Policies addressing delayed discharges from hospital in France
Bibliographic record
Abstract
Abstract Background In 2010, the French National Health Insurance created a home return assistance programme (Programme d'accompagnement au retour à domicile après hospitalization, PRADO) to streamline the journey from the hospital to the home by anticipating patients’ needs. Methods A review of academic and grey literature, media reports and relevant organisational websites. Results The programme supports patients medically fit to be discharged and ensures they receive the required home follow-up care. During hospitalisation, a dedicated counsellor in the hospital organises the care pathway post-discharge. Hospitals can voluntarily participate in the programme. Community-based healthcare professionals (general practitioners, nurses, physical therapists and pharmacists) receive fees for the services provided as a part of the care pathway. Almost 100% of requests made to the local branches of the social health insurance are accepted. The programme started in 2010, initially focusing on maternity services. Since then, it has been extended to other care areas, including heart failure, orthopaedic surgery, stroke, and chronic obstructive pulmonary disease. Since 2019, the service can be offered to people aged 75 years and over, regardless of the reason for their hospitalisation. The governance of the programme is within the social health insurance system. The PRADO programme has been evaluated positively. A 2018 IRDES report identified that it reduced the time between discharge and first contact with a healthcare professional. In some cases, PRADO schemes saw increased cost per patient/comparator due to a more active post-hospital care approach involving more health professionals and better guideline adherence. Full adherence to the programme is identified as a key driver to ensure favourable outcomes such as reduced hospital readmissions. Conclusions The PRADO programme seems to have improved the transition of patients out of hospitals and reduced delayed discharges.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".