Exploring interhospital transfers and partnerships in the hospital sector in New South Wales, Australia
Notice bibliographique
Résumé
Objective The aim of the present study was to explore characteristics of interhospital transfers (IHT) and sharing of care among hospitals in New South Wales (NSW), Australia. Methods Data were extracted from patient-level linked hospital administrative datasets for separations from all NSW acute care hospitals from 1 July 2013 to 30 June 2015. Patient discharge and arrival information was used to identify IHTs. Characteristics of patients and related hospitals were then analysed. Results Transfer-in patients accounted for 3.9% of all NSW admitted patients and, overall, 7.3% of NSW admissions were associated with transfers (IHT rate). Patients with injuries and circulatory system diseases had the highest IHT rate, accounting for one-third of all IHTs. Patients were more often transferred to larger than smaller hospitals (61% vs 29%). Compared with private hospitals, public hospitals had a higher IHT rate (8.4% vs 5.1%) and a greater proportion of transfer-out IHTs (52% vs 28%). Larger public hospitals had lower IHT rates (3-8%) compared with smaller public hospitals (13-26%). Larger public hospitals received and retransferred higher proportions of IHT patients (52-58% and 11% respectively) than their smaller counterparts (26-30% and 2-3% respectively). Less than one-quarter of IHTs were between the public and private sectors or between government health regions. The number of interacting hospitals and their interactions varied across hospital peer groups. Conclusion NSW IHTs were often to hospitals with greater speciality services. The patterns of interhospital interactions could be affected by organisational and regional preferences. What is known about the topic? IHTs aim to provide efficient and effective care. Nonetheless, information on transfers and the sharing of care among hospitals in an Australian setting is lacking. Studies of transfers and hospital partnership patterns will inform efforts to improve patient-centred transfers and hospital accountability in terms of end outcomes for patients. What does this paper add? Transfer-in patients accounted for 3.9% of all NSW admissions; they were often (61%) transferred to hospitals with greater speciality services. The number of IHTs and sharing of care among hospitals varied across hospital peer groups, and could have been affected by organisational and regional preferences. What are the implications for practitioners? The findings of the present study suggest that different patterns of IHTs may not only have resulted from clinical priorities, but that organisational and regional preferences are also likely to be influential factors. Patient-centred IHTs and the development of guidelines need to be pursued to enhance the care and functionality of healthcare. Patient sharing should be acknowledged in hospital and regional performance profiling.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».