The economic value of empowering older patients transitioning from hospital to home: evidence from the ‘Your Care Needs You’ intervention
Bibliographic record
Abstract
BACKGROUND: Hospital-to-home transitions are a critical component of effective healthcare delivery, especially for patients aged 75 and older. This study evaluates the cost-effectiveness of the 'Your Care Needs You' (YCNY) intervention, a patient-centred approach designed to empower older adults during discharge, compared to standard care. METHODS: The analysis adopts the perspective of the National Health Service (NHS) and Personal Social Services. Data were drawn from a cluster randomised controlled trial (cRCT) conducted within the UK NHS over a 90-day postdischarge follow-up period. Adjusted differences in costs and quality-adjusted life years (QALYs) were estimated using multilevel mixed-effects generalised linear models (MME-GLMs) to account for the hierarchical structure of the trial design. Alternatively, seemingly unrelated regression (SUR) models were employed to address potential correlations between costs and QALYs. Scenario analyses and probabilistic sensitivity analyses were conducted to assess the robustness of the results. RESULTS: The YCNY intervention reduced costs by £269 and achieved a QALY gain of 0.0057, resulting in a net health benefit (NHB) of 0.0246 QALYs at a £15,000/QALY threshold. It demonstrated an 89% probability of cost-effectiveness compared to standard care within the trial's time horizon. Findings remained robust across alternative scenarios and sensitivity analyses. CONCLUSION: The results suggest that YCNY is a potentially cost-effective strategy for improving hospital-to-home transitions for older adults. The study supports integrating patient-involved interventions like YCNY into routine NHS practice, with the potential to improve both efficiency and quality of healthcare delivery.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".