Economic evaluation of hospital-to-home transition interventions: a rapid review protocol
Bibliographic record
Abstract
Abstract Background Transitions from hospital to home represent a critical juncture associated with hospital readmissions and increased healthcare costs. While interventions exist to improve care transitions, the economic value of these interventions remains limited. By synthesizing economic evaluations of transition interventions, healthcare systems can identify cost-effective strategies to reduce costs and readmission rates to improve outcomes for patients and providers. Objectives 1) Identify the types of economic analyses commonly applied in hospital-to-home evaluations; 2) summarize the financial implications and economic value of these interventions; 3) compare the costs and cost-effectiveness of interventions across care settings and patient populations. Methods We will conduct a rapid review to summarize existing literature on the cost-effectiveness of interventions aimed at improving hospital-to-home transitions. We will search electronic databases (Embase, MEDLINE, CINAHL) to identify studies assessing the economic impact of transition interventions on patient and health system outcomes. We will focus on economic studies, including cost studies, cost-consequence analyses, cost-minimization analyses, cost-effectiveness analyses, cost-benefit analyses, or cost-utility analyses, to understand the economic value of these interventions for both healthcare systems and patients. Implication Evidence on the economic benefits of hospital-to-home interventions can guide efficient and sustainable resource allocation in healthcare systems. Investing in sustainable transition interventions has the potential to lower overall healthcare expenditures while enhancing patient satisfaction and improving health outcomes. We hypothesize that this review will reveal that targeted strategies, such as enhanced discharge planning and home-based discharge supports, can achieve significant cost savings by reducing hospital readmissions and improving patient adherence to post-discharge care plans.
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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 | 0.093 | 0.147 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.009 |
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".