Policy interventions for improving hospital-to-home transitions of care for older adults and informal caregivers: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Efficient hospital-to-home transitions for older adults and their informal caregivers are hampered by current fragmented care systems, resulting in communication and coordination lapses when people move between hospital-to-home settings. Such fragmentation often leads to suboptimal hand-overs of information and care, medication errors, and overlooked follow-up appointments, which, in turn, contribute to adverse health outcomes for the elderly population. This study aims to answer the question: "What policy interventions can improve the transitions from hospital to home for older adults and their informal caregivers" Thus the study focuses on delineating policy recommendations at the micro, meso, and macro levels to facilitate smoother and more beneficial hospital-to-home transitions for older adults and their informal caregivers. METHODS: As part of the European Union Transitional Care Program (TRANS-SENIOR), this qualitative descriptive study leverages a multiple perspectives approach through in-depth interviews with older adults and informal caregivers. The goal is to pinpoint critical intervention zones of policy recommendations based on a holistic understanding of older adult and caregiver recommendations for improving hospital-to-home transitions. RESULTS: Findings show strategies that strengthen patient and caregiver engagement on the micro level. These include implementing personalized care plans and improving communication channels between healthcare providers and their recipients. The meso level targets healthcare organizations and systems, promoting the adoption of streamlined care coordination, enhanced discharge planning, and bolstered support services for caregivers. Such interventions are designed to smooth the transition process, ensuring that care continues seamlessly from hospital to home. At the macro level, our findings urge policy reforms to address broader systemic issues, such as the allocation of resources, the introduction of funding mechanisms, and the expansion of healthcare workforce capacity. These policy recommendations aim to create an enabling environment for effective care transitions, addressing underlying challenges that impede seamless care transitions. CONCLUSION: This paper presents a set of policy recommendations for policymakers, healthcare professionals, and stakeholders. These recommendations aim to tackle the multifaceted challenges associated with hospital-to-home transitions to enhance care experience and outcomes for older adults and their caregivers by addressing individual, organizational, and systemic issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".