components included in an intervention and should instead seek to identify contextual factors that enable or inhibit the success of these components. Research examining discharge planning and medication reconciliation efforts will be particularly important.
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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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".