Provision of medication supply at hospital discharge: A rapid scoping review of the “Meds-to-Beds” care model
Bibliographic record
Abstract
PURPOSE: Meds-to-beds (MTB) programs support care transitions by providing medications and related counseling to patients before hospital discharge. This review describes studies of MTB programs in terms of their study designs, intervention elements, and outcomes. . METHODS: A rapid scoping review was performed searching MEDLINE, Embase, Web of Science, CINAHL, and International Pharmaceutical Abstracts (from inception to December 2023) for studies examining the implementation and evaluation of an MTB program. Citations and full-text articles were independently screened by 2 reviewers. An adaptation of the Consolidated Framework for Implementation Research for care transitions guided data extraction. Data were synthesized using the 3-stage approach of Petticrew and Roberts. . RESULTS: Titles and abstracts from 4,362 identified citations and 129 full-text articles were screened, with 45 studies extracted. Of these, quality improvement initiatives (n = 16; 36%) and retrospective cohort studies (n = 12; 27%) were the most common. The majority of included studies were published between 2019 and 2023 (n = 27; 60%) and were conducted in the US (n = 37; 82%). Twenty-nine studies (64%) examined an MTB program as the sole intervention, while 16 studies (36%) examined an MTB program as part of a complex intervention. More than half the studies examined multiple outcomes (n = 25; 56%), with the most studied outcomes being healthcare utilization or system outcomes (n = 32; 71%), cost effects or pharmacy operational impacts (n = 21; 47%), patient and caregiver experience (n = 11; 24%), and patient clinical outcomes (n = 7; 16%). CONCLUSION: This rapid scoping review highlights existing gaps in the literature around MTB programs. The reviewed studies primarily had noncontrolled study designs, focusing on healthcare utilization and cost or pharmacy operational impacts. Fewer studies examined patient and caregiver experiences and patient clinical outcomes. Future research expanding the breadth of outcomes studied using controlled designs is warranted. .
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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 | 0.048 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".